DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . The following FINAL office action is in response to Applicant communication filed on 07/10/2026 regarding application 18/776,120. Claims 1-3, 5, 9, 16 and 18-20 have been amended. Claims 1-20 are pending and have been rejected.
Response to Amendments
2. Applicant’s amendment filed on 07/10/2026 necessitated new grounds of rejection in this office action.
IDS Statements
3. The 1 Information Disclosure Statement (IDS) filed on 04/24/2026 complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 and is considered by the Examiner.
Priority
4. The Examiner has noted the Applicants claiming Priority from Provisional Application PRO 63/514,040 filed on 07/17/2023. Therefore, the earliest effective filing date examined for this case is reflective of 07/17/2023.
Status of Claims
5. Applicant’s arguments, see page 11 filed on 07/10/2026, with respect to the Claim Objections for Claims 1, 3, 5-6, 15-16 and 19-20 have been fully considered, and are found to be persuasive. Therefore, the Claim Objections for Claims 1, 3, 5-6, 15-16 and 19-20 is withdrawn.
6. Applicant’s arguments, see pages 11-14 filed on 07/10/2026, with respect to the 35 U.S.C. § 102 (a) (2) Claim Rejections for Claims 1-7, 10-13, 15-17 and 19-20 have been fully considered, and are found to be not persuasive. Applicant’s arguments with respect to Claims 1-20 have been considered, but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Response to 35 U.S.C. § 101 Arguments
7. Applicant’s 35 U.S.C. § 101 arguments, filed with respect to Claims 1-20 have been fully considered, but they are found not persuasive (see Applicant Remarks, Pages 8-11 dated 07/10/2026). Examiner respectfully disagrees.
Argument #1:
(A). Applicant argues that Claims 1-20 do not recite an abstract idea, law of nature of natural phenomenon under revised step 2a prong one of the 35 U.S.C § 101 analysis (see Applicant Remarks, Pages 8-10, dated 07/10/2026). Examiner respectfully disagrees.
Specifically, Applicant argues that for Independent Claims 1 and 19-20 the claimed process could not practically be performed mentally or in the human mind or via a generic business method (see Applicant’s Remarks, Page 10, dated 07/10/2026). Examiner respectfully disagrees.
Here, the claim limitations for Independent Claims 1 and 19-20 are directed to the abstract idea of risk evaluation, data analysis, and decision-making (specifically, calculating risk scores, comparing them to thresholds, and selecting/ranking replacement options to lower overall risk). Mental Processes: Concepts that can be performed in the human mind or by a human using a pen and paper, such as reviewing a list of tasks, evaluating risk, ranking choices, and substituting one task for another. The process of “determining… that an overall risk score…exceeds a … threshold” is a mental act. A human could look at data, calculate a score, and decide if it is too high. Even though this is done by a machine learning model, the Federal Circuit (e.g., Recentive Analytics, Inc. v. Fox Corp.) views the automation of human-like judgment as a mental process. Mathematical Concepts: Calculations of risk scores, replacement scores, predicted failure correlations, and comparing values against numerical thresholds. “Assigning… a replacement score” based on “failure correlation” and “ranking” candidates are mathematical relationships or mathematical calculations. Analyzing correlations and sorting results are mathematical operations that form the core “directed to” character of these claims. Methods of Organizing Human Activity: Managing business or operational tasks, rules for risk mitigation, and administrative tracking of project components. The steps of “generating a set of tasks” and “generating an updated set of tasks” are considered fundamental administrative or management activities. Courts have consistently held that organizing human activity, such as managing a work schedule or task list, is an abstract concept.
The claims are reasonably characterized as being directed to managing and mitigating risk in a set of tasks. In particular, the claims recite a process comprising: generating a set of tasks having offset potentials and failure mechanisms; determining an overall risk score for the set; determining that the overall risk exceeds a threshold; identifying a task associated with the excessive risk; receiving replacement candidates; evaluating each replacement candidate based upon predicted failure correlation with the remaining tasks; assigning replacement scores; ranking the candidates; selecting a replacement task; and replacing the identified task so that the updated set satisfies the applicable risk threshold. This is a process for evaluating risk and selecting an action to mitigate that risk.
MPEP § 2106 identifies certain methods of organizing human activity as an abstract-idea grouping, including fundamental economic principles or practices and managing personal behavior or interactions. Risk mitigation and allocation are paradigmatic examples of the types of activities that can be performed as part of organizing human activity. The use of a computer or ML model to perform such analysis does not, by itself, remove the underlying concept from the abstract-idea analysis.
The claims therefore do not merely recite “generic risk scoring” in isolation, as Applicant suggests. Rather, the claims recite a particular sequence for applying risk-management criteria to select a substitute task. Specificity in the implementation of an abstract process does not necessarily make the process non-abstract. To the extent the claim limitations concerning scores, thresholds, and correlations require mathematical calculations or relationships, those limitations may additionally implicate the mathematical-concepts grouping. However, the principal characterization is the risk-management process itself. The mere use of terms such as “score,” “threshold,” or “correlation” should not independently be treated as mathematical concepts unless the claim actually recites a mathematical relationship or calculation.
Applicant also suggests that the large data set, multiple sources, complex correlation structure, and billion model evaluations demonstrate that the invention cannot be characterized as a mental process. The argument does not overcome the § 101 rejection. The principal abstract-idea characterization is the management and mitigation of risk, not that every claimed computer operation can literally be performed mentally in the precise claimed form. To the extent individual operations involve evaluation, judgment, comparison, ranking, and selection, those underlying concepts may also implicate the mental-process grouping. The claim's use of a computer and ML model does not eliminate the independent basis for characterizing the overall process as risk management. Moreover, the fact that a computer can process a larger quantity of information or perform the analysis faster than a human does not, standing alone, establish a technological improvement. Thus, Applicant's computational-complexity argument does not negate the abstract nature of the underlying activity.
Examiner refers Applicant to MPEP § 2106.04 (a) (2) II which states that: “the sub-groupings encompass both activity of a single person and activity that involves multiple people, and thus, certain activity between a person and a computer may fall within the "Certain Methods of Organizing Human Activities" groupings. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings.” With respect to “Mathematical Concepts” category, Examiner refers Applicant to MPEP § 2106.04 (a) (2) (I) (C): “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping.” “It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea).” Furthermore, see MPEP § 2106.05 (c): “For data, mere "manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’" has not been deemed a transformation. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360, 31 USPQ2d 1754, 1755, 1759 (Fed. Cir. 1994)).”
Also, with respect to “Mental Processes” category, Examiner refers Applicant to MPEP § 2106.04 (a) (2) (III) (B): “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, 839 F.3d at 1139, 120 USPQ2d at 1474 (holding that claims to the mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper"). The use of a physical aid (e.g., pencil and paper or a slide rule) to help perform a mental step (e.g., a mathematical calculation) does not negate the mental nature of the limitation, but simply accounts for variations in memory capacity from one person to another.”
Moreover, with respect to “Mental Processes” category, Examiner refers Applicant to MPEP § 2106.04 (a) (2) (III) (C): “Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claims are an abstract idea because it could be "performed by humans without a computer").” “For instance, the Examiner has reviewed Applicant’s Specification and determined that the claimed invention is described as concepts that are performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer (see Applicant’s Specification ¶ [0173]: “Computers suitable for the execution of a computer program can be based on general or special-purpose microprocessors or both, or any other kind of central processing unit.”), or 2) in a computer environment (see Applicant’s Specification ¶ [0169-0170]: “The apparatus can also include, in addition to hardware, code that creates an execution environment for computer programs, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them. A computer program can be written in any form of programming language, including compiled or interpreted languages, or declarative or procedural languages; and it can be deployed in any form, including as a stand-alone program, e.g., as an app, or as a module, component, engine, subroutine, or other unit suitable for executing in a computing environment, which environment may include one or more computers interconnected by a data communication network in one or more locations.”), or 3) is merely using a computer as a tool to perform these concepts.” Thus, based on these 3 factors, Examiner maintains that the claims still recite a mental process.
Therefore, in conclusion, Examiner maintains that Claims 1-20 are directed to abstract ideas under “Mental Processes” or “Mathematical Concepts” or “Certain Methods of Organizing Human Activities” Groupings under 35 U.S.C. § 101 Step 2A Prong 1.
Argument #2:
(B). Applicant argues that Claims 1-20 recite additional elements that integrate the judicial exception into a practical application under revised step 2a prong two of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Pages 9-11, dated 07/10/2026). Examiner respectfully disagrees.
Specifically, Applicant argues that Independent Claims 1 and 19-20 satisfy these principles of “improving the functioning of a computer or improve another technology or technical field” due to the claims reciting a specific ML-based technique for solving the technical problem of dynamically repairing a task portfolio based on predicted correlated failures across the portfolio” (see Applicant’s Remarks, 3rd ¶ of Page 9, dated 07/10/2026). Examiner respectfully disagrees.
Examiner points out under 35 U.S.C. § 101 step 2a prong 2, considering these claims as a whole, the additional elements do not integrate the recited risk-management concept into a practical application. The “one or more computers,” “storage devices,” and “instructions” merely provide a generic computer environment for performing the recited operations. MPEP § 2106.05(f) explains that merely implementing an abstract idea on a computer or using a computer as a tool to perform an abstract idea does not establish eligibility. Likewise, the ML model does not perform a technological function independent of the abstract risk-management process. It is used to: evaluate portfolio risk; identify an excessive-risk task; evaluate replacement candidates; predict failure correlations; assign scores; rank candidates; and select a replacement. Every one of these operations serves the same underlying objective—managing the risk of the task portfolio. The ordered combination therefore does not transform the claims into a technological process. It merely establishes a particular computerized sequence for carrying out the risk-management concept.
Moreover, with respect to Independent Claims 1 and 19-20, certain/particular limitations shown recite (1) mere data gathering (e.g., “receiving, a plurality of replacement candidates, each replacement candidate comprising a candidate offset potential and one or more candidate failure mechanisms”) and (2) selecting a particular data source or type of data to be manipulated (e.g., “selecting, based on the ranking, the replacement task” & “selecting a replacement task for the task, the selecting comprising”) in which each of these claim limitations reflects mere insignificant extra-solution activities (see MPEP § 2106.05 (g)).
Applicant emphasizes that the selected task is substituted into the set and that the updated overall risk score satisfies the first failure threshold. This limitation does not provide the required practical application. The claimed generation of an updated task set is the desired result of the abstract process, not a technological transformation. The generation of an updated task set is simply the implementation of the selected risk-management decision. The claims (e.g., in particular Independent Claims 1 and 19-20) do not require the updated set to control a physical machine, modify a computer's architecture, alter operation of a technical system, or otherwise transform an article or technology. The condition that the updated risk score satisfies the threshold merely defines the desired result of the risk-mitigation process. Thus, the limitation establishes that the risk-management procedure successfully achieved its intended objective; it does not establish a technological improvement.
The Federal Circuit's Recentive decision is particularly instructive because the claimed technology there involved machine learning and optimization, yet the claims remained abstract because the ML technology was used as a tool to perform the desired scheduling functions rather than to improve ML technology itself. Accordingly, the presence of ML does not change the character of the claimed process. The claims recites generic computer components and machine learning models ("a machine learning model", "receiving", "generating", "assigning") used in a way to process data, calculate scores, and output a new list. It does not improve how a computer or machine learning system functions technically. Instead, it uses the machine learning model as a generic tool to perform the abstract mental and mathematical process of risk scoring and task swapping. Because the operation is generic data manipulation, the abstract idea is not integrated into a practical technological application. The claim does not meaningfully limit the judicial exception through an improvement to computer technology, a particular machine integral to the claimed process, a transformation, or another meaningful technological application.
Applicant characterizes the problem as technically complex and computationally difficult because the risk of a portfolio depends upon individual task risks as well as correlated failure mechanisms among tasks.
The characterization of a problem as computationally complex, however, does not by itself establish that the claimed invention improves computer functionality or another technology or technical field. MPEP § 2106.05(a) directs the improvement inquiry to whether the claim improves “the functioning of the computer itself” or another technology or technical field. The MPEP further explains that an improvement in the abstract idea itself—for example, an improvement to a fundamental economic concept—is not an improvement in technology.
Here, the claimed problem is fundamentally one of portfolio risk management. The claims seek to determine whether a set of tasks presents excessive risk, identify a task contributing to that risk, evaluate possible replacement tasks based upon their respective failure characteristics and correlations, rank the candidates, select a replacement, and generate an updated set satisfying a risk threshold.
Nothing in these operations changes the operation of a computer, processor, memory, database, machine-learning architecture, or other computer technology. Rather, the claimed computer performs an analysis of information concerning mitigation tasks and produces a revised task portfolio.
Thus, even assuming that the claimed risk-management problem is computationally difficult, the asserted difficulty is a difficulty in performing the desired risk analysis, not a technological deficiency in the underlying computer or ML technology. The distinction is significant. A claim does not become directed to a technological improvement merely because a computer performs a difficult calculation or because the claimed result could not practically be obtained as efficiently without computer assistance.
Applicant repeatedly characterizes the claimed process as a “specific machine-learning-based portfolio repair process.” That characterization does not establish eligibility. The claims do not recite an improvement to the operation of the machine-learning model itself. For example, the claims do not recite a particular ML architecture, a new training technique, a new model-update mechanism, a new loss function, a new feature representation, a new model-compression technique, a new inference architecture, a reduction in model memory requirements, an improvement in model convergence, an improvement in continual-learning performance, or another modification to the operation of machine learning technology. Instead, the claims use a machine-learning model as an analytical tool to: determine an overall risk score; determine that the overall risk exceeds a threshold; assign replacement scores to candidates; predict failure correlations; rank candidates; and select a replacement task. Those are the substantive risk-management operations performed using the ML model. This distinction is particularly relevant in view of Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025). There, the Federal Circuit considered claims using machine learning to optimize television-event schedules and network maps. The court explained that, in software and ML cases, the relevant inquiry is whether the claims focus on a specific asserted improvement in computer capabilities or instead on an abstract process for which computers are merely invoked as a tool. The court held the claims ineligible where the claimed ML technology was used to perform the desired scheduling/network-map functions without an improvement to the underlying ML technology. The present claims similarly use machine learning to achieve an improved risk-management result. They do not claim an improvement to machine learning itself. Accordingly, Applicant's repeated reference to the claimed process as “machine-learning-based” does not convert the underlying risk-management process into a technological improvement.
Applicant asserts that Independent Claims 1 and 19-20 are not merely a high-level risk-selection process because it specifies particular data types, failure mechanisms, replacement candidates, predicted correlations, ranking, and threshold satisfaction. The argument conflates specificity of an abstract process with improvement to technology. A claim can contain numerous detailed steps and nevertheless remain directed to an abstract idea. The relevant question is not simply whether the claim is detailed, but whether the claimed details demonstrate that the claim is directed to a technological improvement rather than to the abstract result implemented using technology. Here, each purportedly “specific” limitation further defines how the risk-management decision is made: “multiple data types from a plurality of sources” defines the information used for the risk determination; “offset potential” and “failure mechanisms” define characteristics of the tasks; “overall risk score” and “failure threshold” define criteria for determining excessive risk; “candidate failure mechanisms” define characteristics of replacement candidates; “predicted failure correlation” defines a relationship used to evaluate the candidates; “replacement score” defines a value used to compare candidates; “ranking” orders the candidates; and “selecting” chooses the candidate that is to replace the identified task. These limitations make the risk-management process more particular, but they do not describe a change to how the computer or ML model operates. Thus, the additional specificity does not establish that the claims improve computer functionality or another technology.
Applicant emphasizes that the ML model receives multiple data types from multiple sources. This limitation does not, by itself, integrate the abstract idea into a practical application. The claims do not recite a technological mechanism for solving a problem associated with heterogeneous data. For example, the claims do not require a particular data-format conversion technique, database architecture, synchronization mechanism, distributed-storage arrangement, communication protocol, data-compression technique, feature-extraction technique, or other technological improvement for processing the multiple sources. Instead, the sources and data types merely supply information that is used by the ML model in making the claimed risk determinations. Consequently, the limitation amounts to specifying the inputs to the risk-analysis process, rather than an improvement in computer data processing.
Applicant places particular emphasis on the limitation requiring a predicted failure correlation indicating whether a replacement candidate and the remaining tasks are likely to fail together, independently, or both. This limitation further defines the criterion by which replacement candidates are evaluated. It does not, however, recite a technological improvement in generating correlations. The claimed correlation is used to answer a portfolio-management question: which replacement task should be selected so that the resulting collection of tasks has an acceptable risk profile? The correlation therefore functions as a decision criterion within the claimed risk-management process. Nothing in the claim requires a new computational technique for calculating correlation, a new ML architecture for predicting correlation, or an improvement in the operation of the ML model. The claim merely requires that the model assign scores based on the predicted relationship. Accordingly, the predicted failure correlation does not transform the underlying risk-management concept into a technological process.
Applicant relies on the specification's statement that the ML model can generate non-intuitive predictions concerning which tasks are likely to fail together or independently. That disclosure demonstrates that the claimed system can assist a user in making a more informed portfolio-management decision. It does not demonstrate that the computer or ML technology itself has been improved. The fact that a computer-generated prediction may be more accurate, more comprehensive, or less intuitive than a human prediction does not establish an improvement in computer functionality. The relevant inquiry is whether the claimed invention improves the operation of the technology, rather than merely improving the outcome obtained when the technology is applied to a particular field. This distinction is consistent with Recentive, in which the Federal Circuit rejected the proposition that improved speed and efficiency in performing the desired activity, without an improvement to the underlying ML technology, was sufficient to establish eligibility. Here, the purported improvement is likewise in the quality of the portfolio-risk decision, not in the operation of the ML system.
Applicant relies on the specification's statement that the complex correlation structure across many projects means that there is “no closed form solution or simple computation.” This statement supports the proposition that the underlying risk-management problem may be mathematically or computationally complex. It does not establish that the claimed solution improves computer technology. Indeed, the claim does not recite a particular solution to the computational complexity itself. It does not require a new algorithm for reducing computational complexity, a new optimization technique, a reduced-complexity ML architecture, a particular parallel-processing architecture, reduced memory usage, reduced processing time, or another change in the manner in which the computer performs computation. Instead, the claims use a machine-learning model to evaluate the complex portfolio relationships and select a replacement task. Thus, even assuming the underlying risk problem has no simple closed-form solution, the claim remains directed to using computational technology to perform the risk-management analysis.
Applicant also relies on the specification's disclosure that an example may involve “1 billion individual model evaluations.” The number of computations does not establish eligibility. If anything, this disclosure demonstrates the distinction between the claimed result and a technological improvement. The specification describes the large number of model evaluations to demonstrate the complexity of analyzing the portfolio. But the claims do not require a particular technique for making those evaluations computationally more efficient. There is no claim limitation requiring: parallel processing of the billion evaluations; a particular processor architecture; distributed processing; reduced memory consumption; reduced computation time; model compression; improved convergence; reduced communication overhead; or another technical solution to the computational burden. Accordingly, the claimed invention is not directed to improving the computer's ability to perform the one billion evaluations. It is directed to using the evaluations to make a portfolio-management decision.
Applicant correctly notes that MPEP § 2106.05(a) recognizes that a claim may integrate an abstract idea into a practical application when the claim improves computer functionality or another technology or technical field. However, the MPEP expressly distinguishes an improvement in technology from an improvement in the abstract idea itself. The improvement consideration is directed to technological improvements, not simply to achieving a better result in carrying out an economic, organizational, or other abstract activity. The present claims do not improve processor functionality; memory operation; database operation; data transmission; ML model architecture; ML training; ML inference; model storage; model convergence; computer-resource utilization; or another identified computer technology. Instead, the claims improve the selection of tasks within a risk-managed portfolio. The distinction can also be illustrated by the Federal Circuit's treatment of ML claims in Recentive. There, the court recognized that machine learning is a technology capable of patent-eligible improvement, but held that merely applying generic ML techniques to a new environment, without improving the ML models themselves, was insufficient. Thus, Applicant's reliance on MPEP § 2106.05(a) does not change the eligibility analysis.
To the extent Applicant relies upon the USPTO's current guidance concerning AI improvements, including Ex parte Desjardins, that decision does not compel a different result. Ex parte Desjardins, Appeal No. 2024-000567, was designated precedential and involved claims that the Appeals Review Panel determined reflected an improvement to AI technology itself. The USPTO identifies the decision as concerning a “technological improvement to a machine learning model” under Step 2A, Prong Two. The distinction is material. In Desjardins, the asserted improvement concerned the operation of the ML system itself, including improvements relating to continual learning, storage requirements, and preservation of prior learning. The ARP expressly agreed that the claimed subject matter reflected an improvement in the AI technology. By contrast, the present claims do not claim a new way for an ML model to learn, store information, converge, infer, update, or otherwise operate. The ML model is used to perform risk analysis and candidate selection. Accordingly, Desjardins supports the proposition that actual improvements to ML technology can constitute a practical application; it does not establish that every use of ML to improve a nontechnical result constitutes such an improvement. The present claims fall on the latter side of that distinction. In summary, the judicial exception is not integrated into a practical application under Step 2A, Prong Two. Claims 1-20 are maintained as being patent ineligible under 35 U.S.C. § 101 step 2a prong 2.
Argument #3:
(C). Applicant argues that Claims 1-20 recite additional elements that amount to significantly more than the recited judicial exceptions under revised step 2B of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Pages 9-11, dated 07/10/2026). Examiner respectfully disagrees.
In response, Examiner refers Applicant to Examiner’s 35 U.S.C. 101 analysis section (e.g., Claim Rejections - 35 U.S.C. § 101 section shown below) shown for step 2B particularly for Independent Claims 1 and 19-20. The claims do not recite additional elements that amount to significantly more than the recited judicial exceptions, because they are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exceptions. The limitations are directed to limitations referenced in MPEP § 2106.05I.A. that are not enough to qualify as significantly more when recited in these claims with the abstract idea which include: (1) adding the words “apply it” (or an equivalent) with the judicial exception, (2) or mere instructions to implement an abstract idea on a computer and providing the results to the user on a computer, and (3) generally linking the use of the judicial exception to a particular technological environment or field of use.
Even assuming, arguendo, that the claims integrate the abstract idea into a practical application, the claims nevertheless fail Step 2B. The additional elements consist principally of a generic computer implementation, data gathering, ML-based scoring/prediction, ranking, and selection. The computer and storage elements are generic implementation components. The claims do not recite an unconventional computer architecture or specialized hardware. The ML limitation likewise does not specify a technological improvement to the ML model. Instead, the model performs the ordinary analytical functions of evaluating inputs, generating predictions or scores, and ranking alternatives. The use of multiple data sources and data types provides additional information for the analysis but does not itself supply an inventive concept. Similarly, the use of failure correlation, replacement scores, rankings, and thresholds merely specifies the criteria and sequence by which the risk-management decision is made. Thus, even when considered together, the additional elements amount to a computerized implementation of the claimed risk-management process rather than an inventive concept that transforms the abstract idea into patent-eligible subject matter.
Where a factual finding of well-understood, routine, conventional activity is necessary, that finding should be supported in accordance with MPEP § 2106.07 and the applicable evidentiary requirements. The Step 2B conclusion, however, does not depend solely upon treating every individual ML operation as conventional. The claims can also be considered as a whole to determine whether the ordered combination provides significantly more than the abstract idea. MPEP § 2106 describes Step 2B as requiring consideration of whether the additional elements, individually and in combination, amount to significantly more.
Here, the ordered combination remains identify excessive portfolio risk → identify contributing task → obtain replacement candidates → evaluate failure relationships → score candidates → rank candidates → select replacement → substitute replacement → verify acceptable risk. That sequence is the risk-management process itself. The addition of computer and ML terminology does not transform that process into a technological improvement.
Moreover, with respect to Independent Claims 1 and 19-20, certain/particular limitations shown recite (1) mere data gathering (e.g., “receiving, a plurality of replacement candidates, each replacement candidate comprising a candidate offset potential and one or more candidate failure mechanisms”) and (2) selecting a particular data source or type of data to be manipulated (e.g., “selecting, based on the ranking, the replacement task” & “selecting a replacement task for the task, the selecting comprising”) in which each of these claim limitations reflects mere insignificant extra-solution activities (see MPEP § 2106.05 (g)). Furthermore, these certain/particular claim limitations as demonstrated above for Independent Claims 1 and 19-20 reflects Well-Understood, Routine and Conventional Activities (WURC) under MPEP § 2106.05 (d) ii: See Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec,838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359,1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
The additional elements of “machine learning” or “machine learning model” in Claims 1 and 19-20 do not amount to significantly more than the judicial exceptions under step 2B due to being expressly recognized as Well-Understood, Routine and Conventional (WURC) in the art. (See for example; US PG Pub (US 2023/0135611 A1) hereinafter Kojo, et. al. Kojo at ¶ [0150-0153]: “The selection process may be performed by an algorithm such as Hill Climbing, Gradient Descent and/or some other suitable optimization algorithm. There are two ways to train the model used for the selection process: with synthetic data, and/or with enriched data.” See also Kojo at ¶ [0165-0167]: “Use a machine-learning algorithm to alter the parameters of the optimization logic.” See for example; US PG Pub (US 2023/0290247 A1) hereinafter McBride, et. al. McBride at ¶ [0051]: “The GHG monitoring system 16 can also include a machine learning (ML)/artificial intelligence (AI) module 58 that can be utilized with the image processing module 56 or with other functionality, e.g., to generate, improve, or utilize EMs 28, traffic models 27, or related models (e.g., GHG offset calculation models 29 discussed below) used to generate the carbon offset credits 30 or to determine traffic signaling and timing that can optimize GHG traffic emissions associated with an intersection 12 or network of intersections 12.” See for example; US PG Pub (US 2024/0403776 A1) hereinafter Krishna, et. al. Krishna at ¶ [0080] & ¶ [0097] noting transfer learning ML models regarding “to provide specific, actionable steps to efficiently reduce resource inputs (e.g., energy consumption) and/or resource outputs (e.g., emissions).”)
Applicant has identified a potentially useful application of machine learning to portfolio-risk management. However, usefulness and computational sophistication do not, by themselves, establish patent eligibility. The claims are directed to managing and mitigating risk by evaluating a task portfolio, identifying excessive risk, evaluating replacement candidates according to predicted failure relationships, ranking the candidates, selecting a replacement, and updating the portfolio to satisfy a risk threshold. The claimed machine-learning model is used as a tool to perform those risk-management functions. The claims do not recite an improvement to the functioning of a computer, an improvement to an ML model or its training/inference technology, a specialized computer architecture, a particular data-processing architecture, or another technological improvement. The specification's disclosures concerning large data sets, multiple sources, complex correlations, non-intuitive predictions, and large numbers of model evaluations demonstrate the complexity of the risk-management problem but do not, without corresponding claimed technological improvements, establish that the claims improve computer technology.
In summary accordingly, under Step 2A, Prong One, the claims recite the abstract idea of managing and mitigating risk in a set of tasks, with additional aspects potentially involving evaluation, judgment, scoring, and mathematical relationships. Under Step 2A, Prong Two, the additional elements do not integrate that abstract idea into a practical application because the computer and ML model are used as tools to perform the risk-management process and the claims do not recite an improvement to computer functionality, ML technology, or another technology or technical field. Finally, under Step 2B, the additional elements, considered individually and as an ordered combination, do not provide significantly more than the abstract idea. The ordered combination of elements in the Dependent Claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself. Therefore, under Step 2B, Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. Thus, Claims 1-20 are patent ineligible with respect to the 35 U.S.C. § 101 analysis.
Claim Rejections - 35 USC § 101
8. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
9. Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-20 are focused to a statutory category namely, a “process” or a “method” (Claims 1-18), a “system” or an “apparatus” (Claim 19) and a “non-transitory computer storage medium” or an “article of manufacture” (Claim 20).
Step 2A Prong One: Independent Claims 1 and 19-20 recites limitations that set forth the abstract idea(s), namely (see in bold except where strikethrough):
“” (see Independent Claim 19);
“” (see Independent Claim 20);
“generating a set of tasks, wherein each task comprises an offset potential and one or more failure mechanisms, and wherein the set comprises a metric” (see Independent Claims 1 and 19-20);
“determining, by a model and based on multiple data types from a plurality of sources, that an overall risk score of the set exceeds a first failure threshold due to a risk score of a task of the set of tasks exceeding a second threshold” (see Independent Claims 1 and 19-20);
“selecting a replacement task for the task, the selecting comprising” (see Independent Claims 1 and 19-20);
“receiving, a plurality of replacement candidates, each replacement candidate comprising a candidate offset potential and one or more candidate failure mechanisms” (see Independent Claims 1 and 19-20);
“assigning, by the model and to each of the plurality of replacement candidates, a replacement score for the replacement candidate based on a predicted failure correlation of the replacement candidate with respect to each other sets of the set of tasks, wherein the predicted failure correlation indicates whether the replacement candidate and the remaining tasks are likely to fail together, independently, or both” (see Independent Claims 1 and 19-20);
“ranking the plurality of replacement candidates based on the replacement scores” (see Independent Claims 1 and 19-20);
“selecting, based on the ranking, the replacement task” (see Independent Claims 1 and 19-20);
“generating, an updated set of tasks including the replacement task in place of the task that caused the overall risk score of the set of tasks to exceed the first failure threshold, wherein an updated overall risk score of the updated set of tasks satisfies the first failure threshold” (see Independent Claims 1 and 19-20).
Here, the claim limitations for Independent Claims 1 and 19-20 are directed to the abstract idea of risk evaluation, data analysis, and decision-making (specifically, calculating risk scores, comparing them to thresholds, and selecting/ranking replacement options to lower overall risk). Mental Processes: Concepts that can be performed in the human mind or by a human using a pen and paper, such as reviewing a list of tasks, evaluating risk, ranking choices, and substituting one task for another. The process of “determining… that an overall risk score…exceeds a … threshold” is a mental act. A human could look at data, calculate a score, and decide if it is too high. Even though this is done by a machine learning model, the Federal Circuit (e.g., Recentive Analytics, Inc. v. Fox Corp.) views the automation of human-like judgment as a mental process. Mathematical Concepts: Calculations of risk scores, replacement scores, predicted failure correlations, and comparing values against numerical thresholds. “Assigning… a replacement score” based on “failure correlation” and “ranking” candidates are mathematical relationships or mathematical calculations. Analyzing correlations and sorting results are mathematical operations that form the core “directed to” character of these claims. Methods of Organizing Human Activity: Managing business or operational tasks, rules for risk mitigation, and administrative tracking of project components. The steps of “generating a set of tasks” and “generating an updated set of tasks” are considered fundamental administrative or management activities. Courts have consistently held that organizing human activity, such as managing a work schedule or task list, is an abstract concept.
See for example the full 35 U.S.C. § 101 analysis breakdown shown here: “Generating a set of tasks (offset potential, failure mechanisms, metric)”: Recites concepts of organizing information (generating task sets with parameters). “Determining via a machine learning model that an overall risk score exceeds a threshold”: Recites a mental process / mathematical concept (calculating a risk score and comparing it against a threshold). “Selecting a replacement task (receiving candidates, assigning replacement scores via ML based on failure correlation, ranking, and selecting)”: Recites mental processes (evaluating, scoring, correlating, and ranking candidates) and methods of organizing human activity (risk mitigation). “Generating an updated set of tasks where the overall risk score satisfies the threshold”: Recites the conclusion of an abstract optimization routine (updating a data record to satisfy a numerical constraint).
Therefore, other than reciting (e.g., “one or more computers” & “one or more storage devices”, etc…), nothing in the claim elements precludes the steps from being performed as “Mental Processes” which pertains to (1) concepts performed in the human mind (including observations or evaluations or judgments) or (2) using pen and paper as a physical aid and additionally or alternatively as “Certain Methods of Organizing Human Activities” which pertains to (3) fundamental economic principles or practices (including mitigating risk) or (4) managing personal behavior (including teachings or following rules or instructions) and additionally or alternatively as “Mathematical Concepts” which pertains to (5) mathematical calculations or (6) mathematical relationships.
Therefore, at step 2a prong 1, Yes, Claims 1-20 recites an abstract idea. We proceed onto analyzing the claims at step 2a prong 2.
Step 2A Prong Two: With respect to Step 2A Prong Two of the eligibility inquiry (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Independent Claim 19 recites additional elements directed to: (e.g., “one or more computers” & “one or more storage devices”). Independent Claim 20 recites additional elements directed to: (e.g., “one or more computers”). These additional elements have been considered individually and in combination, but fail to integrate the abstract idea into a practical application because they amount to using computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment. See MPEP § 2106.05(f) and MPEP § 2106.05(h).
Independent Claims 1 and 19-20: With respect to reliance on (e.g., “machine learning model”) as an additional element shown in Independent Claims 1 and 19-20 when considered both individually and as an ordered combination (as a whole) with these recited claim limitations, this additional element does not provide limitations that are indicative of integration into a practical application under step 2a prong 2 due to the following: (1) recites mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions by providing the results to the user on a computer (see MPEP § 2106.05 (f)) or (2) limiting a particular field of use or technological environment due to restricting the use of a mental process to a specific field such as broadcasting schedules or task risk assessment is considered a “token addition” using a computer in a greenhouse gas environmental field (see MPEP § 2106.05 (h)).
Examiner notes that the additional elements such as (e.g., using a machine learning model to rank tasks) are viewed as generally linking an abstract idea to a particular technological environment. Field of use is not an improvement. Absence of technical integration: According to MPEP § 2106.05 (h), the steps describe what is being done (generating tasks, ranking candidates) rather than how the technology is being technically improved. “Apply it” logic: Limiting the abstract idea to being performed “by a machine learning model” is treated as a mere instruction to “apply it" in a technology environment. Moreover, the data gathering and output steps are seen as mere insignificant extra-solution activities under MPEP §2106.05 (g).
These steps are ineligible because they merely link the abstract process of risk assessment and decision-making to a generic computer without providing a specific technical improvement to that environment. Moreover, these steps, when viewed individually or as an ordered combination, do not integrate the abstract idea into a practical application because they: (1) do not improvement the computer itself (no mention of memory management, speed or efficiency), (2) do not provide a technical solution to a technical problem and (3) simply use a generic computer as a tool to automate a process that could be performed mentally or with pen and paper (given enough time).
In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception. Therefore, at step 2a prong 2, Claims 1-20 are directed to the abstract idea and do not recite additional elements that integrate into a practical application.
Step 2B: (As explained in MPEP § 2106.05), it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Independent Claim 19 recites additional elements directed to: (e.g., “one or more computers” & “one or more storage devices”). Independent Claim 20 recites additional elements directed to: (e.g., “one or more computers”). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (computing environment) and does not amount to significantly more than the abstract idea itself. See MPEP § 2106.05 (f) and MPEP § 2106.05 (h). Notably, Applicant’s Specification suggests that the claimed invention relies on nothing more than a general-purpose computer executing the instructions to implement the invention (e.g., see at Applicant’s Specification ¶ [0165]: “Computers suitable for the execution of a computer program can be based on general or special-purpose microprocessors or both, or any other kind of central processing unit. Generally, a central processing unit will receive instructions and data from a read-only memory or a random-access memory or both.” See also Applicant’s Specification ¶ [0043] noting: “FIG. 7 is a block diagram of an example generic computing system.”).
Independent Claims 1 and 19-20: With respect to reliance on (e.g., “machine learning model”) as an additional element shown in Independent Claims 1 and 19-20 when considered both individually and as an ordered combination (as a whole) with these recited claim limitations, this additional element does not amount to significantly more than the judicial exceptions under step 2B due to the following: (1) recites mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions by providing the results to the user on a computer (see MPEP § 2106.05 (f)) or (2) limiting a particular field of use or technological environment due to restricting the use of a mental process to a specific field such as broadcasting schedules or task risk assessment is considered a “token addition” using a computer in a greenhouse gas environmental field (see MPEP § 2106.05 (h)). Examiner notes that the additional elements such as (e.g., using a machine learning model to rank tasks) are viewed as generally linking an abstract idea to a particular technological environment. Field of use is not an improvement. Absence of technical integration: According to MPEP § 2106.05 (h), the steps describe what is being done (generating tasks, ranking candidates) rather than how the technology is being technically improved. “Apply it” logic: Limiting the abstract idea to being performed “by a machine learning model” is treated as a mere instruction to “apply it" in a technology environment.
Moreover, with respect to Independent Claims 1 and 19-20, certain/particular limitations shown recite (1) mere data gathering (e.g., “receiving, a plurality of replacement candidates, each replacement candidate comprising a candidate offset potential and one or more candidate failure mechanisms”) and (2) selecting a particular data source or type of data to be manipulated (e.g., “selecting, based on the ranking, the replacement task” & “selecting a replacement task for the task, the selecting comprising”) in which each of these claim limitations reflects mere insignificant extra-solution activities (see MPEP § 2106.05 (g)). Furthermore, these certain/particular claim limitations as demonstrated above for Independent Claims 1 and 19-20 reflects Well-Understood, Routine and Conventional Activities (WURC) under MPEP § 2106.05 (d) ii: See Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec,838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359,1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
The additional elements of “machine learning” or “machine learning model” in Claims 1 and 19-20 do not amount to significantly more than the judicial exceptions under step 2B due to being expressly recognized as Well-Understood, Routine and Conventional (WURC) in the art. (See for example; US PG Pub (US 2023/0135611 A1) hereinafter Kojo, et. al. Kojo at ¶ [0150-0153]: “The selection process may be performed by an algorithm such as Hill Climbing, Gradient Descent and/or some other suitable optimization algorithm. There are two ways to train the model used for the selection process: with synthetic data, and/or with enriched data.” See also Kojo at ¶ [0165-0167]: “Use a machine-learning algorithm to alter the parameters of the optimization logic.” See for example; US PG Pub (US 2023/0290247 A1) hereinafter McBride, et. al. McBride at ¶ [0051]: “The GHG monitoring system 16 can also include a machine learning (ML)/artificial intelligence (AI) module 58 that can be utilized with the image processing module 56 or with other functionality, e.g., to generate, improve, or utilize EMs 28, traffic models 27, or related models (e.g., GHG offset calculation models 29 discussed below) used to generate the carbon offset credits 30 or to determine traffic signaling and timing that can optimize GHG traffic emissions associated with an intersection 12 or network of intersections 12.” See for example; US PG Pub (US 2024/0403776 A1) hereinafter Krishna, et. al. Krishna at ¶ [0080] & ¶ [0097] noting transfer learning ML models regarding “to provide specific, actionable steps to efficiently reduce resource inputs (e.g., energy consumption) and/or resource outputs (e.g., emissions).”)
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent Claims 2-18 recite substantially the same or similar additional elements as addressed above and when considered individually and as an ordered combination (as a whole) with these limitations recite the same abstract idea(s) as shown in Independent Claims 1 and 19-20 along with further steps/details pertaining to “Mental Processes” which pertains to (1) concepts performed in the human mind (including observations or evaluations or judgments) or (2) using pen and paper as a physical aid and additionally or alternatively as “Certain Methods of Organizing Human Activities” which pertains to (3) fundamental economic principles or practices (including mitigating risk) or (4) managing personal behavior (including teachings or following rules or instructions) and additionally or alternatively as “Mathematical Concepts” which pertains to (5) mathematical calculations or (6) mathematical relationships.
Dependent Claims 2-4, 10-11 and 13-17 further narrow the abstract ideas, and are therefore still ineligible for the reasons previously provided in Steps 2A Prong 2 and Step 2B for Independent Claims 1 and 19-20. Dependent Claims 5-9: With respect to reliance on (e.g., “machine learning model” (see Dependent Claims 5-7 and 9) & “transfer learning machine learning model” (see Dependent Claims 8-9)) as additional elements shown in Dependent Claims 5-9 when considered both individually and as an ordered combination (as a whole) with these recited claim limitations, these additional elements both do not provide limitations that are indicative of integration into a practical application under step 2a prong 2 and also do not amount to significantly more than the judicial exceptions under step 2B due to the following: (1) recites mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions by providing the results to the user on a computer (see MPEP § 2106.05 (f)) or (2) limiting a particular field of use or technological environment due to restricting the use of a mental process to a specific field such as broadcasting schedules or task risk assessment is considered a “token addition” using a computer in a greenhouse gas environmental field (see MPEP § 2106.05 (h)). Dependent Claims 12 and 18: With respect to reliance on (e.g., “a market ecosystem” (see Dependent Claim 12) & “a sensor” (see Dependent Claim 18)) as additional elements shown in Dependent Claims 12 and 18 when considered both individually and as an ordered combination (as a whole) with these recited claim limitations, these additional elements both do not provide limitations that are indicative of integration into a practical application under step 2a prong 2 and also do not amount to significantly more than the judicial exceptions under step 2B due to the following: (1) recites mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions by providing the results to the user on a computer (see MPEP § 2106.05 (f)) or (2) limiting a field of use or particular technological environment pertaining to a permanence action generating an incentive supportive of one or more of the tasks, wherein the incentive reduces a probability of the at least one of the one or more failure mechanisms using a computer in a market ecosystem field of use (see MPEP § 2106.05 (h)).
The additional elements of “transfer machine learning model” in Claims 8-9 and a “a sensor” in Dependent Claim 18 do not amount to significantly more than the judicial exceptions under step 2B due to being expressly recognized as Well-Understood, Routine and Conventional (WURC) in the art. See for example; US PG Pub (US 2024/0403776 A1) hereinafter Krishna, et. al. Krishna at ¶ [0080] & ¶ [0097] noting transfer learning ML models regarding “to provide specific, actionable steps to efficiently reduce resource inputs (e.g., energy consumption) and/or resource outputs (e.g., emissions).” US PG Pub (US 2024/0403776 A1) hereinafter Krishna, et. al. Krishna at ¶ [0035] noting “satellite imagery, and/or other sensor data (e.g., from third-party sensors)”. See for example; US PG Pub (US 2023/0290247 A1) hereinafter McBride, et. al. McBride at ¶ [0074]: “Each of the SDs 14 can include at least one video capture device 24, which may include at least two cameras to capture depth of field information to capture images or video of passing traffic at a known location based on the installation location, MAC address, or a location signal such as GPS emitted by the SD 14. The camera may take the form of a CCD or CMOS sensor found in consumer photographic equipment or may take the form of other computer vision systems such as Lidar, Radar and other refracted light or sound-based systems.” McBride at ¶ [0127]: The proposed system can obtain weather using sensors and cameras 24 in the SDs 14, or information may be downloaded from high-fidelity third-party providers such as WeatherSource.)
The ordered combination of elements in the Dependent Claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself. Therefore, under Step 2B, Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. Thus, Claims 1-20 are ineligible with respect to the 35 U.S.C. § 101 analysis.
Claim Rejections - 35 USC § 103
10. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
11. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
12. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
13. Claims 1-7, 10-13, 15-17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub (US 2023/0135611 A1) hereinafter Kojo, et. al., in view of US PG Pub (US 2016/0092808 A1) hereinafter Cheng, et. al., in view of US PG Pub (US 2023/0085225 A1) hereinafter Matsuoka, et. al., in view of US PG Pub (US 2012/00290104 A1) hereinafter Holt, et. al.
Regarding Independent Claim 1, Kojo method for updating a set of tasks for greenhouse gas mitigation teaches the following:
- generating a set of tasks (see at least Kojo: (Dependent Claims 5-6 of Kojo) & ¶ [0024]. Kojo teaches that the platform 100 can be accessed and used to buy Meta Carbon Credits (and/or fractions thereof) 142 to directly offset CO2e emissions arising from any items that may be relevant to the user's business, consumption or various operations and activities. See at least Dependent Claims 5-6 of Kojo: Each request including one or more of a list of activities or an emissions value to offset, or an amount of meta carbon credits to purchase.), wherein each task comprises an offset potential (see at least Kojo: ¶ [0054-0056] & ¶ [0069] & ¶ [0126] & ¶ [0132-0136]. Kojo notes that a trained model may then be applied to the aggregated plurality of requests at step 230 to select CIM records from a database and amounts associated with each selected CIM record. The trained model may select CIM records based on the CIM allocation rules and the batch emissions value, and may select CIM records by optimizing an offset value of each selected CIM record in view of a cost associated with each selected CIM record. See at least Kojo at ¶ [0054-0056]: Sequestering 1000 kg of CO2 from the atmosphere through a specific CIM costs $20. The CIM has been assigned an OV of 860, which means that the Platform's internal experts have evaluated one Carbon Credit from that CIM to sequester in reality 860 kg of CO2e in the atmosphere (even though it is being marketed by the CIM Supplier as doing so at 1000 kg). The CIM is split up into 1000 CIM Units, each CIM Unit representing 1/1000 CIM and costing $20/1000=$0.02. The OV of a CIM Unit is OV=860/1000=0.86. To Offset the EV of the television set mentioned above (EV=500), 581 CIM Units are needed (500 EV/0.86 OV≈581). In this case, the Offsetting costs $ 0.02*581=$11.62. See at least Kojo at ¶ [0126]: User Orders from the Emission Calculations Engine and to transform them into an EV Batch 134, to select and allocate CIM Units from the CIM Pool 170 within received constraints, to compile an OV Batch 140 to match the EV Batch 134 and to issue and assign a corresponding amount of MCCs (and/or fractions thereof) 142 to realize the Offsetting, other climate action or the creation of MCCs for another purchase as requested in the User Orders included in the applicable CME Transaction. See at least Kojo at ¶ [0132-0136]: noting CIM 1: CIM1: type=“removal”; method=“reforestation”; region=“Asia-Pacific”; price=$10; OV=860 (For details on how the OV of a CIM is determined, see the definition of “Offset Value (OV)” above. CIM2: type=“removal”; method=“mechanical capture”; region=“Europe”; price=$28; OV=950; CIM3: type=“removal”; method=“mechanical capture”; region=“North America”; $29; OV=1100. The penalty factors will be applied to the Offset Value of the OV Batch during a single or multiple optimization runs (as may be set in the Optimization Run Rules).) and one or more failure mechanisms (see at least Kojo: ¶ [0076] & ¶ [0196] & ¶ [0201]. Kojo notes that Yet another CAR could be that no transaction may contain more than 5% CIM Units from CIMs with a Failure Risk Factor of over x. See also Kojo at ¶ [0185]: CAR3 737: the CAR may require allocation of at most 5% of aggregate OV from CIMs with failure_risk>14, therefore no more than 35 OV of the batch may be allocated to CIMs having a failure risk parameter greater than 14. See also Tables 1-5 of Kojo noting failure risk mechanisms or failure risk conditions.), and wherein the set of tasks comprises a metric (see at least Kojo: Tables 1-5 & ¶ [0095] & ¶ [0110] & ¶ [0223]. Kojo notes at ¶ [0095] that the CARs are set by internal experts for the purposes of realizing certain overarching policy goals, of risk management, of inventory management or other similar reasons. Kojo teaches at ¶ [0110] that these rules ensure that in the absence or despite of User Preferences the goals of e.g. risk management and indirect benefits, such as increased biodiversity, are reached when processing a CME Transaction. See also Kojo at ¶ [0223] noting “Table 9, the Acceptance Module 138 may also use other metrics for measuring the quality of TB1 747, such as expected average OV/price based on various meta data such as statistical average increase in quality over time.”);
- determining, by a machine learning model (see at least Kojo: ¶ [0034] & ¶ [0106] & ¶ [0164]. Kojo notes that to increase the optimal performance of the Optimization Logic described above, a method for using machine learning to modify the parameters of optimization logic is described. See also Kojo at ¶ [0034]: The engine within the Platform that, as described in this document, employs artificial intelligence and/or machine learning to carry out CME Transactions, and to report the results of the transactions as well as to receive data and feedback on the successfulness of the transactions and other information in order to adjust and optimize certain data in the Platform and its usage of CIM Allocation Rules and User Preferences in future transactions. See also Kojo at ¶ [0106]: This process may be performed or assisted with computer methods including artificial intelligence and/or machine learning.) and based on multiple data types from a plurality of sources (see at least Kojo: ¶ [0022] & ¶ [0065] & ¶ [0092]. Kojo teaches that the emissions calculation engine 120 may estimate the carbon footprints of Items on the basis of the items' characteristics or qualities by using categorical Emission Values or Emission Values of similar Items, or may receive the Emission Values of Items from other sources, resulting in emission value estimates 122, 124, and 126. See also Kojo at ¶ [0065]: These data may have been received directly from the Item suppliers (e.g. product manufacturers or service providers), from this or other Users, from research or from other sources (e.g. public or commercial climate impact indices). If no specific EV data exist on a certain Item the engine may apply EV estimates made on the basis of the characteristics of the Item or categorical types thereof. These estimates may have been received from external CO2e emission indices, from research or from other sources. See also Kojo at ¶ [0092]: The Carbon Market Engine 130 may also report to the User(s) about the sources and amounts of CIM Units allocated and the MCCs (and/or fractions thereof) issued and assigned to the User Order(s) via reporting module 144. “See also Tables 1-9 of Kojo noting multiple data types.”), that an overall risk score of the set of tasks exceeds a first failure threshold due to a risk score of a task of the set of tasks exceeding a second threshold (see at least Kojo: ¶ [0180-0186] & (Dependent Claims 4 and 8-9 of Kojo) & (Tables 1-9). Kojo teaches that the plurality of factors comprising an offset value, an integrity score, an impact factor, and a failure factor, the plurality of factors being satisfied when each value of the new CIM record for the plurality of factors satisfies a predetermined threshold value. See also Dependent Claim 8 of Kojo: Re-selecting CIM records to generate a second set of CIM records when the scaled objective value of the first set of CIM records is less than a predetermined threshold, the second set of CIM records being the selected CIM records when a scaled objective value of the second set of CIM records is greater than the predetermined threshold. See also Dependent Claim 9 of Kojo: Comparing the scaled objective value of the second set of CIM records to historical objective values of similar CIM record groups, the trained model being re-run when the scaled objective value of the second set of CIM records is more than a predetermined threshold less than the historical objective values of similar CIM record groups. See also Tables 1-9 of Kojo noting overall risk scores.);
- selecting a replacement task for the task, the selecting comprising (see at least Kojo: ¶ [0010] & ¶ [0180-0186] & ¶ [0221] & (Tables 1-9). Kojo teaches that aggregate schema 739 may organize the CARs and user preference rules from 710 to facilitate selection of the CIMs for the batch, and may be applied sequentially by the Ranking and Organization Module (ROM) 136. See also Kojo at ¶ [0010] noting identifying a selected set of CIM records using a trained model. See also Kojo at ¶ [0221]: Kojo teaches that the ROM 136 may for example begin by looking for a replacement for the CIM with the lowest OV/price with failure_risk of 15 or higher in the batch. In this case, the CIM to be replaced would be CIM.1.a.iii. The ROM may do this for example by re-running CAR1, with the results being shown in Table 6. See also Tables 1-9 of Kojo notes replacement tasks, ¶ [0040-0045] & ¶ [0080-0083].);
- receiving, a plurality of replacement candidates, each replacement candidate comprising a candidate offset potential and one or more candidate failure mechanisms (see at least Kojo: ¶ [0040-0045] & ¶ [0080-0083] & (Tables 1-9). Kojo notes that in Example 1: the production chain of a specific television set (Item) causes the emission of 500 kg CO2e into the atmosphere. The Emission Value of the Item is EV=500. Example 2: the User wishes to take action to reduce 2000 kg of CO2 in the atmosphere. The Emission Value input by the User is EV=2000. Example 3: the User wishes to buy 4 MCCs. Each MCC representing 1000 kg of CO2 in the atmosphere, the Emission Value input by the User is EV=4000. See also Kojo at [0080-0083]: CIM1: type=“avoidance”; method=“forest protection”; region=“South America”; climate_integrity_score=82; price=$10.50; OV=670 CIM2: type=“removal” method=“reforestation”; region=“Asia-Pacific”; climate_integrity_score=84; price=$12.50; OV=845 CIM3: type=“removal” method=“mechanical capture”; region=“North America”; climate_integrity_score=95; price=$25.75; OV=1115CIMs using reforestation and/or forest protection as a method typically have a lower price per tCO2, but they are usually marked with higher uncertainty (due to e.g. forest fires and other natural disasters) and lower permanence (due to e.g. using living trees in a partially controlled environment as carbon storage). See also Kojo at ¶ [0221]: Kojo teaches that the ROM 136 may for example begin by looking for a replacement for the CIM with the lowest OV/price with failure_risk of 15 or higher in the batch. In this case, the CIM to be replaced would be CIM.1.a.iii. The ROM may do this for example by re-running CAR1, with the results being shown in Table 6. See also Jojo at ¶ [0133-0153] and also Kojo at Tables 1-9.).
Kojo method for updating a set of tasks for greenhouse gas mitigation does not explicitly disclose, but Cheng in the analogous art for updating a set of tasks for greenhouse gas mitigation disclose the following:
- assigning, by the machine learning model (see at least Cheng: ¶ [0049] & ¶ [0073] & (Claims 8-9 of Cheng). Cheng teaches that machine learning algorithms, neural networks, or any other suitable function capable of analyzing historical maintenance data for purposes of enabling predictions of future causal connections between failures of dependent components. See also (Dependent Claims 8-9 of Cheng): “Causality analyzer is configured to implement a machine learning algorithm to mine the maintenance data and train the maintenance policy generator to predict potential production losses associated with the future maintenance events, and thereby facilitate generation of the maintenance policy. The machine learning algorithm includes a Bayesian algorithm, and wherein the causality analyzer is configured to generate probability tables for corresponding nodes of a Bayesian network structure in which the nodes represent corresponding failure events of the plurality of components and reflect the operational dependencies between pairs of the plurality of components.) and to each of the plurality of replacement candidates, a replacement score for the replacement candidate based on a predicted failure correlation of the replacement candidate with respect to each other sets of the set of tasks (see at least Cheng: ¶ [0041-0042] & ¶ [0063] & ¶ [0068]. Cheng teaches that will of course be appreciated that the various factors considered by the example score calculator 122 of FIG. 1 are merely non-limiting examples of the types of criticality score factors that may be utilized by the critical component identifier 120. For example, it may occur that a failed component experiences relatively little downtime in cases in which a temporary replacement component is available. However, cost associated with such replacement components, and/or with other activity required to avoid downtime and maintain operations of the production facility during a repair or replacement of the failed component, may also be quantified and included within the criticality score for the component in question. See also Cheng at ¶ [0063]: “For example, such costs can be associated with a cost of a replacement part, including associated delivery fees and delivery times. As referenced herein, such operational costs can also refer to costs associated with temporary replacement parts that are used until new replacement parts are received, or any other costs related to, or caused by, a particular failure.” See also Cheng at ¶ [0068]: “Infer, deduce, or otherwise obtain at least an approximate replacement value for any such missing data values within the event data 208. As a simplified example, it may occur that the event data 208 includes, for a specific failure, a known failure type 220 associated with a first valve. However, the corresponding failure location 212 may not be known from reported event data.”), wherein the predicted failure correlation indicates whether the replacement candidate and the remaining tasks are likely to fail together, independently, or both (see at least Cheng: ¶ [0068] & ¶ [0080] & ¶ [0117]. Cheng teaches that a failure of the component 106 will directly cause a corresponding failure of one or both of the components 108, 110. There may be a correlation between such failures or other maintenance events, which may or may not rise to a level of actual or direct causality. For example, in the examples provided below in which a Bayesian network is utilized, conditional probabilities associating a failure of a particular component with one or more preceding conditions, including failure of a preceding component, may be characterized. Thus, it may be appreciated that the term causal connection or causality should be understood to include potential or inferred causation, thereby including correlations and probabilities of relationships between failures or other maintenance events. See also Cheng at ¶ [0041-0042].)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Kojo method for updating a set of tasks for greenhouse gas mitigation with the aforementioned teachings of: assigning, by the machine learning model and to each of the plurality of replacement candidates, a replacement score for the replacement candidate based on a predicted failure correlation of the replacement candidate with respect to each other sets of the set of tasks, wherein the predicted failure correlation indicates whether the replacement candidate and the remaining tasks are likely to fail together, independently, or both, and in view of Cheng, whereby specifying component level maintenance activities as part of such maintenance policies, the maintenance policy generator is capable of quantifying and otherwise characterizing relative benefits of potential maintenance policies with respect to actual or potential production losses occurred. For example, the maintenance policy generator may provide a number of different potential maintenance policies, along with associated information regarding corresponding production and production losses, so that a user of the system may select an appropriate, desired maintenance policy. Similarly, the maintenance policy generator may provide an appropriate graphical user interface for such a user to explore various “what-if” scenarios with respect to relative effects of potential changes to the existing maintenance policy, as quantified with respect to associated potential production losses (see at least Cheng: ¶ [0051].). Moreover, a ML algorithm is capable of analyzing historical maintenance data for purposes of enabling predictions of future causal connections between failures of dependent components (see at least Cheng: ¶ [0049].).
Further, the claimed invention is merely a combination of old elements in a similar field for updating a set of tasks for greenhouse gas mitigation and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Cheng, the results of the combination were predictable.
Kojo / Cheng method for updating a set of tasks for greenhouse gas mitigation does not explicitly disclose, but Matsuoka in the analogous art for updating a set of tasks for greenhouse gas mitigation disclose the following:
- ranking the plurality of replacement candidates based on the replacement scores (see at least Matsuoka: ¶ [0050-0051] & ¶ [0077-0079]. Matsuoka teaches that the task recommendation system can rank the new projects and/or tasks based on a likelihood of the member 110 selecting the project and/or task for delegation to the representative 104 for performance and/or coordination with third-party services 114. Alternatively, the task recommendation system 106 may rank the projects and/or tasks based on the level of urgency for completion of each project and/or task. The level of urgency may be determined based on member characteristics (e.g., data corresponding to a member's own prioritization of certain tasks or categories of tasks) and/or potential risks to the member if the project and/or task is not performed. For example, a task corresponding to replacement or installation of carbon monoxide detectors within the member's home may be ranked higher than a task corresponding to the replacement of a refrigerator water dispenser filter, as carbon monoxide filters may be more critical to member safety. As another illustrative example, if a member 110 places significant importance on the maintenance of their vehicle, the task recommendation system 106 may rank a task related to vehicle maintenance higher than a task related to other types of maintenance. See also Cheng at ¶ [0077-0079].);
- selecting, based on the ranking, the replacement task (see at least Matsuoka: ¶ [0050-0051] & ¶ [0077-0079] & ¶ [0117]. Matsuoka teaches that the newly created task or project may be ranked according to a likelihood of the member selecting the task or project for delegation to the representative 104 for performance and coordination with third-party services. Alternatively, the new task or project may be ranked based on the level of urgency for completion of each project or task. See also Matsuoka at ¶ [0041-0042]: “The representative 104, based on their knowledge of the member 110, may select any of the identified one or more projects and/or tasks for presentation to the member 110. In some instances, if the representative 104 selects any of the identified one or more projects and/or tasks, the task recommendation system 106 may provide, via the representative console, one or more task templates that may be used to further define the selected projects and/or tasks. The one or more task templates may correspond to the task type or category for the projects and/or tasks being defined.”).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Kojo / Cheng method for updating a set of tasks for greenhouse gas mitigation with the aforementioned teachings of: ranking the plurality of replacement candidates based on the replacement scores and selecting based on the ranking, the replacement task, and in further view of Matsuoka, in order for the task recommendation system of Matsuoka rank the projects and/or tasks based on the level of urgency for completion of each project and/or task. The level of urgency may be determined based on member characteristics (e.g., data corresponding to a member's own prioritization of certain tasks or categories of tasks) and/or potential risks to the member if the project and/or task is not performed. For example, a task corresponding to replacement or installation of carbon monoxide detectors within the member's home may be ranked higher than a task corresponding to the replacement of a refrigerator water dispenser filter, as carbon monoxide filters may be more critical to member safety (see at least Matsuoka: ¶ [0050].).
Further, the claimed invention is merely a combination of old elements in a similar field for updating a set of tasks for greenhouse gas mitigation and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Matsuoka, the results of the combination were predictable.
Kojo / Cheng / Matsuoka method for updating a set of tasks for greenhouse gas mitigation does not explicitly disclose, but Holt in the analogous art for updating a set of tasks for greenhouse gas mitigation disclose the following:
- generating, an updated set of tasks including the replacement task in place of the task that caused the overall risk score of the of tasks to exceed the first failure threshold, wherein an updated overall risk score of the updated set of tasks satisfies the first failure threshold (see at least Holt: Figs. 3-4 & ¶ [0061-0069] & ¶ [0079]. Holt teaches that the next scheduled action may include a re-scheduling of the instrumentation calibration date (e.g., move date forward or move date backwards), or the creation of a work order for replacement or repair of the instrumentation 26. See also Holt at ¶ [0068]: For instruments 26 that may be more critical to plant operations, the risk threshold of not using the instrument 26 may be exceeded (decision 110). The logic 100 may then decide on any possible mitigation courses of action (decision 116). In certain circumstances, it may be possible to mitigate the risk to plant operations by selecting certain mitigation actions (block 118). For example, if the instrument 26 that has failed is measuring turbine temperatures (e.g., HP turbine 84 or LP turbine 86), then the turbine may be allowed to operate, albeit, at reduced limits. For example, the turbine may be allowed to operate at 95%, 90%, 80%, 50% of maximum load. See also Holt at ¶ [0069]: For example, if the instrumentation 26 that may have become inoperable includes instrumentation 26 required for emission monitoring, then the recommended action may include a recommendation for immediate replacement of the failed instrumentation 26 and an automated action to shut down the plant if the replacement is not completed before the end of a certain time period (e.g., 15 minutes, 1 hour, 4 hours, 1 day). See also Holt at ¶ [0079]: The logic 100 may update the risk projection (block 106) of replacing the equipment 24 and/or instrumentation 26 with newer designs, and compare the replacement risk against any updated risk threshold (block 108). Likewise, the risk of not upgrading the plant 10 resources may be used as a point of comparison. Should the risk threshold of upgrading the equipment not exceed the updated risk threshold (block 110), then a next scheduled action may be calculated (block 112) to include a schedule and list of equipment 24 and/or instrumentation 26 upgrades. In this way, the logic 100 may monitor inputs 28 and 30 so as to derive one or more upgrades to the plant 10 that may increase the plant's efficiency and production. See also Holt at ¶ [0082]. See also Holt at Fig. 3 step 116 noting mitigation available? -> Fig. 3 step 110 noting risk threshold exceeded?.)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Kojo / Cheng / Matsuoka method for updating a set of tasks for greenhouse gas mitigation with the aforementioned teachings of: generating, an updated set of tasks including the replacement ask in place of the task that caused the overall risk score of the set of tasks to exceed the first failure threshold, wherein an updated overall risk score of the updated set of tasks satisfies the first failure threshold, and in further view of Holt, whereby a risk of equipment failure may be calculated by the risk calculation engine based on the dynamic and the static data. The derived risk may then be input into the DSS, and the DSS may then derive operational decisions, such as risk mitigation decisions and recommended actions, that may result in a more efficient plant operation. A method is also provided that may enable a continuous monitoring of the dynamic and the static inputs, so as to update risk projections and/or risk thresholds associated with plant equipment and operations. The risk projections and/or thresholds may then be used to derive actions suitable for improving the use of the equipment and increasing plant reliability and efficiency (see at least Holt: ¶ [0025].).
Further, the claimed invention is merely a combination of old elements in a similar field for updating a set of tasks for greenhouse gas mitigation and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Holt, the results of the combination were predictable.
Regarding Independent Claim 19, Kojo system for updating a set of tasks for greenhouse gas mitigation teaches the following:
- comprising one or more computers (see at least Kojo: Fig. 12 & ¶ [0228].) and one or more storage devices (see at least Kojo: Fig. 12 & ¶ [0230-0232].) on which are stored instructions that are operable (see at least Kojo: ¶ [0229] & ¶ [0232].), when executed by the one or more computers (see at least Kojo: Fig. 12 & ¶ [0228].), to cause the one or more computers (see at least Kojo: Fig. 12 & ¶ [0228].) to perform operations comprising:
- generating a set of tasks (see at least Kojo: (Dependent Claims 5-6 of Kojo) & ¶ [0024]. Kojo teaches that the platform 100 can be accessed and used to buy Meta Carbon Credits (and/or fractions thereof) 142 to directly offset CO2e emissions arising from any items that may be relevant to the user's business, consumption or various operations and activities. See at least Dependent Claims 5-6 of Kojo: Each request including one or more of a list of activities or an emissions value to offset, or an amount of meta carbon credits to purchase.), wherein each task comprises an offset potential (see at least Kojo: ¶ [0054-0056] & ¶ [0069] & ¶ [0126] & ¶ [0132-0136]. Kojo notes that a trained model may then be applied to the aggregated plurality of requests at step 230 to select CIM records from a database and amounts associated with each selected CIM record. The trained model may select CIM records based on the CIM allocation rules and the batch emissions value, and may select CIM records by optimizing an offset value of each selected CIM record in view of a cost associated with each selected CIM record. See at least Kojo at ¶ [0054-0056]: Sequestering 1000 kg of CO2 from the atmosphere through a specific CIM costs $20. The CIM has been assigned an OV of 860, which means that the Platform's internal experts have evaluated one Carbon Credit from that CIM to sequester in reality 860 kg of CO2e in the atmosphere (even though it is being marketed by the CIM Supplier as doing so at 1000 kg). The CIM is split up into 1000 CIM Units, each CIM Unit representing 1/1000 CIM and costing $20/1000=$0.02. The OV of a CIM Unit is OV=860/1000=0.86. To Offset the EV of the television set mentioned above (EV=500), 581 CIM Units are needed (500 EV/0.86 OV≈581). In this case, the Offsetting costs $ 0.02*581=$11.62. See at least Kojo at ¶ [0126]: User Orders from the Emission Calculations Engine and to transform them into an EV Batch 134, to select and allocate CIM Units from the CIM Pool 170 within received constraints, to compile an OV Batch 140 to match the EV Batch 134 and to issue and assign a corresponding amount of MCCs (and/or fractions thereof) 142 to realize the Offsetting, other climate action or the creation of MCCs for another purchase as requested in the User Orders included in the applicable CME Transaction. See at least Kojo at ¶ [0132-0136]: noting CIM 1: CIM1: type=“removal”; method=“reforestation”; region=“Asia-Pacific”; price=$10; OV=860 (For details on how the OV of a CIM is determined, see the definition of “Offset Value (OV)” above. CIM2: type=“removal”; method=“mechanical capture”; region=“Europe”; price=$28; OV=950; CIM3: type=“removal”; method=“mechanical capture”; region=“North America”; $29; OV=1100. The penalty factors will be applied to the Offset Value of the OV Batch during a single or multiple optimization runs (as may be set in the Optimization Run Rules).) and one or more failure mechanisms (see at least Kojo: ¶ [0076] & ¶ [0196] & ¶ [0201]. Kojo notes that Yet another CAR could be that no transaction may contain more than 5% CIM Units from CIMs with a Failure Risk Factor of over x. See also Kojo at ¶ [0185]: CAR3 737: the CAR may require allocation of at most 5% of aggregate OV from CIMs with failure_risk>14, therefore no more than 35 OV of the batch may be allocated to CIMs having a failure risk parameter greater than 14. See also Tables 1-5 of Kojo noting failure risk mechanisms or failure risk conditions.), and wherein the set of tasks comprises a metric (see at least Kojo: Tables 1-5 & ¶ [0095] & ¶ [0110] & ¶ [0223]. Kojo notes at ¶ [0095] that the CARs are set by internal experts for the purposes of realizing certain overarching policy goals, of risk management, of inventory management or other similar reasons. Kojo teaches at ¶ [0110] that these rules ensure that in the absence or despite of User Preferences the goals of e.g. risk management and indirect benefits, such as increased biodiversity, are reached when processing a CME Transaction. See also Kojo at ¶ [0223] noting “Table 9, the Acceptance Module 138 may also use other metrics for measuring the quality of TB1 747, such as expected average OV/price based on various meta data such as statistical average increase in quality over time.”);
- determining, by a machine learning model (see at least Kojo: ¶ [0034] & ¶ [0106] & ¶ [0164]. Kojo notes that to increase the optimal performance of the Optimization Logic described above, a method for using machine learning to modify the parameters of optimization logic is described. See also Kojo at ¶ [0034]: The engine within the Platform that, as described in this document, employs artificial intelligence and/or machine learning to carry out CME Transactions, and to report the results of the transactions as well as to receive data and feedback on the successfulness of the transactions and other information in order to adjust and optimize certain data in the Platform and its usage of CIM Allocation Rules and User Preferences in future transactions. See also Kojo at ¶ [0106]: This process may be performed or assisted with computer methods including artificial intelligence and/or machine learning.) and based on multiple data types from a plurality of sources (see at least Kojo: ¶ [0022] & ¶ [0065] & ¶ [0092]. Kojo teaches that the emissions calculation engine 120 may estimate the carbon footprints of Items on the basis of the items' characteristics or qualities by using categorical Emission Values or Emission Values of similar Items, or may receive the Emission Values of Items from other sources, resulting in emission value estimates 122, 124, and 126. See also Kojo at ¶ [0065]: These data may have been received directly from the Item suppliers (e.g. product manufacturers or service providers), from this or other Users, from research or from other sources (e.g. public or commercial climate impact indices). If no specific EV data exist on a certain Item the engine may apply EV estimates made on the basis of the characteristics of the Item or categorical types thereof. These estimates may have been received from external CO2e emission indices, from research or from other sources. See also Kojo at ¶ [0092]: The Carbon Market Engine 130 may also report to the User(s) about the sources and amounts of CIM Units allocated and the MCCs (and/or fractions thereof) issued and assigned to the User Order(s) via reporting module 144. “See also Tables 1-9 of Kojo noting multiple data types.”), that an overall risk score of the set of tasks exceeds a first failure threshold due to a risk score of a task of the set of tasks exceeding a second threshold (see at least Kojo: ¶ [0180-0186] & (Dependent Claims 4 and 8-9 of Kojo) & (Tables 1-9). Kojo teaches that the plurality of factors comprising an offset value, an integrity score, an impact factor, and a failure factor, the plurality of factors being satisfied when each value of the new CIM record for the plurality of factors satisfies a predetermined threshold value. See also Dependent Claim 8 of Kojo: Re-selecting CIM records to generate a second set of CIM records when the scaled objective value of the first set of CIM records is less than a predetermined threshold, the second set of CIM records being the selected CIM records when a scaled objective value of the second set of CIM records is greater than the predetermined threshold. See also Dependent Claim 9 of Kojo: Comparing the scaled objective value of the second set of CIM records to historical objective values of similar CIM record groups, the trained model being re-run when the scaled objective value of the second set of CIM records is more than a predetermined threshold less than the historical objective values of similar CIM record groups. See also Tables 1-9 of Kojo noting overall risk scores.);
- selecting a replacement task for the task, the selecting comprising (see at least Kojo: ¶ [0010] & ¶ [0180-0186] & ¶ [0221] & (Tables 1-9). Kojo teaches that aggregate schema 739 may organize the CARs and user preference rules from 710 to facilitate selection of the CIMs for the batch, and may be applied sequentially by the Ranking and Organization Module (ROM) 136. See also Kojo at ¶ [0010] noting identifying a selected set of CIM records using a trained model. See also Kojo at ¶ [0221]: Kojo teaches that the ROM 136 may for example begin by looking for a replacement for the CIM with the lowest OV/price with failure_risk of 15 or higher in the batch. In this case, the CIM to be replaced would be CIM.1.a.iii. The ROM may do this for example by re-running CAR1, with the results being shown in Table 6. See also Tables 1-9 of Kojo notes replacement tasks, ¶ [0040-0045] & ¶ [0080-0083].);
- receiving, a plurality of replacement candidates, each replacement candidate comprising a candidate offset potential and one or more candidate failure mechanisms (see at least Kojo: ¶ [0040-0045] & ¶ [0080-0083] & (Tables 1-9). Kojo notes that in Example 1: the production chain of a specific television set (Item) causes the emission of 500 kg CO2e into the atmosphere. The Emission Value of the Item is EV=500. Example 2: the User wishes to take action to reduce 2000 kg of CO2 in the atmosphere. The Emission Value input by the User is EV=2000. Example 3: the User wishes to buy 4 MCCs. Each MCC representing 1000 kg of CO2 in the atmosphere, the Emission Value input by the User is EV=4000. See also Kojo at [0080-0083]: CIM1: type=“avoidance”; method=“forest protection”; region=“South America”; climate_integrity_score=82; price=$10.50; OV=670 CIM2: type=“removal” method=“reforestation”; region=“Asia-Pacific”; climate_integrity_score=84; price=$12.50; OV=845 CIM3: type=“removal” method=“mechanical capture”; region=“North America”; climate_integrity_score=95; price=$25.75; OV=1115CIMs using reforestation and/or forest protection as a method typically have a lower price per tCO2, but they are usually marked with higher uncertainty (due to e.g. forest fires and other natural disasters) and lower permanence (due to e.g. using living trees in a partially controlled environment as carbon storage). See also Kojo at ¶ [0221]: Kojo teaches that the ROM 136 may for example begin by looking for a replacement for the CIM with the lowest OV/price with failure_risk of 15 or higher in the batch. In this case, the CIM to be replaced would be CIM.1.a.iii. The ROM may do this for example by re-running CAR1, with the results being shown in Table 6. See also Jojo at ¶ [0133-0153] and also Kojo at Tables 1-9.);
Kojo system for updating a set of tasks for greenhouse gas mitigation does not explicitly disclose, but Cheng in the analogous art for updating a set of tasks for greenhouse gas mitigation disclose the following:
- assigning, by the machine learning model (see at least Cheng: ¶ [0049] & ¶ [0073] & (Claims 8-9 of Cheng). Cheng teaches that machine learning algorithms, neural networks, or any other suitable function capable of analyzing historical maintenance data for purposes of enabling predictions of future causal connections between failures of dependent components. See also (Dependent Claims 8-9 of Cheng): “Causality analyzer is configured to implement a machine learning algorithm to mine the maintenance data and train the maintenance policy generator to predict potential production losses associated with the future maintenance events, and thereby facilitate generation of the maintenance policy. The machine learning algorithm includes a Bayesian algorithm, and wherein the causality analyzer is configured to generate probability tables for corresponding nodes of a Bayesian network structure in which the nodes represent corresponding failure events of the plurality of components and reflect the operational dependencies between pairs of the plurality of components.) and to each of the plurality of replacement candidates, a replacement score for the replacement candidate based on a predicted failure correlation of the replacement candidate with respect to each other sets of the set of tasks (see at least Cheng: ¶ [0041-0042] & ¶ [0063] & ¶ [0068]. Cheng teaches that will of course be appreciated that the various factors considered by the example score calculator 122 of FIG. 1 are merely non-limiting examples of the types of criticality score factors that may be utilized by the critical component identifier 120. For example, it may occur that a failed component experiences relatively little downtime in cases in which a temporary replacement component is available. However, cost associated with such replacement components, and/or with other activity required to avoid downtime and maintain operations of the production facility during a repair or replacement of the failed component, may also be quantified and included within the criticality score for the component in question. See also Cheng at ¶ [0063]: “For example, such costs can be associated with a cost of a replacement part, including associated delivery fees and delivery times. As referenced herein, such operational costs can also refer to costs associated with temporary replacement parts that are used until new replacement parts are received, or any other costs related to, or caused by, a particular failure.” See also Cheng at ¶ [0068]: “Infer, deduce, or otherwise obtain at least an approximate replacement value for any such missing data values within the event data 208. As a simplified example, it may occur that the event data 208 includes, for a specific failure, a known failure type 220 associated with a first valve. However, the corresponding failure location 212 may not be known from reported event data.”), wherein the predicted failure correlation indicates whether the replacement candidate and the remaining tasks are likely to fail together, independently, or both (see at least Cheng: ¶ [0068] & ¶ [0080] & ¶ [0117]. Cheng teaches that a failure of the component 106 will directly cause a corresponding failure of one or both of the components 108, 110. There may be a correlation between such failures or other maintenance events, which may or may not rise to a level of actual or direct causality. For example, in the examples provided below in which a Bayesian network is utilized, conditional probabilities associating a failure of a particular component with one or more preceding conditions, including failure of a preceding component, may be characterized. Thus, it may be appreciated that the term causal connection or causality should be understood to include potential or inferred causation, thereby including correlations and probabilities of relationships between failures or other maintenance events. See also Cheng at ¶ [0041-0042].)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Kojo system for updating a set of tasks for greenhouse gas mitigation with the aforementioned teachings of: assigning, by the machine learning model and to each of the plurality of replacement candidates, a replacement score for the replacement candidate based on a predicted failure correlation of the replacement candidate with respect to each other sets of the set of tasks, wherein the predicted failure correlation indicates whether the replacement candidate and the remaining tasks are likely to fail together, independently, or both, and in view of Cheng, whereby specifying component level maintenance activities as part of such maintenance policies, the maintenance policy generator is capable of quantifying and otherwise characterizing relative benefits of potential maintenance policies with respect to actual or potential production losses occurred. For example, the maintenance policy generator may provide a number of different potential maintenance policies, along with associated information regarding corresponding production and production losses, so that a user of the system may select an appropriate, desired maintenance policy. Similarly, the maintenance policy generator may provide an appropriate graphical user interface for such a user to explore various “what-if” scenarios with respect to relative effects of potential changes to the existing maintenance policy, as quantified with respect to associated potential production losses (see at least Cheng: ¶ [0051].). Moreover, a ML algorithm is capable of analyzing historical maintenance data for purposes of enabling predictions of future causal connections between failures of dependent components (see at least Cheng: ¶ [0049].).
Further, the claimed invention is merely a combination of old elements in a similar field for updating a set of tasks for greenhouse gas mitigation and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Cheng, the results of the combination were predictable.
Kojo / Cheng system for updating a set of tasks for greenhouse gas mitigation does not explicitly disclose, but Matsuoka in the analogous art for updating a set of tasks for greenhouse gas mitigation disclose the following:
- ranking the plurality of replacement candidates based on the replacement scores (see at least Matsuoka: ¶ [0050-0051] & ¶ [0077-0079]. Matsuoka teaches that the task recommendation system can rank the new projects and/or tasks based on a likelihood of the member 110 selecting the project and/or task for delegation to the representative 104 for performance and/or coordination with third-party services 114. Alternatively, the task recommendation system 106 may rank the projects and/or tasks based on the level of urgency for completion of each project and/or task. The level of urgency may be determined based on member characteristics (e.g., data corresponding to a member's own prioritization of certain tasks or categories of tasks) and/or potential risks to the member if the project and/or task is not performed. For example, a task corresponding to replacement or installation of carbon monoxide detectors within the member's home may be ranked higher than a task corresponding to the replacement of a refrigerator water dispenser filter, as carbon monoxide filters may be more critical to member safety. As another illustrative example, if a member 110 places significant importance on the maintenance of their vehicle, the task recommendation system 106 may rank a task related to vehicle maintenance higher than a task related to other types of maintenance. See also Cheng at ¶ [0077-0079].);
- selecting, based on the ranking, the replacement task (see at least Matsuoka: ¶ [0050-0051] & ¶ [0077-0079] & ¶ [0117]. Matsuoka teaches that the newly created task or project may be ranked according to a likelihood of the member selecting the task or project for delegation to the representative 104 for performance and coordination with third-party services. Alternatively, the new task or project may be ranked based on the level of urgency for completion of each project or task. See also Matsuoka at ¶ [0041-0042]: “The representative 104, based on their knowledge of the member 110, may select any of the identified one or more projects and/or tasks for presentation to the member 110. In some instances, if the representative 104 selects any of the identified one or more projects and/or tasks, the task recommendation system 106 may provide, via the representative console, one or more task templates that may be used to further define the selected projects and/or tasks. The one or more task templates may correspond to the task type or category for the projects and/or tasks being defined.”).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Kojo / Cheng system for updating a set of tasks for greenhouse gas mitigation with the aforementioned teachings of: ranking the plurality of replacement candidates based on the replacement scores and selecting based on the ranking, the replacement task, and in further view of Matsuoka, in order for the task recommendation system of Matsuoka rank the projects and/or tasks based on the level of urgency for completion of each project and/or task. The level of urgency may be determined based on member characteristics (e.g., data corresponding to a member's own prioritization of certain tasks or categories of tasks) and/or potential risks to the member if the project and/or task is not performed. For example, a task corresponding to replacement or installation of carbon monoxide detectors within the member's home may be ranked higher than a task corresponding to the replacement of a refrigerator water dispenser filter, as carbon monoxide filters may be more critical to member safety (see at least Matsuoka: ¶ [0050].).
Further, the claimed invention is merely a combination of old elements in a similar field for updating a set of tasks for greenhouse gas mitigation and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Matsuoka, the results of the combination were predictable.
Kojo / Cheng / Matsuoka system for updating a set of tasks for greenhouse gas mitigation does not explicitly disclose, but Holt in the analogous art for updating a set of tasks for greenhouse gas mitigation disclose the following:
- generating, an updated set of tasks including the replacement task in place of the task that caused the overall risk score of the of tasks to exceed the first failure threshold, wherein an updated overall risk score of the updated set of tasks satisfies the first failure threshold (see at least Holt: Figs. 3-4 & ¶ [0061-0069] & ¶ [0079]. Holt teaches that the next scheduled action may include a re-scheduling of the instrumentation calibration date (e.g., move date forward or move date backwards), or the creation of a work order for replacement or repair of the instrumentation 26. See also Holt at ¶ [0068]: For instruments 26 that may be more critical to plant operations, the risk threshold of not using the instrument 26 may be exceeded (decision 110). The logic 100 may then decide on any possible mitigation courses of action (decision 116). In certain circumstances, it may be possible to mitigate the risk to plant operations by selecting certain mitigation actions (block 118). For example, if the instrument 26 that has failed is measuring turbine temperatures (e.g., HP turbine 84 or LP turbine 86), then the turbine may be allowed to operate, albeit, at reduced limits. For example, the turbine may be allowed to operate at 95%, 90%, 80%, 50% of maximum load. See also Holt at ¶ [0069]: For example, if the instrumentation 26 that may have become inoperable includes instrumentation 26 required for emission monitoring, then the recommended action may include a recommendation for immediate replacement of the failed instrumentation 26 and an automated action to shut down the plant if the replacement is not completed before the end of a certain time period (e.g., 15 minutes, 1 hour, 4 hours, 1 day). See also Holt at ¶ [0079]: The logic 100 may update the risk projection (block 106) of replacing the equipment 24 and/or instrumentation 26 with newer designs, and compare the replacement risk against any updated risk threshold (block 108). Likewise, the risk of not upgrading the plant 10 resources may be used as a point of comparison. Should the risk threshold of upgrading the equipment not exceed the updated risk threshold (block 110), then a next scheduled action may be calculated (block 112) to include a schedule and list of equipment 24 and/or instrumentation 26 upgrades. In this way, the logic 100 may monitor inputs 28 and 30 so as to derive one or more upgrades to the plant 10 that may increase the plant's efficiency and production. See also Holt at ¶ [0082]. See also Holt at Fig. 3 step 116 noting mitigation available? -> Fig. 3 step 110 noting risk threshold exceeded?.)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Kojo / Cheng / Matsuoka system for updating a set of tasks for greenhouse gas mitigation with the aforementioned teachings of: generating, an updated set of tasks including the replacement ask in place of the task that caused the overall risk score of the set of tasks to exceed the first failure threshold, wherein an updated overall risk score of the updated set of tasks satisfies the first failure threshold, and in further view of Holt, whereby a risk of equipment failure may be calculated by the risk calculation engine based on the dynamic and the static data. The derived risk may then be input into the DSS, and the DSS may then derive operational decisions, such as risk mitigation decisions and recommended actions, that may result in a more efficient plant operation. A method is also provided that may enable a continuous monitoring of the dynamic and the static inputs, so as to update risk projections and/or risk thresholds associated with plant equipment and operations. The risk projections and/or thresholds may then be used to derive actions suitable for improving the use of the equipment and increasing plant reliability and efficiency (see at least Holt: ¶ [0025].).
Further, the claimed invention is merely a combination of old elements in a similar field for updating a set of tasks for greenhouse gas mitigation and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Holt, the results of the combination were predictable.
Regarding Independent Claim 20, Kojo non-transitory computer storage medium for updating a set of tasks for greenhouse gas mitigation teaches the following:
- encoded with instructions that, when executed by one or more computers (see at least Kojo: Fig. 12 & ¶ [0228].), cause the one or more computers to perform the following operations (see at least Kojo: Fig. 12 & ¶ [0229-0232].) operations:
- generating a set of tasks (see at least Kojo: (Dependent Claims 5-6 of Kojo) & ¶ [0024]. Kojo teaches that the platform 100 can be accessed and used to buy Meta Carbon Credits (and/or fractions thereof) 142 to directly offset CO2e emissions arising from any items that may be relevant to the user's business, consumption or various operations and activities. See at least Dependent Claims 5-6 of Kojo: Each request including one or more of a list of activities or an emissions value to offset, or an amount of meta carbon credits to purchase.), wherein each task comprises an offset potential (see at least Kojo: ¶ [0054-0056] & ¶ [0069] & ¶ [0126] & ¶ [0132-0136]. Kojo notes that a trained model may then be applied to the aggregated plurality of requests at step 230 to select CIM records from a database and amounts associated with each selected CIM record. The trained model may select CIM records based on the CIM allocation rules and the batch emissions value, and may select CIM records by optimizing an offset value of each selected CIM record in view of a cost associated with each selected CIM record. See at least Kojo at ¶ [0054-0056]: Sequestering 1000 kg of CO2 from the atmosphere through a specific CIM costs $20. The CIM has been assigned an OV of 860, which means that the Platform's internal experts have evaluated one Carbon Credit from that CIM to sequester in reality 860 kg of CO2e in the atmosphere (even though it is being marketed by the CIM Supplier as doing so at 1000 kg). The CIM is split up into 1000 CIM Units, each CIM Unit representing 1/1000 CIM and costing $20/1000=$0.02. The OV of a CIM Unit is OV=860/1000=0.86. To Offset the EV of the television set mentioned above (EV=500), 581 CIM Units are needed (500 EV/0.86 OV≈581). In this case, the Offsetting costs $ 0.02*581=$11.62. See at least Kojo at ¶ [0126]: User Orders from the Emission Calculations Engine and to transform them into an EV Batch 134, to select and allocate CIM Units from the CIM Pool 170 within received constraints, to compile an OV Batch 140 to match the EV Batch 134 and to issue and assign a corresponding amount of MCCs (and/or fractions thereof) 142 to realize the Offsetting, other climate action or the creation of MCCs for another purchase as requested in the User Orders included in the applicable CME Transaction. See at least Kojo at ¶ [0132-0136]: noting CIM 1: CIM1: type=“removal”; method=“reforestation”; region=“Asia-Pacific”; price=$10; OV=860 (For details on how the OV of a CIM is determined, see the definition of “Offset Value (OV)” above. CIM2: type=“removal”; method=“mechanical capture”; region=“Europe”; price=$28; OV=950; CIM3: type=“removal”; method=“mechanical capture”; region=“North America”; $29; OV=1100. The penalty factors will be applied to the Offset Value of the OV Batch during a single or multiple optimization runs (as may be set in the Optimization Run Rules).) and one or more failure mechanisms (see at least Kojo: ¶ [0076] & ¶ [0196] & ¶ [0201]. Kojo notes that Yet another CAR could be that no transaction may contain more than 5% CIM Units from CIMs with a Failure Risk Factor of over x. See also Kojo at ¶ [0185]: CAR3 737: the CAR may require allocation of at most 5% of aggregate OV from CIMs with failure_risk>14, therefore no more than 35 OV of the batch may be allocated to CIMs having a failure risk parameter greater than 14. See also Tables 1-5 of Kojo noting failure risk mechanisms or failure risk conditions.), and wherein the set of tasks comprises a metric (see at least Kojo: Tables 1-5 & ¶ [0095] & ¶ [0110] & ¶ [0223]. Kojo notes at ¶ [0095] that the CARs are set by internal experts for the purposes of realizing certain overarching policy goals, of risk management, of inventory management or other similar reasons. Kojo teaches at ¶ [0110] that these rules ensure that in the absence or despite of User Preferences the goals of e.g. risk management and indirect benefits, such as increased biodiversity, are reached when processing a CME Transaction. See also Kojo at ¶ [0223] noting “Table 9, the Acceptance Module 138 may also use other metrics for measuring the quality of TB1 747, such as expected average OV/price based on various meta data such as statistical average increase in quality over time.”);
- determining, by a machine learning model (see at least Kojo: ¶ [0034] & ¶ [0106] & ¶ [0164]. Kojo notes that to increase the optimal performance of the Optimization Logic described above, a method for using machine learning to modify the parameters of optimization logic is described. See also Kojo at ¶ [0034]: The engine within the Platform that, as described in this document, employs artificial intelligence and/or machine learning to carry out CME Transactions, and to report the results of the transactions as well as to receive data and feedback on the successfulness of the transactions and other information in order to adjust and optimize certain data in the Platform and its usage of CIM Allocation Rules and User Preferences in future transactions. See also Kojo at ¶ [0106]: This process may be performed or assisted with computer methods including artificial intelligence and/or machine learning.) and based on multiple data types from a plurality of sources (see at least Kojo: ¶ [0022] & ¶ [0065] & ¶ [0092]. Kojo teaches that the emissions calculation engine 120 may estimate the carbon footprints of Items on the basis of the items' characteristics or qualities by using categorical Emission Values or Emission Values of similar Items, or may receive the Emission Values of Items from other sources, resulting in emission value estimates 122, 124, and 126. See also Kojo at ¶ [0065]: These data may have been received directly from the Item suppliers (e.g. product manufacturers or service providers), from this or other Users, from research or from other sources (e.g. public or commercial climate impact indices). If no specific EV data exist on a certain Item the engine may apply EV estimates made on the basis of the characteristics of the Item or categorical types thereof. These estimates may have been received from external CO2e emission indices, from research or from other sources. See also Kojo at ¶ [0092]: The Carbon Market Engine 130 may also report to the User(s) about the sources and amounts of CIM Units allocated and the MCCs (and/or fractions thereof) issued and assigned to the User Order(s) via reporting module 144. “See also Tables 1-9 of Kojo noting multiple data types.”), that an overall risk score of the set of tasks exceeds a first failure threshold due to a risk score of a task of the set of tasks exceeding a second threshold (see at least Kojo: ¶ [0180-0186] & (Dependent Claims 4 and 8-9 of Kojo) & (Tables 1-9). Kojo teaches that the plurality of factors comprising an offset value, an integrity score, an impact factor, and a failure factor, the plurality of factors being satisfied when each value of the new CIM record for the plurality of factors satisfies a predetermined threshold value. See also Dependent Claim 8 of Kojo: Re-selecting CIM records to generate a second set of CIM records when the scaled objective value of the first set of CIM records is less than a predetermined threshold, the second set of CIM records being the selected CIM records when a scaled objective value of the second set of CIM records is greater than the predetermined threshold. See also Dependent Claim 9 of Kojo: Comparing the scaled objective value of the second set of CIM records to historical objective values of similar CIM record groups, the trained model being re-run when the scaled objective value of the second set of CIM records is more than a predetermined threshold less than the historical objective values of similar CIM record groups. See also Tables 1-9 of Kojo noting overall risk scores.);
- selecting a replacement task for the task, the selecting comprising (see at least Kojo: ¶ [0010] & ¶ [0180-0186] & ¶ [0221] & (Tables 1-9). Kojo teaches that aggregate schema 739 may organize the CARs and user preference rules from 710 to facilitate selection of the CIMs for the batch, and may be applied sequentially by the Ranking and Organization Module (ROM) 136. See also Kojo at ¶ [0010] noting identifying a selected set of CIM records using a trained model. See also Kojo at ¶ [0221]: Kojo teaches that the ROM 136 may for example begin by looking for a replacement for the CIM with the lowest OV/price with failure_risk of 15 or higher in the batch. In this case, the CIM to be replaced would be CIM.1.a.iii. The ROM may do this for example by re-running CAR1, with the results being shown in Table 6. See also Tables 1-9 of Kojo notes replacement tasks, ¶ [0040-0045] & ¶ [0080-0083].);
- receiving, a plurality of replacement candidates, each replacement candidate comprising a candidate offset potential and one or more candidate failure mechanisms (see at least Kojo: ¶ [0040-0045] & ¶ [0080-0083] & (Tables 1-9). Kojo notes that in Example 1: the production chain of a specific television set (Item) causes the emission of 500 kg CO2e into the atmosphere. The Emission Value of the Item is EV=500. Example 2: the User wishes to take action to reduce 2000 kg of CO2 in the atmosphere. The Emission Value input by the User is EV=2000. Example 3: the User wishes to buy 4 MCCs. Each MCC representing 1000 kg of CO2 in the atmosphere, the Emission Value input by the User is EV=4000. See also Kojo at [0080-0083]: CIM1: type=“avoidance”; method=“forest protection”; region=“South America”; climate_integrity_score=82; price=$10.50; OV=670 CIM2: type=“removal” method=“reforestation”; region=“Asia-Pacific”; climate_integrity_score=84; price=$12.50; OV=845 CIM3: type=“removal” method=“mechanical capture”; region=“North America”; climate_integrity_score=95; price=$25.75; OV=1115CIMs using reforestation and/or forest protection as a method typically have a lower price per tCO2, but they are usually marked with higher uncertainty (due to e.g. forest fires and other natural disasters) and lower permanence (due to e.g. using living trees in a partially controlled environment as carbon storage). See also Kojo at ¶ [0221]: Kojo teaches that the ROM 136 may for example begin by looking for a replacement for the CIM with the lowest OV/price with failure_risk of 15 or higher in the batch. In this case, the CIM to be replaced would be CIM.1.a.iii. The ROM may do this for example by re-running CAR1, with the results being shown in Table 6. See also Jojo at ¶ [0133-0153] and also Kojo at Tables 1-9.);
Kojo non-transitory computer storage medium for updating a set of tasks for greenhouse gas mitigation does not explicitly disclose, but Cheng in the analogous art for updating a set of tasks for greenhouse gas mitigation disclose the following:
- assigning, by the machine learning model (see at least Cheng: ¶ [0049] & ¶ [0073] & (Claims 8-9 of Cheng). Cheng teaches that machine learning algorithms, neural networks, or any other suitable function capable of analyzing historical maintenance data for purposes of enabling predictions of future causal connections between failures of dependent components. See also (Dependent Claims 8-9 of Cheng): “Causality analyzer is configured to implement a machine learning algorithm to mine the maintenance data and train the maintenance policy generator to predict potential production losses associated with the future maintenance events, and thereby facilitate generation of the maintenance policy. The machine learning algorithm includes a Bayesian algorithm, and wherein the causality analyzer is configured to generate probability tables for corresponding nodes of a Bayesian network structure in which the nodes represent corresponding failure events of the plurality of components and reflect the operational dependencies between pairs of the plurality of components.) and to each of the plurality of replacement candidates, a replacement score for the replacement candidate based on a predicted failure correlation of the replacement candidate with respect to each other sets of the set of tasks (see at least Cheng: ¶ [0041-0042] & ¶ [0063] & ¶ [0068]. Cheng teaches that will of course be appreciated that the various factors considered by the example score calculator 122 of FIG. 1 are merely non-limiting examples of the types of criticality score factors that may be utilized by the critical component identifier 120. For example, it may occur that a failed component experiences relatively little downtime in cases in which a temporary replacement component is available. However, cost associated with such replacement components, and/or with other activity required to avoid downtime and maintain operations of the production facility during a repair or replacement of the failed component, may also be quantified and included within the criticality score for the component in question. See also Cheng at ¶ [0063]: “For example, such costs can be associated with a cost of a replacement part, including associated delivery fees and delivery times. As referenced herein, such operational costs can also refer to costs associated with temporary replacement parts that are used until new replacement parts are received, or any other costs related to, or caused by, a particular failure.” See also Cheng at ¶ [0068]: “Infer, deduce, or otherwise obtain at least an approximate replacement value for any such missing data values within the event data 208. As a simplified example, it may occur that the event data 208 includes, for a specific failure, a known failure type 220 associated with a first valve. However, the corresponding failure location 212 may not be known from reported event data.”), wherein the predicted failure correlation indicates whether the replacement candidate and the remaining tasks are likely to fail together, independently, or both (see at least Cheng: ¶ [0068] & ¶ [0080] & ¶ [0117]. Cheng teaches that a failure of the component 106 will directly cause a corresponding failure of one or both of the components 108, 110. There may be a correlation between such failures or other maintenance events, which may or may not rise to a level of actual or direct causality. For example, in the examples provided below in which a Bayesian network is utilized, conditional probabilities associating a failure of a particular component with one or more preceding conditions, including failure of a preceding component, may be characterized. Thus, it may be appreciated that the term causal connection or causality should be understood to include potential or inferred causation, thereby including correlations and probabilities of relationships between failures or other maintenance events. See also Cheng at ¶ [0041-0042].)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Kojo system for updating a set of tasks for greenhouse gas mitigation with the aforementioned teachings of: assigning, by the machine learning model and to each of the plurality of replacement candidates, a replacement score for the replacement candidate based on a predicted failure correlation of the replacement candidate with respect to each other sets of the set of tasks, wherein the predicted failure correlation indicates whether the replacement candidate and the remaining tasks are likely to fail together, independently, or both, and in view of Cheng, whereby specifying component level maintenance activities as part of such maintenance policies, the maintenance policy generator is capable of quantifying and otherwise characterizing relative benefits of potential maintenance policies with respect to actual or potential production losses occurred. For example, the maintenance policy generator may provide a number of different potential maintenance policies, along with associated information regarding corresponding production and production losses, so that a user of the system may select an appropriate, desired maintenance policy. Similarly, the maintenance policy generator may provide an appropriate graphical user interface for such a user to explore various “what-if” scenarios with respect to relative effects of potential changes to the existing maintenance policy, as quantified with respect to associated potential production losses (see at least Cheng: ¶ [0051].). Moreover, a ML algorithm is capable of analyzing historical maintenance data for purposes of enabling predictions of future causal connections between failures of dependent components (see at least Cheng: ¶ [0049].).
Further, the claimed invention is merely a combination of old elements in a similar field for updating a set of tasks for greenhouse gas mitigation and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Cheng, the results of the combination were predictable.
Kojo / Cheng system for updating a set of tasks for greenhouse gas mitigation does not explicitly disclose, but Matsuoka in the analogous art for updating a set of tasks for greenhouse gas mitigation disclose the following:
- ranking the plurality of replacement candidates based on the replacement scores (see at least Matsuoka: ¶ [0050-0051] & ¶ [0077-0079]. Matsuoka teaches that the task recommendation system can rank the new projects and/or tasks based on a likelihood of the member 110 selecting the project and/or task for delegation to the representative 104 for performance and/or coordination with third-party services 114. Alternatively, the task recommendation system 106 may rank the projects and/or tasks based on the level of urgency for completion of each project and/or task. The level of urgency may be determined based on member characteristics (e.g., data corresponding to a member's own prioritization of certain tasks or categories of tasks) and/or potential risks to the member if the project and/or task is not performed. For example, a task corresponding to replacement or installation of carbon monoxide detectors within the member's home may be ranked higher than a task corresponding to the replacement of a refrigerator water dispenser filter, as carbon monoxide filters may be more critical to member safety. As another illustrative example, if a member 110 places significant importance on the maintenance of their vehicle, the task recommendation system 106 may rank a task related to vehicle maintenance higher than a task related to other types of maintenance. See also Cheng at ¶ [0077-0079].);
- selecting, based on the ranking, the replacement task (see at least Matsuoka: ¶ [0050-0051] & ¶ [0077-0079] & ¶ [0117]. Matsuoka teaches that the newly created task or project may be ranked according to a likelihood of the member selecting the task or project for delegation to the representative 104 for performance and coordination with third-party services. Alternatively, the new task or project may be ranked based on the level of urgency for completion of each project or task. See also Matsuoka at ¶ [0041-0042]: “The representative 104, based on their knowledge of the member 110, may select any of the identified one or more projects and/or tasks for presentation to the member 110. In some instances, if the representative 104 selects any of the identified one or more projects and/or tasks, the task recommendation system 106 may provide, via the representative console, one or more task templates that may be used to further define the selected projects and/or tasks. The one or more task templates may correspond to the task type or category for the projects and/or tasks being defined.”).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Kojo / Cheng system for updating a set of tasks for greenhouse gas mitigation with the aforementioned teachings of: ranking the plurality of replacement candidates based on the replacement scores and selecting based on the ranking, the replacement task, and in further view of Matsuoka, in order for the task recommendation system of Matsuoka rank the projects and/or tasks based on the level of urgency for completion of each project and/or task. The level of urgency may be determined based on member characteristics (e.g., data corresponding to a member's own prioritization of certain tasks or categories of tasks) and/or potential risks to the member if the project and/or task is not performed. For example, a task corresponding to replacement or installation of carbon monoxide detectors within the member's home may be ranked higher than a task corresponding to the replacement of a refrigerator water dispenser filter, as carbon monoxide filters may be more critical to member safety (see at least Matsuoka: ¶ [0050].).
Further, the claimed invention is merely a combination of old elements in a similar field for updating a set of tasks for greenhouse gas mitigation and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Matsuoka, the results of the combination were predictable.
Kojo / Cheng / Matsuoka system for updating a set of tasks for greenhouse gas mitigation does not explicitly disclose, but Holt in the analogous art for updating a set of tasks for greenhouse gas mitigation disclose the following:
- generating, an updated set of tasks including the replacement task in place of the task that caused the overall risk score of the of tasks to exceed the first failure threshold, wherein an updated overall risk score of the updated set of tasks satisfies the first failure threshold (see at least Holt: Figs. 3-4 & ¶ [0061-0069] & ¶ [0079]. Holt teaches that the next scheduled action may include a re-scheduling of the instrumentation calibration date (e.g., move date forward or move date backwards), or the creation of a work order for replacement or repair of the instrumentation 26. See also Holt at ¶ [0068]: For instruments 26 that may be more critical to plant operations, the risk threshold of not using the instrument 26 may be exceeded (decision 110). The logic 100 may then decide on any possible mitigation courses of action (decision 116). In certain circumstances, it may be possible to mitigate the risk to plant operations by selecting certain mitigation actions (block 118). For example, if the instrument 26 that has failed is measuring turbine temperatures (e.g., HP turbine 84 or LP turbine 86), then the turbine may be allowed to operate, albeit, at reduced limits. For example, the turbine may be allowed to operate at 95%, 90%, 80%, 50% of maximum load. See also Holt at ¶ [0069]: For example, if the instrumentation 26 that may have become inoperable includes instrumentation 26 required for emission monitoring, then the recommended action may include a recommendation for immediate replacement of the failed instrumentation 26 and an automated action to shut down the plant if the replacement is not completed before the end of a certain time period (e.g., 15 minutes, 1 hour, 4 hours, 1 day). See also Holt at ¶ [0079]: The logic 100 may update the risk projection (block 106) of replacing the equipment 24 and/or instrumentation 26 with newer designs, and compare the replacement risk against any updated risk threshold (block 108). Likewise, the risk of not upgrading the plant 10 resources may be used as a point of comparison. Should the risk threshold of upgrading the equipment not exceed the updated risk threshold (block 110), then a next scheduled action may be calculated (block 112) to include a schedule and list of equipment 24 and/or instrumentation 26 upgrades. In this way, the logic 100 may monitor inputs 28 and 30 so as to derive one or more upgrades to the plant 10 that may increase the plant's efficiency and production. See also Holt at ¶ [0082]. See also Holt at Fig. 3 step 116 noting mitigation available? -> Fig. 3 step 110 noting risk threshold exceeded?.)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Kojo / Cheng / Matsuoka system for updating a set of tasks for greenhouse gas mitigation with the aforementioned teachings of: generating, an updated set of tasks including the replacement ask in place of the task that caused the overall risk score of the set of tasks to exceed the first failure threshold, wherein an updated overall risk score of the updated set of tasks satisfies the first failure threshold, and in further view of Holt, whereby a risk of equipment failure may be calculated by the risk calculation engine based on the dynamic and the static data. The derived risk may then be input into the DSS, and the DSS may then derive operational decisions, such as risk mitigation decisions and recommended actions, that may result in a more efficient plant operation. A method is also provided that may enable a continuous monitoring of the dynamic and the static inputs, so as to update risk projections and/or risk thresholds associated with plant equipment and operations. The risk projections and/or thresholds may then be used to derive actions suitable for improving the use of the equipment and increasing plant reliability and efficiency (see at least Holt: ¶ [0025].).
Further, the claimed invention is merely a combination of old elements in a similar field for updating a set of tasks for greenhouse gas mitigation and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Holt, the results of the combination were predictable.
Regarding Dependent Claim 2, Kojo / Cheng / Matsuoka / Holt method for updating a set of tasks for greenhouse gas mitigation teaches the limitations of Independent Claim 1 above, and Cheng further teaches the method for updating a set of tasks for greenhouse gas mitigation comprising:
- wherein assigning the replacement score for the replacement candidate based on the predicted failure correlation comprises assigning the replacement score based on (i) predictive rates of failure and (ii) a predicted offset potential (see at least Cheng: ¶ [0041-0042] & ¶ [0063] & ¶ [0068] & ¶ [0080]. Cheng teaches that will of course be appreciated that the various factors considered by the example score calculator 122 of FIG. 1 are merely non-limiting examples of the types of criticality score factors that may be utilized by the critical component identifier 120. For example, it may occur that a failed component experiences relatively little downtime in cases in which a temporary replacement component is available. However, cost associated with such replacement components, and/or with other activity required to avoid downtime and maintain operations of the production facility during a repair or replacement of the failed component, may also be quantified and included within the criticality score for the component in question. See also Cheng at ¶ [0063]: “For example, such costs can be associated with a cost of a replacement part, including associated delivery fees and delivery times. As referenced herein, such operational costs can also refer to costs associated with temporary replacement parts that are used until new replacement parts are received, or any other costs related to, or caused by, a particular failure.” See also Cheng at ¶ [0068]: “Infer, deduce, or otherwise obtain at least an approximate replacement value for any such missing data values within the event data 208. As a simplified example, it may occur that the event data 208 includes, for a specific failure, a known failure type 220 associated with a first valve. However, the corresponding failure location 212 may not be known from reported event data.” See also Cheng at ¶ [0080]: There may be a correlation between such failures or other maintenance events, which may or may not rise to a level of actual or direct causality. For example, in the examples provided below in which a Bayesian network is utilized, conditional probabilities associating a failure of a particular component with one or more preceding conditions, including failure of a preceding component, may be characterized. Thus, it may be appreciated that the term causal connection or causality should be understood to include potential or inferred causation, thereby including correlations and probabilities of relationships between failures or other maintenance events.)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Kojo / Cheng / Matsuoka / Holt method for updating a set of tasks for greenhouse gas mitigation with the aforementioned teachings of: wherein assigning the replacement score for the replacement candidate based on the predicted failure correlation comprises assigning the replacement score based on (i) predictive rates of failure and (ii) a predicted offset potential, and in further view of Cheng, whereby specifying component level maintenance activities as part of such maintenance policies, the maintenance policy generator is capable of quantifying and otherwise characterizing relative benefits of potential maintenance policies with respect to actual or potential production losses occurred. For example, the maintenance policy generator may provide a number of different potential maintenance policies, along with associated information regarding corresponding production and production losses, so that a user of the system may select an appropriate, desired maintenance policy. Similarly, the maintenance policy generator may provide an appropriate graphical user interface for such a user to explore various “what-if” scenarios with respect to relative effects of potential changes to the existing maintenance policy, as quantified with respect to associated potential production losses (see at least Cheng: ¶ [0051].). Moreover, a ML algorithm is capable of analyzing historical maintenance data for purposes of enabling predictions of future causal connections between failures of dependent components (see at least Cheng: ¶ [0049].).
Further, the claimed invention is merely a combination of old elements in a similar field for updating a set of tasks for greenhouse gas mitigation and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Cheng, the results of the combination were predictable.
Regarding Dependent Claim 3, Kojo / Cheng / Matsuoka / Holt method for updating a set of tasks for greenhouse gas mitigation teaches the limitations of Independent Claim 1 above, and Matsuoka further teaches the method for updating a set of tasks for greenhouse gas mitigation comprising:
- wherein ranking the plurality of replacement candidates based on the replacement scores further comprises (see at least Matsuoka: Fig. 2 & ¶ [0050-0051] & ¶ [0117].):
- determining, for the task of the state of tasks exceed the second threshold (see at least Matsuoka: ¶ [0137] & ¶ [0156].);
- ranking the replacement candidates based on respective potential of each replacement candidate to repair the mitigation failure value (see at least Matsuoka: ¶ [0040-0041] & ¶ [0050-0051] & ¶ [0117].)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Kojo / Cheng / Matsuoka / Holt method for updating a set of tasks for greenhouse gas mitigation with the aforementioned teachings of: wherein ranking the plurality of replacement candidates based on the replacement scores further comprise: determining, for the task of the set of tasks exceeding the second threshold, a mitigation failure value and ranking the replacement candidates based on respective potential of each replacement candidate to repair the mitigation failure value, and in further view of Matsuoka, in order for the task recommendation system of Matsuoka rank the projects and/or tasks based on the level of urgency for completion of each project and/or task. The level of urgency may be determined based on member characteristics (e.g., data corresponding to a member's own prioritization of certain tasks or categories of tasks) and/or potential risks to the member if the project and/or task is not performed. For example, a task corresponding to replacement or installation of carbon monoxide detectors within the member's home may be ranked higher than a task corresponding to the replacement of a refrigerator water dispenser filter, as carbon monoxide filters may be more critical to member safety (see at least Matsuoka: ¶ [0050].).
Further, the claimed invention is merely a combination of old elements in a similar field for updating a set of tasks for greenhouse gas mitigation and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Matsuoka, the results of the combination were predictable.
Regarding Dependent Claim 4, Kojo / Cheng / Matsuoka / Holt method for updating a set of tasks for greenhouse gas mitigation teaches the limitations of Independent Claim 1 above, and Matsuoka further teaches the method for updating a set of tasks for greenhouse gas mitigation comprising:
- wherein ranking the plurality of replacement candidates further comprises (see at least Matsuoka: Fig. 2 & ¶ [0050-0051] & ¶ [0117].) ranking the replacement candidates based on real-time data collected from similar mitigation projects (see at least Matsuoka: ¶ [0050-0051] & ¶ [0157] & ¶ [0162]. Matsuoka notes that the machine learning algorithm or artificial intelligence may be used to determine or recommend what information should be presented to the member and to similarly-situated members for similar projects and tasks or types of projects and tasks. See also Matsuoka at ¶ [0088]: “The task creation machine learning module 302 may revise the classification or clustering algorithm to decrease the likelihood of this task template 306 being selected for similar project/task categories or types. Further, if the representative 104 manually selects an alternative task template for the identified issue expressed by the member 110, the task creation machine learning module 302 may use this selection to further revise the classification or clustering algorithm to increase the likelihood of the algorithm selecting this particular task template for similar projects and tasks.” See also Matsuoka at ¶ [0117].).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Kojo / Cheng / Matsuoka / Holt method for updating a set of tasks for greenhouse gas mitigation with the aforementioned teachings of: wherein ranking the plurality of replacement candidates further comprises ranking the replacement candidates based on real-time data collected from similar mitigation projects, and in further view of Matsuoka, in order for the task recommendation system of Matsuoka rank the projects and/or tasks based on the level of urgency for completion of each project and/or task. The level of urgency may be determined based on member characteristics (e.g., data corresponding to a member's own prioritization of certain tasks or categories of tasks) and/or potential risks to the member if the project and/or task is not performed. For example, a task corresponding to replacement or installation of carbon monoxide detectors within the member's home may be ranked higher than a task corresponding to the replacement of a refrigerator water dispenser filter, as carbon monoxide filters may be more critical to member safety (see at least Matsuoka: ¶ [0050].).
Further, the claimed invention is merely a combination of old elements in a similar field for updating a set of tasks for greenhouse gas mitigation and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Matsuoka, the results of the combination were predictable.
Regarding Dependent Claim 5, Kojo / Cheng / Matsuoka / Holt method for updating a set of tasks for greenhouse gas mitigation teaches the limitations of Independent Claim 1 above, and Kojo further teaches the method for updating a set of tasks for greenhouse gas mitigation comprising:
- wherein determining, by the machine learning model (see at least Kojo: ¶ [0034] & ¶ [0106] & ¶ [0164]. Kojo notes that to increase the optimal performance of the Optimization Logic described above, a method for using machine learning to modify the parameters of optimization logic is described. See also Kojo at ¶ [0034]: The engine within the Platform that, as described in this document, employs artificial intelligence and/or machine learning to carry out CME Transactions, and to report the results of the transactions as well as to receive data and feedback on the successfulness of the transactions and other information in order to adjust and optimize certain data in the Platform and its usage of CIM Allocation Rules and User Preferences in future transactions. See also Kojo at ¶ [0106]: This process may be performed or assisted with computer methods including artificial intelligence and/or machine learning.) and based on multiple data types from the plurality of sources (see at least Kojo: ¶ [0022] & ¶ [0065] & ¶ [0092]. Kojo teaches that the emissions calculation engine 120 may estimate the carbon footprints of Items on the basis of the items' characteristics or qualities by using categorical Emission Values or Emission Values of similar Items, or may receive the Emission Values of Items from other sources, resulting in emission value estimates 122, 124, and 126. See also Kojo at ¶ [0065]: These data may have been received directly from the Item suppliers (e.g. product manufacturers or service providers), from this or other Users, from research or from other sources (e.g. public or commercial climate impact indices). If no specific EV data exist on a certain Item the engine may apply EV estimates made on the basis of the characteristics of the Item or categorical types thereof. These estimates may have been received from external CO2e emission indices, from research or from other sources. See also Kojo at ¶ [0092]: The Carbon Market Engine 130 may also report to the User(s) about the sources and amounts of CIM Units allocated and the MCCs (and/or fractions thereof) issued and assigned to the User Order(s) via reporting module 144. “See also Tables 1-9 of Kojo noting multiple data types.”), that the overall risk score of the set of tasks exceeds the first failure threshold due to the risk score of the task of the set of tasks exceeding the second threshold (see at least Kojo: (Dependent Claims 4 and 8-9) & (Tables 1-9 of Kojo). Kojo teaches that the plurality of factors comprising an offset value, an integrity score, an impact factor, and a failure factor, the plurality of factors being satisfied when each value of the new CIM record for the plurality of factors satisfies a predetermined threshold value. See also Dependent Claim 8 of Kojo: Re-selecting CIM records to generate a second set of CIM records when the scaled objective value of the first set of CIM records is less than a predetermined threshold, the second set of CIM records being the selected CIM records when a scaled objective value of the second set of CIM records is greater than the predetermined threshold. See also Dependent Claim 9 of Kojo: Comparing the scaled objective value of the second set of CIM records to historical objective values of similar CIM record groups, the trained model being re-run when the scaled objective value of the second set of CIM records is more than a predetermined threshold less than the historical objective values of similar CIM record groups. See also Tables 1-9 of Kojo noting overall risk scores.) comprises:
- predicting, by the machine learning model (see at least Kojo: ¶ [0034] & ¶ [0106] & ¶ [0164]. Kojo notes that to increase the optimal performance of the Optimization Logic described above, a method for using machine learning to modify the parameters of optimization logic is described. See also Kojo at ¶ [0034]: The engine within the Platform that, as described in this document, employs artificial intelligence and/or machine learning to carry out CME Transactions, and to report the results of the transactions as well as to receive data and feedback on the successfulness of the transactions and other information in order to adjust and optimize certain data in the Platform and its usage of CIM Allocation Rules and User Preferences in future transactions. See also Kojo at ¶ [0106]: This process may be performed or assisted with computer methods including artificial intelligence and/or machine learning.) and based on real-time data collected for the task based on the multiple data types from the plurality of sources (see at least Kojo: ¶ [0022] & ¶ [0065] & ¶ [0092]. Kojo teaches that the emissions calculation engine 120 may estimate the carbon footprints of Items on the basis of the items' characteristics or qualities by using categorical Emission Values or Emission Values of similar Items, or may receive the Emission Values of Items from other sources, resulting in emission value estimates 122, 124, and 126. See also Kojo at ¶ [0065]: These data may have been received directly from the Item suppliers (e.g. product manufacturers or service providers), from this or other Users, from research or from other sources (e.g. public or commercial climate impact indices). If no specific EV data exist on a certain Item the engine may apply EV estimates made on the basis of the characteristics of the Item or categorical types thereof. These estimates may have been received from external CO2e emission indices, from research or from other sources. See also Kojo at ¶ [0092]: The Carbon Market Engine 130 may also report to the User(s) about the sources and amounts of CIM Units allocated and the MCCs (and/or fractions thereof) issued and assigned to the User Order(s) via reporting module 144. “See also Tables 1-9 of Kojo noting multiple data types.”), that future variations of a mitigation for the task are below a threshold mitigation (see at least Kojo: ¶ [0034] & ¶ [0166-0168] & (Dependent Claims 4 and 8-9) & (Tables 1-9 of Kojo). Kojo teaches that the results of the transactions as well as to receive data and feedback on the successfulness of the transactions and other information in order to adjust and optimize certain data in the Platform and its usage of CIM Allocation Rules and User Preferences in future transactions. See also Kojo at ¶ [0052] noting Offset (CO2e emissions): the act of purchasing and retiring Carbon Credits with the effect of removing or sequestering CO2 from the atmosphere or preventing the emission of said gases into the atmosphere, so as to make good the emission of CO2e into the atmosphere that has taken place or will take place in the future. See also Kojo at ¶ [0166-0168]: If an adjusted set of parameters is found to be superior to the original parameters, the CME may take these into use in future CME Transactions to increase the likelihood of maximizing the aggregate ObV of OV Batches over time.)
Regarding Dependent Claim 6, Kojo / Cheng / Matsuoka / Holt method for updating a set of tasks for greenhouse gas mitigation teaches the limitations of Independent Claim 1 above, and Kojo further teaches the method for updating a set of tasks for greenhouse gas mitigation comprising:
- determining, by the machine learning model (see at least Kojo: ¶ [0034] & ¶ [0106] & ¶ [0164]. Kojo notes that to increase the optimal performance of the Optimization Logic described above, a method for using machine learning to modify the parameters of optimization logic is described. See also Kojo at ¶ [0034]: The engine within the Platform that, as described in this document, employs artificial intelligence and/or machine learning to carry out CME Transactions, and to report the results of the transactions as well as to receive data and feedback on the successfulness of the transactions and other information in order to adjust and optimize certain data in the Platform and its usage of CIM Allocation Rules and User Preferences in future transactions. See also Kojo at ¶ [0106]: This process may be performed or assisted with computer methods including artificial intelligence and/or machine learning.) and for a failure scenario, an impact on mitigation outcomes for respective failure mechanisms of each of the tasks of the set (see at least Kojo: ¶ [0180-0186] & (Tables 1-9).);
- predicting, based on aggregated impacts across all the tasks of the set, a total impact of the scenario on the set of tasks (see at least Kojo: ¶ [0071-0073] & ¶ [0180-0186] & (Tables 1-9). Kojo teaches that the evaluators may score the CIMs using a scoring system developed by the operator. The scores received by each CIM reflect its scientifically approved, measurable and verifiable impact on the climate.);
- using the total impact (see at least Kojo: (Tables 1-9 of Kojo) & ¶ [0071-0073] & ¶ [0110-0115]. Kojo notes that Impact Factors: “The weighted average Social & Biodiversity Impact Factor of the whole OV batch should be at least z”. The CARs 162 may be defined and refined from time to time by the Platform's internal experts using their scientific expertise in accordance with the purchase policies and targets set forth by the operator. See also Kojo at ¶ [0071-0073]: Community Impact Factor (positive effects in the community carrying out the CIM with indirect effect on the climate).), determining the overall risk score (see at least Kojo: ¶ [0076] & ¶ [0196] & ¶ [0201]. Kojo notes that Yet another CAR could be that no transaction may contain more than 5% CIM Units from CIMs with a Failure Risk Factor of over x. See also Kojo at ¶ [0185]: CAR3 737: the CAR may require allocation of at most 5% of aggregate OV from CIMs with failure_risk>14, therefore no more than 35 OV of the batch may be allocated to CIMs having a failure risk parameter greater than 14. See also Tables 1-5 of Kojo noting failure risk mechanisms or failure risk conditions.)
Regarding Dependent Claim 7, Kojo / Cheng / Matsuoka / Holt method for updating a set of tasks for greenhouse gas mitigation teaches the limitations of Independent Claim 1 above, and Kojo further teaches the method for updating a set of tasks for greenhouse gas mitigation comprising:
- further comprising training the machine learning model (see at least Kojo: ¶ [0063] & ¶ [0106], ¶ [0164] & ¶ [0167-0168]. Kojo notes that FIG. 2 shows a flow diagram for a specific embodiment of a method 200 of allocating CIMs using a trained model. See also Kojo at ¶ [0069-0070]: Kojo teaches that a trained model may then be applied to the aggregated plurality of requests at step 230 to select CIM records from a database and amounts associated with each selected CIM record. The trained model may select CIM records based on the CIM allocation rules and the batch emissions value, and may select CIM records by optimizing an offset value of each selected CIM record in view of a cost associated with each selected CIM record. See also Kojo noting a “trained model” at ¶ [0163], ¶ [0195], ¶ [0200-0201], ¶ [0210]. See also Kojo noting “machine-learning” or “machine learning algorithm” at ¶ [0034], ¶ [0106], ¶ [0164] & ¶ [0167-0168].) comprising:
- receiving training data (see at least Kojo: ¶ [0063] & ¶ [0106], ¶ [0164] & ¶ [0167-0168]. Kojo notes that FIG. 2 shows a flow diagram for a specific embodiment of a method 200 of allocating CIMs using a trained model. See also Kojo at ¶ [0069-0070]: Kojo teaches that a trained model may then be applied to the aggregated plurality of requests at step 230 to select CIM records from a database and amounts associated with each selected CIM record. The trained model may select CIM records based on the CIM allocation rules and the batch emissions value, and may select CIM records by optimizing an offset value of each selected CIM record in view of a cost associated with each selected CIM record.), from the plurality of sources and including multiple data types (see at least Kojo: ¶ [0022] & ¶ [0065] & ¶ [0092]. Kojo teaches that the emissions calculation engine 120 may estimate the carbon footprints of Items on the basis of the items' characteristics or qualities by using categorical Emission Values or Emission Values of similar Items, or may receive the Emission Values of Items from other sources, resulting in emission value estimates 122, 124, and 126. See also Kojo at ¶ [0065]: These data may have been received directly from the Item suppliers (e.g. product manufacturers or service providers), from this or other Users, from research or from other sources (e.g. public or commercial climate impact indices). If no specific EV data exist on a certain Item the engine may apply EV estimates made on the basis of the characteristics of the Item or categorical types thereof. These estimates may have been received from external CO2e emission indices, from research or from other sources. See also Kojo at ¶ [0092]: The Carbon Market Engine 130 may also report to the User(s) about the sources and amounts of CIM Units allocated and the MCCs (and/or fractions thereof) issued and assigned to the User Order(s) via reporting module 144. “See also Tables 1-9 of Kojo noting multiple data types.”), data representative of a plurality of tasks (see at least Kojo: (Dependent Claims 5-6 of Kojo) & ¶ [0024] & (Tables 1-9 of Kojo). Kojo teaches that the platform 100 can be accessed and used to buy Meta Carbon Credits (and/or fractions thereof) 142 to directly offset CO2e emissions arising from any items that may be relevant to the user's business, consumption or various operations and activities. See at least Dependent Claims 5-6 of Kojo: Each request including one or more of a list of activities or an emissions value to offset, or an amount of meta carbon credits to purchase. “See also Tables 1-9 of Kojo.”);
- providing, to the machine learning model (see at least Kojo: ¶ [0034], ¶ [0106], ¶ [0164] & ¶ [0167-0168].), the training data (see at least Kojo: ¶ [0063] & ¶ [0106], ¶ [0164] & ¶ [0167-0168]. Kojo notes that FIG. 2 shows a flow diagram for a specific embodiment of a method 200 of allocating CIMs using a trained model. See also Kojo at ¶ [0069-0070]: Kojo teaches that a trained model may then be applied to the aggregated plurality of requests at step 230 to select CIM records from a database and amounts associated with each selected CIM record. The trained model may select CIM records based on the CIM allocation rules and the batch emissions value, and may select CIM records by optimizing an offset value of each selected CIM record in view of a cost associated with each selected CIM record.);
- wherein the training data representative of each task (see at least Kojo: ¶ [0063] & ¶ [0106], ¶ [0164] & ¶ [0167-0168].) includes (i) rates of failure (see at least Kojo: ¶ [0076] & ¶ [0196] & ¶ [0201]. Kojo notes that Yet another CAR could be that no transaction may contain more than 5% CIM Units from CIMs with a Failure Risk Factor of over x. See also Kojo at ¶ [0185]: CAR3 737: the CAR may require allocation of at most 5% of aggregate OV from CIMs with failure_risk>14, therefore no more than 35 OV of the batch may be allocated to CIMs having a failure risk parameter greater than 14. See also Tables 1-5 of Kojo noting failure risk mechanisms or failure risk conditions.), (ii) offset results (see at least Kojo: ¶ [0195]. Kojo notes that the ROM 136 may use CIM evaluation rules to identify potential CIM records to offset the identified items in the batch at block 745, resulting in tentative batch 747. See at least Kojo: ¶ [0054-0056] & ¶ [0069] & ¶ [0126] & ¶ [0132-0136]. Kojo notes that a trained model may then be applied to the aggregated plurality of requests at step 230 to select CIM records from a database and amounts associated with each selected CIM record. The trained model may select CIM records based on the CIM allocation rules and the batch emissions value, and may select CIM records by optimizing an offset value of each selected CIM record in view of a cost associated with each selected CIM record. See at least Kojo at ¶ [0054-0056]: Sequestering 1000 kg of CO2 from the atmosphere through a specific CIM costs $20. The CIM has been assigned an OV of 860, which means that the Platform's internal experts have evaluated one Carbon Credit from that CIM to sequester in reality 860 kg of CO2e in the atmosphere (even though it is being marketed by the CIM Supplier as doing so at 1000 kg). The CIM is split up into 1000 CIM Units, each CIM Unit representing 1/1000 CIM and costing $20/1000=$0.02. The OV of a CIM Unit is OV=860/1000=0.86. To Offset the EV of the television set mentioned above (EV=500), 581 CIM Units are needed (500 EV/0.86 OV≈581). In this case, the Offsetting costs $ 0.02*581=$11.62. See at least Kojo at ¶ [0126]: User Orders from the Emission Calculations Engine and to transform them into an EV Batch 134, to select and allocate CIM Units from the CIM Pool 170 within received constraints, to compile an OV Batch 140 to match the EV Batch 134 and to issue and assign a corresponding amount of MCCs (and/or fractions thereof) 142 to realize the Offsetting, other climate action or the creation of MCCs for another purchase as requested in the User Orders included in the applicable CME Transaction. See at least Kojo at ¶ [0132-0136]: noting CIM 1: CIM1: type=“removal”; method=“reforestation”; region=“Asia-Pacific”; price=$10; OV=860 (For details on how the OV of a CIM is determined, see the definition of “Offset Value (OV)” above. CIM2: type=“removal”; method=“mechanical capture”; region=“Europe”; price=$28; OV=950; CIM3: type=“removal”; method=“mechanical capture”; region=“North America”; $29; OV=1100. The penalty factors will be applied to the Offset Value of the OV Batch during a single or multiple optimization runs (as may be set in the Optimization Run Rules).), (iii) correlation strength to one or more other tasks (see at least Kojo: ¶ [0076] & ¶ [0180-0186] &¶ [0219-0220]. Kojo teaches that CAR3 737: the CAR may require allocation of at most 5% of aggregate OV from CIMs with failure_risk>14, therefore no more than 35 OV of the batch may be allocated to CIMs having a failure risk parameter greater than 14. See also Kojo at ¶ [0076]: One CAR could be for example that no transaction may include more than 20% of CIM Units received from a single CIM. Another CAR could be that each CME Transaction must include CIM Units from at least two CIMs located in different continents. Yet another CAR could be that no transaction may contain more than 5% CIM Units from CIMs with a Failure Risk Factor of over x. See also Kojo at ¶ [0199]: CAR1. allocate min 75% from type=“reforestation”; P(CAR1) penalty=−−0.10. See also Kojo at ¶ [0208]: CAR 2: allocate from min 2 regions (max 90% each); P(CAR2): penalty=−0.20. See also Kojo at ¶ [0214-0216]: CAR3: allocate max 5% from failure_risk>14; P(CAR3): penalty=−0.70. The engine cannot mark CAR3 as completed as the final allocation does not in itself ensure that a maximum of 5% of the OV in the batch comes from CIMs with failure_risk of 15 or higher. See also Kojo at ¶ [0219-0220]: CAR3: allocate max 5% from failure_risk>14; P(CAR3): penalty=−0.70. The ROM 136 finds that only 47.57% of the OV in the batch comes from CIMs with a failure_risk of 14 or less whereas the target set by CAR3 is 95%. See also Tables 1-9 of Kojo noting multiple replacement scores and failure correlation of the replacement candidates with respect to each other sets of the set of tasks.)
Regarding Dependent Claim 10, Kojo / Cheng / Matsuoka / Holt method for updating a set of tasks for greenhouse gas mitigation teaches the limitations of Independent Claim 1 above, and Kojo further teaches the method for updating a set of tasks for greenhouse gas mitigation comprising:
- determining, for one or more of the tasks of the set of tasks (see at least Kojo: (Dependent Claims 5-6 of Kojo) & ¶ [0024]. Kojo teaches that the platform 100 can be accessed and used to buy Meta Carbon Credits (and/or fractions thereof) 142 to directly offset CO2e emissions arising from any items that may be relevant to the user's business, consumption or various operations and activities. See at least Dependent Claims 5-6 of Kojo: Each request including one or more of a list of activities or an emissions value to offset, or an amount of meta carbon credits to purchase.), a permanence action supportive of the task, the permanence action counteracting at least one of the one or more failure mechanisms (see at least Kojo: (Tables 1-9) & ¶ [0080-0083]. Kojo teaches that CIMs using reforestation and/or forest protection as a method typically have a lower price per tCO2, but they are usually marked with higher uncertainty (due to e.g. forest fires and other natural disasters) and lower permanence (due to e.g. using living trees in a partially controlled environment as carbon storage). In addition, such CIMs typically score lower on verification due to the difficulty of independently verifying the planting and long duration of growing trees. Therefore, the OV of such CIMs is normally lower. CIMs using mechanical carbon capture as a method, on the other hand, often have a higher price per tCO2 but also better permanence and reliability. See also Kojo at ¶ [0119-0123]: Kojo teaches that the general categorization of a CIM, for example avoidance (e.g. a cooking stove or a forest protection project) or removal (e.g. reforestation or mechanical capture project. CIM method: may be a specific methodology or a more general categorization. An example of the general category is nature-based projects, which may include reforestation and forest protection. See also Kojo at ¶ [0133-0138]: CIM1: type=“removal”; method=“reforestation”; region=“Asia-Pacific”; price=$10; OV=860 (For details on how the OV of a CIM is determined, see the definition of “Offset Value (OV)” above.) CIM2: type=“removal”; method=“mechanical capture”; region=“Europe”; price=$28; OV=950 CIM3: type=“removal”; method=“mechanical capture”; region=“North America”; $29; OV=1100.);
- generating, the updated set of tasks including the permanence action (see at least Kojo: (Tables 1-9) & ¶ [0080-0083] & ¶ [0119-0123]. Kojo teaches that CIMs using reforestation and/or forest protection as a method typically have a lower price per tCO2, but they are usually marked with higher uncertainty (due to e.g. forest fires and other natural disasters) and lower permanence (due to e.g. using living trees in a partially controlled environment as carbon storage). In addition, such CIMs typically score lower on verification due to the difficulty of independently verifying the planting and long duration of growing trees. Therefore, the OV of such CIMs is normally lower. CIMs using mechanical carbon capture as a method, on the other hand, often have a higher price per tCO2 but also better permanence and reliability. See also Kojo at ¶ [0119-0123]: Kojo teaches that the general categorization of a CIM, for example avoidance (e.g. a cooking stove or a forest protection project) or removal (e.g. reforestation or mechanical capture project. CIM method: may be a specific methodology or a more general categorization. An example of the general category is nature-based projects, which may include reforestation and forest protection.)
Regarding Dependent Claim 11, Kojo / Cheng / Matsuoka / Holt method for updating a set of tasks for greenhouse gas mitigation teaches the limitations of Claims 1 and 10 above, and Kojo further teaches the method for updating a set of tasks for greenhouse gas mitigation comprising:
- further comprising determining that the selected replacement task has a first failure mechanism (see at least Kojo: (Tables 1-9) & ¶ [0156] & ¶ [0180-0194]. Kojo teaches that the Ranking and Optimization Module 136 selects a set of CIM Units from applicable CIMs in such a way that the effect (ObV-adjusted) aggregate OV of the OV Batch is maximized, under the constraint that the amount of available funds (as allocated by the User Orders in the CME Transaction) must not be exceeded. This formulation means that the problem is one of constrained optimization, which means that any algorithms for solving such problems can be utilized. See also Kojo at ¶ [0180-0194]: Aggregate schema 739 may organize the CARs and user preference rules from 710 to facilitate selection of the CIMs for the batch, and may be applied sequentially by the Ranking and Organization Module (ROM) 136. For example, the aggregate schema 739 may organize the rules in the following manner: CAR.1: allocate 375 OV from type “reforestation;” [to be met first] UP1: prefer 200 OV from region “Asia-Pacific;” UP2: prefer 50 EV from region “Africa.” CAR.2: allocate ≥50 OV from at least two regions; [to be met second] UP3: if OV (region “Asia-Pacific”)<200, prefer 200 OV−OV (region “Asia-Pacific”) from region “Asia-Pacific;” UP4: if OV (region “Africa”)<50, prefer 50 OV−OV (region “Africa”) from region “Africa;” CAR.3: allocate ≤25 OV from failure_risk>14; UP5: if OV (method “technology”)<125, prefer 125 OV−OV (method “technology”) from method “technology.”The aggregate schema 739 may be passed from control module 132 to the ROM 136 for use in the CIM selection.), wherein the permanence action has a second failure mechanism different from the first failure mechanism (see at least Kojo: (Tables 1-9) & ¶ [0080-0083] & ¶ [0119-0123]. Kojo teaches that CIMs using reforestation and/or forest protection as a method typically have a lower price per tCO2, but they are usually marked with higher uncertainty (due to e.g. forest fires and other natural disasters) and lower permanence (due to e.g. using living trees in a partially controlled environment as carbon storage). In addition, such CIMs typically score lower on verification due to the difficulty of independently verifying the planting and long duration of growing trees. Therefore, the OV of such CIMs is normally lower. CIMs using mechanical carbon capture as a method, on the other hand, often have a higher price per tCO2 but also better permanence and reliability. See also Kojo at ¶ [0119-0123]: Kojo teaches that the general categorization of a CIM, for example avoidance (e.g. a cooking stove or a forest protection project) or removal (e.g. reforestation or mechanical capture project. CIM method: may be a specific methodology or a more general categorization. An example of the general category is nature-based projects, which may include reforestation and forest protection.)
Regarding Dependent Claim 12, Kojo / Cheng / Matsuoka / Holt method for updating a set of tasks for greenhouse gas mitigation teaches the limitations of Claims 1 and 10 above, and Kojo further teaches the method for updating a set of tasks for greenhouse gas mitigation comprising:
- wherein the permanence action (see at least Kojo: ¶ [0080-0083].) comprises generating, in a market ecosystem (see at least Kojo: ¶ [0020] & Figs. 6-7 noting carbon market engine.), an incentive supportive of one or more of the tasks, wherein the incentive reduces a probability of the at least one of the one or more failure mechanisms (see at least Kojo: ¶ [0091] & [0110-0115] & ¶ [0180-0185]. See also Tables 1-9 of Kojo.).
Regarding Dependent Claim 13, Kojo / Cheng / Matsuoka / Holt method for updating a set of tasks for greenhouse gas mitigation teaches the limitations of Claims 1 and 10 above, and Kojo further teaches the method for updating a set of tasks for greenhouse gas mitigation comprising:
- wherein determining the permanence action (see at least Kojo: ¶ [0080-0083].) comprises determining, for a set of two or more tasks of the set (see at least Kojo: ¶ [0180-0186] & (Tables 1-9).), that the permanence action counteracts the respective failure mechanisms of the set of two or more tasks (see at least Kojo: (Tables 1-9) & ¶ [0080-0083] & ¶ [0119-0123]. Kojo teaches that CIMs using reforestation and/or forest protection as a method typically have a lower price per tCO2, but they are usually marked with higher uncertainty (due to e.g. forest fires and other natural disasters) and lower permanence (due to e.g. using living trees in a partially controlled environment as carbon storage). In addition, such CIMs typically score lower on verification due to the difficulty of independently verifying the planting and long duration of growing trees. Therefore, the OV of such CIMs is normally lower. CIMs using mechanical carbon capture as a method, on the other hand, often have a higher price per tCO2 but also better permanence and reliability. See also Kojo at ¶ [0119-0123]: Kojo teaches that the general categorization of a CIM, for example avoidance (e.g. a cooking stove or a forest protection project) or removal (e.g. reforestation or mechanical capture project. CIM method: may be a specific methodology or a more general categorization. An example of the general category is nature-based projects, which may include reforestation and forest protection.)
Regarding Dependent Claim 15, Kojo / Cheng / Matsuoka / Holt method for updating a set of tasks for greenhouse gas mitigation teaches the limitations of Independent Claim 1 above, and Kojo further teaches the method for updating a set of tasks for greenhouse gas mitigation comprising:
- receiving new input data including data indicating (see at least Kojo: ¶ [0079] & ¶ [0128] & ¶ [0162]. Kojo notes that if so, the Acceptance Module 138 accepts TB1 646 and passes it on as the Final Batch 650 to satisfy the corresponding EV Batch and to conclude the CME Transaction. If not, the Acceptance Module 138 returns TB1 646 to the Ranking and Optimization Module 136 for additional optimization runs, the number of such additional runs as set in the Optimization Run Rules. In such case the Ranking and Optimization Module 136 will create a new (tentative) OV Batch (TB2) 648 and re-submit it to the Acceptance Module 138.) at least one of the offset potential, the failure mechanism, and the risk score of an associated task is incorrect (see at least Kojo: ¶ [0058] & ¶ [0079] & ¶ [0132] & ¶ [0216].)
- determining updated values for (see at least Kojo: ¶ [0075] & ¶ [0107] & ¶ [0128]. The Platform may include a mechanism for updating information about CIMs. The Internal Factors of any CIM may be updated by the experts from time to time. The internal experts 160 may also review CIM records, such as CIM 1 472, CIM 3, 474, and CIM n 476 included in the CIM database 170 from time to time, and once the criteria for inclusion are no longer met, may remove the CIM record
from the database 170. As shown in system 400, each CIM record in the CIM database 470 may include its internal and external factors as identified by internal experts 160.) the at least one of the offset potential, the failure mechanism, and the risk score of the associated task that is indicated to be incorrect (see at least Kojo: ¶ [0058] & ¶ [0079] & ¶ [0132] & ¶ [0216].)
- updating the set with the updated values for (see at least Kojo: ¶ [0075] & ¶ [0107] & ¶ [0128]. The Platform may include a mechanism for updating information about CIMs. The Internal Factors of any CIM may be updated by the experts from time to time. The internal experts 160 may also review CIM records, such as CIM 1 472, CIM 3, 474, and CIM n 476 included in the CIM database 170 from time to time, and once the criteria for inclusion are no longer met, may remove the CIM record from the database 170. As shown in system 400, each CIM record in the CIM database 470 may include its internal and external factors as identified by internal experts 160.) the at least one of the offset potential, the failure mechanism, and the risk score of the associated task that is indicated to be incorrect (see at least Kojo: ¶ [0075] & ¶ [0107] & ¶ [0128]. The Platform may include a mechanism for updating information about CIMs. The Internal Factors of any CIM may be updated by the experts from time to time. The internal experts 160 may also review CIM records, such as CIM 1 472, CIM 3, 474, and CIM n 476 included in the CIM database 170 from time to time, and once the criteria for inclusion are no longer met, may remove the CIM record
from the database 170. As shown in system 400, each CIM record in the CIM database 470 may include its internal and external factors as identified by internal experts 160.)
Regarding Dependent Claim 16, Kojo / Cheng / Matsuoka / Holt method for updating a set of tasks for greenhouse gas mitigation teaches the limitations of Claims 1 and 15 above, and Kojo further teaches the method for updating a set of tasks for greenhouse gas mitigation comprising:
- in response to updating the set with the updated values (see at least Kojo: ¶ [0075] & ¶ [0107] & ¶ [0128]. The Platform may include a mechanism for updating information about CIMs. The Internal Factors of any CIM may be updated by the experts from time to time. The internal experts 160 may also review CIM records, such as CIM 1 472, CIM 3, 474, and CIM n 476 included in the CIM database 170 from time to time, and once the criteria for inclusion are no longer met, may remove the CIM record from the database 170. As shown in system 400, each CIM record in the CIM database 470 may include its internal and external factors as identified by internal experts 160.), determining that the overall risk of the set exceeds the failure threshold (see at least Kojo: (Dependent Claims 4 and 8-9 of Kojo) & (Tables 1-9 of Kojo).);
- in response to determining that the overall risk of the set exceeds the failure threshold (see at least Kojo: (Dependent Claims 4 and 8-9 of Kojo) & (Tables 1-9 of Kojo).), selecting another replacement task for the set (see at least Kojo: ¶ [0156] & ¶ [0180-0194]. Kojo teaches that the Ranking and Optimization Module 136 selects a set of CIM Units from applicable CIMs in such a way that the effect (ObV-adjusted) aggregate OV of the OV Batch is maximized, under the constraint that the amount of available funds (as allocated by the User Orders in the CME Transaction) must not be exceeded. This formulation means that the problem is one of constrained optimization, which means that any algorithms for solving such problems can be utilized. See also Kojo at ¶ [0180-0194]: Aggregate schema 739 may organize the CARs and user preference rules from 710 to facilitate selection of the CIMs for the batch, and may be applied sequentially by the Ranking and Organization Module (ROM) 136. For example, the aggregate schema 739 may organize the rules in the following manner: CAR.1: allocate 375 OV from type “reforestation;” [to be met first] UP1: prefer 200 OV from region “Asia-Pacific;” UP2: prefer 50 EV from region “Africa.” CAR.2: allocate ≥50 OV from at least two regions; [to be met second] UP3: if OV (region “Asia-Pacific”)<200, prefer 200 OV−OV (region “Asia-Pacific”) from region “Asia-Pacific;” UP4: if OV (region “Africa”)<50, prefer 50 OV−OV (region “Africa”) from region “Africa;” CAR.3: allocate ≤25 OV from failure_risk>14; UP5: if OV (method “technology”)<125, prefer 125 OV−OV (method “technology”) from method “technology.”The aggregate schema 739 may be passed from control module 132 to the ROM 136 for use in the CIM selection.)
Regarding Dependent Claim 17, Kojo / Cheng / Matsuoka / Holt method for updating a set of tasks for greenhouse gas mitigation teaches the limitations of Claims 1 and 15 above, and Kojo further teaches the method for updating a set of tasks for greenhouse gas mitigation comprising:
- wherein the new input data (see at least Kojo: ¶ [0079] & ¶ [0128] & ¶ [0162]. Kojo notes that if so, the Acceptance Module 138 accepts TB1 646 and passes it on as the Final Batch 650 to satisfy the corresponding EV Batch and to conclude the CME Transaction. If not, the Acceptance Module 138 returns TB1 646 to the Ranking and Optimization Module 136 for additional optimization runs, the number of such additional runs as set in the Optimization Run Rules. In such case the Ranking and Optimization Module 136 will create a new (tentative) OV Batch (TB2) 648 and re-submit it to the Acceptance Module 138.) includes data regarding ecological conditions related to the failure mechanisms associated with respective tasks (see at least Kojo: ¶ [0054-0055] & ¶ [0080-0083] & (Tables 1-9). Kojo teaches sequestering 1000 kg of CO2 from the atmosphere through a specific CIM costs $20. The CIM has been assigned an OV of 860, which means that the Platform's internal experts have evaluated one Carbon Credit from that CIM to sequester in reality 860 kg of CO2e in the atmosphere (even though it is being marketed by the CIM Supplier as doing so at 1000 kg). The CIM is split up into 1000 CIM Units, each CIM Unit representing 1/1000 CIM and costing $20/1000=$0.02. The OV of a CIM Unit is OV=860/1000=0.86. To Offset the EV of the television set mentioned above (EV=500), 581 CIM Units are needed (500 EV/0.86 OV≈581). In this case, the Offsetting costs $ 0.02*581=$11.62. See also Kojo at ¶ [0080-0083]: CIM1: type=“avoidance”; method=“forest protection”; region=“South America”; climate_integrity_score=82; price=$10.50; OV=670 CIM2: type=“removal” method=“reforestation”; region=“Asia-Pacific”; climate_integrity_score=84; price=$12.50; OV=845 CIM3: type=“removal” method=“mechanical capture”; region=“North America”; climate_integrity_score=95;price=$25.75;OV=1115, CIMs using reforestation and/or forest protection as a method typically have a lower price per tCO2, but they are usually marked with higher uncertainty (due to e.g. forest fires and other natural disasters) and lower permanence (due to e.g. using living trees in a partially controlled environment as carbon storage). Examiner Note: Examiner interprets the “ecological conditions” according to “financial records” associated with respective tasks.)
14. Claims 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub (US 2023/0135611 A1) hereinafter Kojo, et. al., in view of US PG Pub (US 2016/0092808 A1) hereinafter Cheng, et. al., in view of US PG Pub (US 2023/0085225 A1) hereinafter Matsuoka, et. al., in view of US PG Pub (US 2012/00290104 A1) hereinafter Holt, et. al., and in further view of US PG Pub (US 2024/0403776 A1) hereinafter Krishna, et. al.
Regarding Dependent Claim 8, Kojo / Cheng / Matsuoka / Holt method for updating a set of tasks for greenhouse gas mitigation as applied to Claims 1 and 7 above does not explicitly disclose, but Krishna in the analogous art for updating a set of tasks for greenhouse gas mitigation disclose the following:
- wherein providing the training data comprises updating the training data using a transfer learning machine learning model (see at least Krishna: ¶ [0046] & ¶ [0080] & ¶ [0097] & Fig. 9. Krishna teaches that the optimization and recommendation module 314 may also trigger the machine learning model training module 320 to fine-tune large, code-producing models with transfer learning techniques. See also Krishna at ¶ [0046]: The management module can function to manage (e.g., create, read, update, delete, or otherwise access) data associated with the machine learning-based resource prediction and optimization system. See also Krishna at ¶ [0097]: The machine learning model training module 320 can function to train, retrain, and/or refine the models described herein. For example, models can be trained and/or fine-tuned via transfer learning techniques on domain-specific documents and literature on manufacturing, industrial systems, energy management, and sustainability (e.g., equipment manuals, journals, research papers, etc.,) to provide specific, actionable steps to efficiently reduce resource inputs (e.g., energy consumption) and/or resource outputs (e.g., emissions).), and training the machine learning model (see at least Krishna: Fig. 9 & ¶ [0165]. Krishna notes that FIG. 9 depicts a flowchart 900 of an example of a method of training a machine learning model for resource baseline prediction. In step 902, a computing system (e.g., machine learning-based resource prediction and optimization system 102) obtains historical data associated with one or more sets of assets.) comprises using the updated training data to train the machine learning model (see at least Krishna: ¶ [0046] & ¶ [0147-0148] & ¶ [0177] & Fig. 9. Krishna notes that the computing system may generate instructions to adjust the asset using the changed model inputs and/or a modification the changed inputs. For example, the simulation may iterative (e.g., repeat some or all steps) until the threshold value is satisfied. See also Krishna at ¶ [0046]: The management module can function to manage (e.g., create, read, update, delete, or otherwise access) data associated with the machine learning-based resource prediction and optimization system. See also Krishna at ¶ [0177]: Krishna teaches that steps 1104-1110 may be repeated any number of times for any number of follow-up inputs. The optimization and recommendation module processes the follow-up inputs. See also Krishna at Fig. 9 step 910 noting “Retraining, in response to the determination, the one or more particular machine learning models”.).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Kojo / Cheng / Matsuoka / Holt method for updating a set of tasks for greenhouse gas mitigation with the aforementioned teachings of: wherein providing the training data comprises updating the training data using a transfer learning machine learning model, and training the machine learning model comprises using the updated training data to train the machine learning model, and in view of Krishna, whereby the machine learning model training module can function to train, retrain, and/or refine the models described herein. For example, models can be trained and/or fine-tuned via transfer learning techniques on domain-specific documents and literature on manufacturing, industrial systems, energy management, and sustainability (e.g., equipment manuals, journals, research papers, etc.,) to provide specific, actionable steps to efficiently reduce resource inputs (e.g., energy consumption) and/or resource outputs (e.g., emissions). This may be based on an understanding of what modifications can be made to equipment settings to make them operate with reduced emissions (see at least Krishna: ¶ [0097].).
Further, the claimed invention is merely a combination of old elements in a similar field for updating a set of tasks for greenhouse gas mitigation and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Krishna, the results of the combination were predictable.
Regarding Dependent Claim 9, Kojo / Cheng / Matsuoka / Holt / Krishna method for updating a set of tasks for greenhouse gas mitigation teaches the limitations of Claims 1 and 7-8 above, and Krishna further teaches the method for updating a set of tasks for greenhouse gas mitigation comprising:
- wherein the transfer learning machine learning model is configured to (see at least Krishna: ¶ [0080] & ¶ [0097]. Krishna teaches that the optimization and recommendation module 314 may also trigger the machine learning model training module 320 to fine-tune large, code-producing models with transfer learning techniques. See also Krishna at ¶ [0097]: The machine learning model training module 320 can function to train, retrain, and/or refine the models described herein. For example, models can be trained and/or fine-tuned via transfer learning techniques on domain-specific documents and literature on manufacturing, industrial systems, energy management, and sustainability (e.g., equipment manuals, journals, research papers, etc.,) to provide specific, actionable steps to efficiently reduce resource inputs (e.g., energy consumption) and/or resource outputs (e.g., emissions).);
- based on an image associated with one of the tasks (see at least Krishna: ¶ [0085] & ¶ [0124] & ¶ [0214]. Krishna notes that the optimization and recommendation module 314 may be able to use an image (e.g., captured by someone from inside the facility) to identify the assets and look up an identifier for that asset and figure out the equipment serial number the associated body of knowledge that it can use as context for making recommendations associated with that asset. See also Krishna at ¶ [0124]: An input (e.g., request, query) can be input in various natural forms for easy human interaction (e.g., basic text box interface, image processing, voice activation, and/or the like) and processed to rapidly find relevant and responsive information. See also Krishna at ¶ [0214].), determine a set of simulation parameters for a simulation simulating the one of the tasks (see at least Krishna: Fig. 5B & ¶ [0037] & ¶ [0073-0075] & ¶ [0169]. Krishna teaches that the machine learning-based resource prediction and optimization system 102 can predict how particular assets should operate (e.g., the amount of emissions they should produce) given various conditions (e.g., current conditions, historical conditions, simulated conditions. See also Krishna at ¶ [0073-0075]: The optimization and recommendation module 314 may use scenario-based (or, simulation-based) predictions to recommend corrective actions to satisfy various standards protocols. This may be applicable, for example, when the resource of interest (or “target” resource) can be controlled by adjusting setpoints. The artificial intelligence models can be used to simulate what the outputs could be (or could have been) for a different set of setpoints. If the underlying process would operate better with those different setpoints, then they can be recommended to the end-user or system as actionable insights. See also Krishna at ¶ [0169]: The computing system estimates, based on the plurality of training data sets and one or more models, respective model parameters for each respective different model associated with the one or more assets. The hierarchical aggregation module estimates the model parameters. In step 1006, the computing system determines, based on the estimated model parameters, respective time-invariant mapping of quantiles for each respective asset of the one or more assets. See also Krishna at ¶ [0043].);
- provide the set of simulation parameters as a portion of the training data to a machine learning model (see at least Krishna: Fig. 5B & ¶ [0043] & ¶ [0092] & ¶ [0169]. Krishna notes at ¶ [0086] that the optimization and recommendation module 314 may use scenario-based (or, simulation-based) predictions to recommend corrective actions to satisfy various standards protocols. The artificial intelligence traceability module 316 can indicate portions of data used to generate outputs and their respective data sources. The artificial intelligence traceability module 316 can also function to corroborate model outputs. See also Krishna at ¶ [0043]: The machine learning-based resource prediction and optimization system 102 obtains training data specific to different assets (e.g., equipment) of a facility in steps 202-1 to 202-N. In steps 204-1 to 204-N, the machine learning-based resource prediction and optimization system 102 estimates model parameters from the training data. The machine learning-based resource prediction and optimization system 102 aggregates the model parameters accounting for equipment correlation to estimate the facility model parameters from the training data in step 212. See also Krishna at ¶ [0169] & Figs. 10A-10B: In step 1004, the computing system estimates, based on the plurality of training data sets and one or more models, respective model parameters for each respective different model associated with the one or more assets. The hierarchical aggregation module estimates the model parameters.)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Kojo / Cheng / Matsuoka / Holt / Krishna method for updating a set of tasks for greenhouse gas mitigation with the aforementioned teachings of: wherein the transfer learning machine learning model is configured to: based on an image associated with one of the tasks, determine a set of simulation parameters for a simulation simulating the one of the tasks; and provide the set of simulation parameters as a portion of the training data to a machine learning model, and in further view of Krishna, whereby the machine learning model training module can function to train, retrain, and/or refine the models described herein. For example, models can be trained and/or fine-tuned via transfer learning techniques on domain-specific documents and literature on manufacturing, industrial systems, energy management, and sustainability (e.g., equipment manuals, journals, research papers, etc.,) to provide specific, actionable steps to efficiently reduce resource inputs (e.g., energy consumption) and/or resource outputs (e.g., emissions). This may be based on an understanding of what modifications can be made to equipment settings to make them operate with reduced emissions (see at least Krishna: ¶ [0097].).
Further, the claimed invention is merely a combination of old elements in a similar field for updating a set of tasks for greenhouse gas mitigation and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Krishna, the results of the combination were predictable.
15. Claim 14 are rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub (US 2023/0135611 A1) hereinafter Kojo, et. al., in view of US PG Pub (US 2016/0092808 A1) hereinafter Cheng, et. al., in view of US PG Pub (US 2023/0085225 A1) hereinafter Matsuoka, et. al., in view of US PG Pub (US 2012/00290104 A1) hereinafter Holt, et. al., and in further view of US PG Pub (US 2023/0394494 A1) hereinafter MacArthur.
Regarding Dependent Claim 14, Kojo / Cheng / Matsuoka / Holt method for updating a set of tasks for greenhouse gas mitigation as applied to Claims 1 and 10 above does not explicitly disclose, but MacArthur in the analogous art for updating a set of tasks for greenhouse gas mitigation disclose the following:
- wherein the permanence action (see at least MacArthur: ¶ [0032] & ¶ [0037] & ¶ [0055-0056]. - wherein determining the permanence action comprises determining that the permanence action is supportive of the task for a period of time (see at least MacArthur: ¶ [0037] & ¶ [0060-0061] & Fig. 5. MacArthur teaches that carbon scoring matrix 104 may include a permanence 102-3 value assigned to an offset. For example, this designation may be a subset of the years of environmental benefits as described with respect to additionality 102-1 value. It may apply to the permanent retirement or avoidance of carbon, such as would be the case with cap-and-trade offsets that are prevented from use as permits for fossil-fueled utilities to operate for extended periods. The letter “P” can be appended to a certification. See also MacArthur at ¶ [0060]: The programmed tasks may further include acceptance of the particular environmental offset based on the environmental benefit score surpassing a particular threshold; generating sustainability rankings helping prioritize investments in companies that demonstrate strong environmental performance and sustainability practices. See also MacArthur at ¶ [0061] & Fig. 5: MacArthur notes that the device may obtain an environmental benefit score (e.g., CarbonScore) for a particular environmental offset (e.g., a carbon offset), the environmental benefit score calculated by executing a standardized algorithm based on a plurality of environmental benefit attributes associated with the particular environmental offset. In step 515, the device may then complete one or more programmed tasks based on the environmental benefit score for the particular environmental offset.)
- wherein the permanence action (see at least MacArthur: ¶ [0037] & ¶ [0060-0061] & Fig. 5.) supportive of the task is an investment (see at least MacArthur: Figs. 4-5 & ¶ [0060]. MacArthur notes that system 100 may utilize the EB score to generate a threshold-based certification of environmental offset reports regarding the particular environmental offset; screen investments based on environmental criteria; automatically analyze ESG (Environmental, Social, and Governance) data considering environmental factors such as carbon footprint, water usage, waste management, and environmental policies; complete a smart contract (based on the score); and so on. The programmed tasks may further include acceptance of the particular environmental offset based on the environmental benefit score surpassing a particular threshold; generating sustainability rankings helping prioritize investments in companies that demonstrate strong environmental performance and sustainability practices; recommending green investment opportunities such as renewable energy projects, sustainable infrastructure development, or companies involved in environmentally friendly technologies.) of a carbon credit, carbon offset, or a combination thereof, for a period of at least 5 years (see at least MacArthur: ¶ [0035-0038] & ¶ [0058] & Figs. 4-5. MacArthur notes that an offset (credit) may benefit the environment over the course of one year versus others doing so over five years, but the credit is taken by a party that is in need of money for food, education, health, etc., and thus may carry a low rank on sustainability but still be considered highly valuable in terms of social value. The “environmental score” (or “CarbonScore”) herein can also be calculated to account for this, and to specifically denote this. See also MacArthur at ¶ [0035]: Carbon scoring matrix 104 may include an additionality 102-1 value assigned to an offset. For example, an offset certificate number may be appended with an “A” followed by the years of benefits ranging from 1 to 50+. Thus, an A20 score may be an offset meeting additionality criteria for a period of 20 years, whereas an A1 score is an offset meeting additionality criteria for only one year. The scoring could be arranged in reverse order such that A1 represents 50+ years of environmental benefits and A50 would represent an offset with only one year of benefit. In another example, for an offset derived from a forestry project, whereby the offset would not occur without compensating the forest owner, the owner may agree not to cut the timber for a period of 1 to 50 years (each year earning an extra point).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Kojo / Cheng / Matsuoka / Holt method for updating a set of tasks for greenhouse gas mitigation with the aforementioned teachings of: wherein the transfer learning machine learning model is configured to: wherein determining the permanence action comprises determining that the permanence action is supportive of the task for a period of time, and wherein the permanence action supportive of the task is an investment of a carbon credit, carbon offset, or a combination thereof, for a period of at least 5 years, and in further view of MacArthur, whereby the particular environmental offset may correspond to one or more environmental assets selected from a group consisting of: carbon-based pollution; water conservation; material waste; and methane production; among others. Also, one or more of the environmental benefit attributes may be categorical attributes and/or ordinal attributes. In one embodiment, the one or more environmental benefit attributes may be selected from a group consisting of: additionality; permanence; leakage; duplication; overestimation; other harms; and likelihood to meet stated environmental benefits (see at least MacArthur: ¶ [0056].).
Further, the claimed invention is merely a combination of old elements in a similar field for updating a set of tasks for greenhouse gas mitigation and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by MacArthur, the results of the combination were predictable.
16. Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub (US 2023/0135611 A1) hereinafter Kojo, et. al., in view of US PG Pub (US 2016/0092808 A1) hereinafter Cheng, et. al., in view of US PG Pub (US 2023/0085225 A1) hereinafter Matsuoka, et. al., in view of US PG Pub (US 2012/00290104 A1) hereinafter Holt, et. al., and in further view of US PG Pub (US 2023/0290247 A1) hereinafter McBride, et. al.
Regarding Dependent Claim 18, Kojo / Cheng / Matsuoka / Holt method for updating a set of tasks for greenhouse gas mitigation as applied to Independent Claim 1 above does not explicitly disclose, but McBride in the analogous art for updating a set of tasks for greenhouse gas mitigation disclose the following:
- receiving measurements indicating progress of the set of tasks (see at least McBride: ¶ [0068] & ¶ [0079] & ¶ [0102]. McBride teaches that it is stated that the use of the ISO 14060 family of standards can enhance the environmental integrity of GHG quantification; enhances the credibility, consistency and transparency of GHG quantification, monitoring, reporting, verification and validation; facilitate the development and implementation of GHG management strategies and plans; facilitate the development and implementation of mitigation actions through emission reductions or removal enhancements; and facilitate the ability to track performance and progress in the reduction of GHG emissions and/or increase in GHG removals. See also McBride at ¶ [0062-0063]: Under both the baseline and project condition, the only GHG emission source affected by the project activities would be GHG emissions from fuel combustion in internal combustion engine vehicles. See also McBride at ¶ [0079]: Determination of the baseline may be performed several ways including but not limited to manual measurements, test driving the corridor, GPS tracking of test fleets, purchasing connected vehicle data, traditional ground loop estimates, crowdsourcing, or performing the above process after installing a new network of SDs 14 and prior to implementing any signaling changes within the network.), the receiving comprising:
- receiving, from a sensor (see at least McBride: ¶ [0049] & ¶ [0074] & ¶ [0127]. McBride teaches that the video capture device 24 includes an image sensor 40 for capturing a series of images to generate the frames of a video, a local video storage module 42, and a local processing module 44 for performing local processing functions such as object of interest extraction, compression, etc. See also McBride at ¶ [0074]: The camera may take the form of a CCD or CMOS sensor found in consumer photographic equipment or may take the form of other computer vision systems such as Lidar, Radar and other refracted light or sound-based systems. See also McBride at ¶ [0127]: The proposed system can obtain weather using sensors and cameras 24 in the SDs 14.), data indicative of an ecological condition (see at least McBride: ¶ [0056] & ¶ [0064]. McBride teaches that the emissions model 28 may utilize the following input data: weather conditions (temperature, humidity), regional fuel composition, regional fleet composition (e.g., electric vehicles, diesel or gasoline trucks, age of vehicles), emissions factors estimated by peer-reviewed sources, etc. See also McBride at ¶ [0064]: Temperature and humidity (publicly available or through third-party data), etc.);
- using the data indicative of the ecological condition as input data (see at least McBride: ¶ [0056] & ¶ [0064].), executing a simulation to provide output data (see at least McBride: ¶ [0083]. McBride notes that the process shown in FIG. 7 may be performed using real world data or it may be performed using simulations. See also McBride at ¶ [0088]: Where the concept of providing each vehicle in the network with an identifier is discussed, it should be noted that this identifier does not need to be unique. What should be obtained is a granularity that is sufficient to build the necessary computer simulations required to derive an accurate GHG emission calculation.), wherein comparing the measurements of the metric comprises using the output data (see at least McBride: ¶ [0026] & ¶ [0043] & ¶ [0079-0082]. McBride notes that comparing new GHG emissions to the baseline GHG emissions to compute carbon offset credits. See also McBride at ¶ [0043]: Following systems and methods can also be used to optimize GHG emissions by monitoring, analyzing and adjusting traffic intersection signaling and timing parameters and using GHG emissions calculations to compare to baseline data to determine if improvements have been made. See also McBride at ¶ [0079]: The real-world estimation or calculation of GHG emissions performed by the disclosed method may be compared to a baseline value to generate an GHG offset (e.g., improvement from baseline. In all cases, the baseline GHG calculations can then be compared against the project emissions with signaling changes in place and real-world data. See also McBride at ¶ [0082] & ¶ [0135].)
- comparing the measurements to the metric (see at least McBride: ¶ [0026] & ¶ [0043] & ¶ [0079-0082]. See also ¶ [0063-0064]: The proposed method for monitoring emission reductions generated by project activities is via modeling, as described above. The emission reductions would be quantified via data input into a traffic model 27 to quantify key metrics of the travel system and provide the inputs into the emissions model 28. Multi-modal turning movement counts in the project condition, individual signal timing in the project condition and baseline condition (measured prior to project start), project condition network geometry, vehicle fleet characteristics in the traffic network, network performance metrics (e.g., stops, delays, queues), temperature and humidity (publicly available or through third-party data), etc.);
- in response to the comparison between the measurements (see at least McBride: ¶ [0026] & ¶ [0043] & ¶ [0079-0082].) and the metric (see at least McBride: ¶ [0063-0064]), determining to select another replacement task (see at least McBride: ¶ [0078]. McBride teaches that the vehicle trajectories, network geometry, speed, and vehicle category information as well as calculated GHG emissions may then be used in step 108 as inputs to an approved GHG offset calculation model 29 as approved by GHG authorities 32, which may refer to EM models 28 such as MOVES, in order to calculate carbon, offset credits 30. This process may be facilitated by a project proponent who uses an approved GHG offset model 29 according to a GHG accounting protocol to turn green projects such as infrastructure improvements, traffic improvements, and tree planting, to name but a small selection of possible projects, into carbon offset credits 30.)
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have combined / modified the teachings of Kojo / Cheng / Matsuoka / Holt method for updating a set of tasks for greenhouse gas mitigation with the aforementioned teachings of: the set of tasks shown above, and in further view of McBride, whereby standards can enhance the environmental integrity of GHG quantification; enhances the credibility, consistency and transparency of GHG quantification, monitoring, reporting, verification and validation; facilitate the development and implementation of GHG management strategies and plans; facilitate the development and implementation of mitigation actions through emission reductions or removal enhancements; and facilitate the ability to track performance and progress in the reduction of GHG emissions and/or increase in GHG removals. As such, a standardized approach may be required or desired for a project for any one or more of these purposes (see at least McBride: ¶ [0068].).
Further, the claimed invention is merely a combination of old elements in a similar field for updating a set of tasks for greenhouse gas mitigation and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by McBride, the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US Patent or US PG Pub Documents
-> US PG Pub (US 2021/0294538 A1) – “Component Management Device, Component Management Method, and Non-Transitory Storage Medium”, hereinafter Oyama, et. al. Oyama notes at [abstract] that “The detection unit detects a first-class component whose used amount exceeds the threshold value, and which is used without causing a failure, and detects a first-class device that is a device including the detected first-class component and not causing a failure related to the first-class component. The correlation determination unit determines the presence or absence of a correlation with respect to a first correlation that is a correlation between the used amount of the detected first-class component and an operating condition of the first-class device.”
-> US PG Pub (US 2020/0326698 A1) – “Failure Prediction Device, Failure Prediction Method, Computer Program, Calculation Model Learning Method, and Calculation Model Generation Method”, hereinafter Kikuchi, et. al. Kikuchi teaches at [abstract] that a plurality of failure prediction model units that output a failure prediction result according to a value input to calculation models generated for different failure details based on operation information on operation up to the occurrence of the respective failures from operation history of a predetermined device; and an operation information input unit that inputs operation information acquired from a device subjected to failure prediction to the plurality of failure prediction model units.
-> US PG Pub (US 2024/0231983 A1) – “Asset Replacement Optimization Based on Predicted Risk of Failure”, hereinafter Phan. Phan teaches at [abstract] that an optimization system may obtain health information identifying different measures of health of an asset. The health information identifies end of life information regarding an end of life curve of the asset and an effective age of the asset. The optimization system may determine, based on the health information, a hazard curve for the asset. The hazard curve indicates a predicted failure rate of the asset over a period of time. The optimization system may provide the hazard curve and the effective age of the asset as inputs to an optimization model. The optimization system may use the optimization model to determine a particular time for replacing the asset, wherein the particular time is determined based on the hazard curve and the effective age. The optimization system may cause the asset to be replaced at the particular time.
-> US PG Pub (US 2012/0173299 A1) – “Systems and Methods for Use in Correcting a Predicting Failure in a Production Process”, hereinafter McMullin. McMullin at [abstract] notes that a plurality of correction scenarios is determined for a predicted production asset failure. A total cost for each correction scenario of the plurality of correction scenarios is estimated by the computing device based at least in part on a cost of repair and a cost of downtime associated with the correction scenario. One of the correction scenarios is selected based at least in part on the total cost of the correction scenario.
-> US PG Pub (US 2024/0127262 A1) – “System and Method for Intelligently Recovering a Client Information Handling System from an unsustainable state in greenhouse gas emissions over a device life cycle”, hereinafter Aurongzeb, et. al. Aurongzeb at [abstract] notes may comprise a network interface device to receive operational telemetry measurements for a client device during routine monitoring intervals, including a CO2 emissions value exceeding a non-eco-friendly state transition threshold value for the client device and indication of a failed hardware component, the hardware processor to predict, via a neural network modeling a relationship between changes in CO2 emissions values and changes in operational telemetry measurements, that replacement of the failed hardware component with a new replacement component having a known power efficiency value will cause a future determined CO2 emissions value for the client device to fall below the non-eco-friendly state transition threshold value, and the network interface device to transmit a replacement instruction for display to a user of the client device to install the new replacement component.
-> US PG Pub (US 2022/0172069 A1) – “Method for Updating Model of Facility Monitoring System”, hereinafter Kim, et. al. Kim notes at [abstract] updating a model of a facility monitoring system, the method including: checking occurrence of an event; when the event occurs, retraining a neural network model that performs a failure diagnosis; extracting a previous threshold value of the neural network model; calculating a new threshold value based on the extracted previous threshold value and a median value of state variables calculated through the retraining of the neural network model; and updating the threshold value of the neural network model based on the new threshold value.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DERICK HOLZMACHER whose telephone number is (571) 270-7853. The examiner can normally be reached on Monday-Friday 9:00 AM – 6:30 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached on 571-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-270-8853.
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/DERICK J HOLZMACHER/Patent Examiner, Art Unit 3625A
/SARA GRACE BROWN/Primary Examiner, Art Unit 3625
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