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 .
Status of the Application
2. Claims 1-20 have been examined in this application. This communication is the first action on the merits.
IDS Statements
3. The 2 Information Disclosure Statements (IDS’s) filed on 11/20/2024 and 01/29/2025 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.
Claim Objections
5. Claims 1, 3, 5-6, 15-16 and 19-20 are objected to because of the following informalities:
(A). The 1st claim limitation of Independent Claims 1 and 19-20 recite the following: “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.” There appears to be a minor claim informality or missing context regarding “the set” which previously is referred to as “a set of tasks”. Therefore, for the purpose of examination, Examiner suggests to Applicant to amend the 1st claim limitation of Independent Claims 1 and 19-20 to read as follows: “generating a set of tasks, wherein each task comprises an offset potential and one or more failure mechanisms, and wherein the set of tasks comprises a metric.”
(B). The 2nd claim limitation of Independent Claims 1 and 19-20 recite the following: “determining, by a machine learning 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”. There appears to be a minor claim informality or missing context regarding “the set” which previously is referred to as “a set of tasks”. Therefore, for the purpose of examination, Examiner suggests to Applicant to amend the 2nd claim limitation of Independent Claims 1 and 19-20 to read as follows: “determining, by a machine learning model and based on multiple data types from a plurality of sources, 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”.
(C). The 2nd limitation of Dependent Claim 3 recites the following limitation: “determining, for the task of the set of tasks exceeding the failure threshold, a mitigation failure value.” There appears to be a minor claim informality or missing context regarding “the failure threshold” which previously is referred to as “a first failure threshold”. To be consistent here, for the purposes of examination, Examiner suggests to Applicant to amend the 2nd limitation of Dependent Claim 3 to recite the following: “determining, for the task of the set of tasks exceeding the first failure threshold, a mitigation failure value.”
(D). The 1st limitation of Dependent Claim 5 recites the following limitation: “wherein determining, by the machine learning model and based on multiple data types from the plurality of sources, that the task of the set of tasks exceeds the failure threshold for the one or more failure mechanisms comprises.” There appears to be a minor claim informality or missing context regarding “the failure threshold” which previously is referred to as “a first failure threshold”. To be consistent here, for the purposes of examination, Examiner suggests to Applicant to amend the 1st limitation of Dependent Claim 5 to recite the following: “wherein determining, by the machine learning model and based on the multiple data types from the plurality of sources, that the task of the set of tasks exceeds the first failure threshold for the one or more failure mechanisms comprises.”
(E). The 2nd limitation of Dependent Claim 6 recites the following limitation: “predicting, based on aggregated impacts across all the tasks of the set, a total impact of the scenario on the set of tasks.” There appears to be a minor claim informality or missing context regarding “the scenario” which previously is referred to as “the failure scenario”. To be consistent here, for the purposes of examination, Examiner suggests to Applicant to amend the 2nd limitation of Dependent Claim 6 to recite the following: “predicting, based on aggregated impacts across all the tasks of the set, a total impact of the failure scenario on the set of tasks.”
(F). The last limitation of Dependent Claim 15 recites the following: “updating the set with the updated values for 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.” There appears to be a minor claim informality or missing context regarding “the set” which previously is referred to as “a set of tasks”. To be consistent here, for the purpose of examination, Examiner suggests to Applicant to amend the last limitation of Dependent Claim 15 to read as follows: “updating the set of tasks with the updated values for 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.”
(G). The 1st limitation and 2nd limitation of Dependent Claim 16 recites the following limitations: “in response to updating the set with the updated values, determining that the overall risk of the set exceeds the failure threshold” & “in response to determining that the overall risk of the set exceeds the failure threshold, selecting another replacement task for the set.” There appears to be a minor claim informalities or missing context regarding “the set” which previously is referred to as “a set of tasks” and “the failure threshold” which previously is referred to as “a first failure threshold” and also regarding “overall risk” which previously is referred to as “an overall risk score”. To be consistent here, for the purpose of examination, Examiner suggests to Applicant to amend the 1st limitation and 2nd limitation of Dependent Claim 16 to read as follows: “in response to updating the set of tasks with the updated values, determining that the overall risk score of the set of tasks exceeds the first failure threshold” & “in response to determining that the overall risk score of the set of tasks exceeds the first failure threshold, selecting another replacement task for the set of tasks.” Appropriate corrections are required.
Claim Rejections - 35 USC § 101
6. 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.
7. 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 failure correlation of the replacement candidate with respect to each other sets of the set of tasks” (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” (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 automated risk assessment and mitigation through mathematical modeling. These claims focus on the functional result of identifying high-risk tasks and substituting them based on correlation scores, rather than a specific technical improvement to the functioning of a computer itself. Since these claims simply describes what the machine learning model does (achieving the result of an updated task list) rather than how the model’s internal architecture is technically improved, it remains focused on the abstract idea itself.
Certain Methods of Organizing Human Activities: 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.
Mental Processes: 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: “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.
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 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 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 § 102
8. 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.
9. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
10. Claims 1-7, 10-13, 15-17 and 19-20 are rejected under 35 U.S.C. 102 (a) (2) as being anticipated by US PG Pub (US 2023/0135611 A1) hereinafter Kojo, 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.);
- assigning, 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 to each of the plurality of replacement candidates, a replacement score for the replacement candidate based on a failure correlation of the replacement candidate with respect to each other sets of the set of 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.);
- ranking the plurality of replacement candidates based on the replacement scores (see at least Kojo: ¶ [0071] & ¶ [0080-0083] & ¶ [0132-0153]. Kojo notes 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. All scores are recorded in the CIM Pool as Internal Factors, such as CIM 1 internal factors 462 and CIM 3 internal factors 466, for each CIM. See also Kojo at ¶ [0080-0083] noting “climate integrity scores” for the plurality of replacement candidates shown in Kojo reference. See also Kojo noting ranking of the replacement candidates in Tables 1-10. See also Kojo at ¶ [0132-0153]: The Ranking and Optimization Module 136 compiles the (tentative) OV Batches on the basis of data received from the foregoing modules and processes. Once a batch is ready, the module also runs Penalty Functions 642 to test the adherence of the batch to received rules and preferences (typically CIM Allocation Rules and User Preference rules (if available) but possibly also other rules and constraints). Where the batch does not conform to the rules and preferences, penalty factors received within the Penalty Functions 642 will be applied. The penalty factors will be applied to the Offset Value of the OV Batch during a single or multiple optimizations runs (as may be set in the Optimization Run Rules). As a result, an Objective Value (ObV) is returned, which comprises the aggregate OV of the OV Batch readjusted by the Penalty Functions. The Ranking and Optimization Module 136 may then replace the aggregate OV of the batch with the value of the ObV, which typically reduces the aggregate OV of the batch and therefore makes it less optimal. See also Tables 1-9 of Kojo and Kojo at ¶ [0180-0194].);
- selecting, based on the ranking, the replacement task (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.);
- generating, an updated set of tasks including the replacement task (see at least Kojo: ¶ [0107] & ¶ [0128] & ¶ [0221]. Kojo notes that the Internal Factors of any CIM may be updated by the experts (and/or by the Meta-Optimization Logic) from time to time. See also Kojo at ¶ [0107]: The Platform may include a mechanism for updating information about CIMs. See also Kojo at ¶ [0128]: The CME 130 also stores information about CIMs and CIM Allocation Rules and offers a method for creating, updating and deleting them. The CME may also store User data concerning User's preferences for making MCC purchases. When the CME determines that a new purchase should be made, it further processes the information about User Orders, User purchase preferences, CIMs and CARs utilizing optimization logic. See also Kojo at ¶ [0221]: Repeating the steps depicted above, the ROM finds that CIM.1.a.iv has the highest OV/price, and selects it for the TSB1.1(R). The ROM therefore removes the CIM Units from CIM.1.a.iii from the OV Batch and allocates a total of 417 CIM Units*0.901 OV/1000=375 OV from CIM.1.a.iv. See also Kojo at Tables 1-9.).
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.);
- assigning, 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 to each of the plurality of replacement candidates, a replacement score for the replacement candidate based on a failure correlation of the replacement candidate with respect to each other sets of the set of 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.);
- ranking the plurality of replacement candidates based on the replacement scores (see at least Kojo: ¶ [0071] & ¶ [0080-0083] & ¶ [0132-0153]. Kojo notes 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. All scores are recorded in the CIM Pool as Internal Factors, such as CIM 1 internal factors 462 and CIM 3 internal factors 466, for each CIM. See also Kojo at ¶ [0080-0083] noting “climate integrity scores” for the plurality of replacement candidates shown in Kojo reference. See also Kojo noting ranking of the replacement candidates in Tables 1-10. See also Kojo at ¶ [0132-0153]: The Ranking and Optimization Module 136 compiles the (tentative) OV Batches on the basis of data received from the foregoing modules and processes. Once a batch is ready, the module also runs Penalty Functions 642 to test the adherence of the batch to received rules and preferences (typically CIM Allocation Rules and User Preference rules (if available) but possibly also other rules and constraints). Where the batch does not conform to the rules and preferences, penalty factors received within the Penalty Functions 642 will be applied. The penalty factors will be applied to the Offset Value of the OV Batch during a single or multiple optimizations runs (as may be set in the Optimization Run Rules). As a result, an Objective Value (ObV) is returned, which comprises the aggregate OV of the OV Batch readjusted by the Penalty Functions. The Ranking and Optimization Module 136 may then replace the aggregate OV of the batch with the value of the ObV, which typically reduces the aggregate OV of the batch and therefore makes it less optimal. See also Tables 1-9 of Kojo and Kojo at ¶ [0180-0194].);
- selecting, based on the ranking, the replacement task (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.);
- generating, an updated set of tasks including the replacement task (see at least Kojo: ¶ [0107] & ¶ [0128] & ¶ [0221]. Kojo notes that the Internal Factors of any CIM may be updated by the experts (and/or by the Meta-Optimization Logic) from time to time. See also Kojo at ¶ [0107]: The Platform may include a mechanism for updating information about CIMs. See also Kojo at ¶ [0128]: The CME 130 also stores information about CIMs and CIM Allocation Rules and offers a method for creating, updating and deleting them. The CME may also store User data concerning User's preferences for making MCC purchases. When the CME determines that a new purchase should be made, it further processes the information about User Orders, User purchase preferences, CIMs and CARs utilizing optimization logic. See also Kojo at ¶ [0221]: Repeating the steps depicted above, the ROM finds that CIM.1.a.iv has the highest OV/price, and selects it for the TSB1.1(R). The ROM therefore removes the CIM Units from CIM.1.a.iii from the OV Batch and allocates a total of 417 CIM Units*0.901 OV/1000=375 OV from CIM.1.a.iv. See also Kojo at Tables 1-9.).
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.);
- assigning, 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 to each of the plurality of replacement candidates, a replacement score for the replacement candidate based on a failure correlation of the replacement candidate with respect to each other sets of the set of 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.);
- ranking the plurality of replacement candidates based on the replacement scores (see at least Kojo: ¶ [0071] & ¶ [0080-0083] & ¶ [0132-0153]. Kojo notes 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. All scores are recorded in the CIM Pool as Internal Factors, such as CIM 1 internal factors 462 and CIM 3 internal factors 466, for each CIM. See also Kojo at ¶ [0080-0083] noting “climate integrity scores” for the plurality of replacement candidates shown in Kojo reference. See also Kojo noting ranking of the replacement candidates in Tables 1-10. See also Kojo at ¶ [0132-0153]: The Ranking and Optimization Module 136 compiles the (tentative) OV Batches on the basis of data received from the foregoing modules and processes. Once a batch is ready, the module also runs Penalty Functions 642 to test the adherence of the batch to received rules and preferences (typically CIM Allocation Rules and User Preference rules (if available) but possibly also other rules and constraints). Where the batch does not conform to the rules and preferences, penalty factors received within the Penalty Functions 642 will be applied. The penalty factors will be applied to the Offset Value of the OV Batch during a single or multiple optimizations runs (as may be set in the Optimization Run Rules). As a result, an Objective Value (ObV) is returned, which comprises the aggregate OV of the OV Batch readjusted by the Penalty Functions. The Ranking and Optimization Module 136 may then replace the aggregate OV of the batch with the value of the ObV, which typically reduces the aggregate OV of the batch and therefore makes it less optimal. See also Tables 1-9 of Kojo and Kojo at ¶ [0180-0194].);
- selecting, based on the ranking, the replacement task (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.);
- generating, an updated set of tasks including the replacement task (see at least Kojo: ¶ [0107] & ¶ [0128] & ¶ [0221]. Kojo notes that the Internal Factors of any CIM may be updated by the experts (and/or by the Meta-Optimization Logic) from time to time. See also Kojo at ¶ [0107]: The Platform may include a mechanism for updating information about CIMs. See also Kojo at ¶ [0128]: The CME 130 also stores information about CIMs and CIM Allocation Rules and offers a method for creating, updating and deleting them. The CME may also store User data concerning User's preferences for making MCC purchases. When the CME determines that a new purchase should be made, it further processes the information about User Orders, User purchase preferences, CIMs and CARs utilizing optimization logic. See also Kojo at ¶ [0221]: Repeating the steps depicted above, the ROM finds that CIM.1.a.iv has the highest OV/price, and selects it for the TSB1.1(R). The ROM therefore removes the CIM Units from CIM.1.a.iii from the OV Batch and allocates a total of 417 CIM Units*0.901 OV/1000=375 OV from CIM.1.a.iv. See also Kojo at Tables 1-9.).
Regarding Dependent Claim 2, Kojo 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 assigning the replacement score for the replacement candidate based on failure correlation (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.) comprises assigning the replacement score based on (i) predictive 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.) and (ii) a predicted offset potential (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).)
Regarding Dependent Claim 3, Kojo 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 ranking the plurality of replacement candidates based on the replacement scores further comprises (see at least Kojo: ¶ [0071] & ¶ [0080-0083] & ¶ [0132-0153]. Kojo notes 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. All scores are recorded in the CIM Pool as Internal Factors, such as CIM 1 internal factors 462 and CIM 3 internal factors 466, for each CIM. See also Kojo at ¶ [0080-0083] noting “climate integrity scores” for the plurality of replacement candidates shown in Kojo reference. See also Kojo noting ranking of the replacement candidates in Tables 1-10. See also Kojo at ¶ [0132-0153]: The Ranking and Optimization Module 136 compiles the (tentative) OV Batches on the basis of data received from the foregoing modules and processes. Once a batch is ready, the module also runs Penalty Functions 642 to test the adherence of the batch to received rules and preferences (typically CIM Allocation Rules and User Preference rules (if available) but possibly also other rules and constraints). Where the batch does not conform to the rules and preferences, penalty factors received within the Penalty Functions 642 will be applied. The penalty factors will be applied to the Offset Value of the OV Batch during a single or multiple optimizations runs (as may be set in the Optimization Run Rules). As a result, an Objective Value (ObV) is returned, which comprises the aggregate OV of the OV Batch readjusted by the Penalty Functions. The Ranking and Optimization Module 136 may then replace the aggregate OV of the batch with the value of the ObV, which typically reduces the aggregate OV of the batch and therefore makes it less optimal. See also Tables 1-9 of Kojo and Kojo at ¶ [0180-0194].)
- determining, for the task of the set of tasks exceeding the failure threshold, a mitigation failure value (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.);
- ranking the replacement candidates based on respective potential of each candidate replacement project to repair the mitigation failure value (see at least Kojo: ¶ [0156] & ¶ [0180-0194] & (Tables 1-9 of Kojo). 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. See also Tables 1-9 of Kojo.).
Regarding Dependent Claim 4, Kojo 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 ranking the plurality of replacement candidates further comprises (see at least Kojo: ¶ [0156] & ¶ [0180-0194] & (Tables 1-9 of Kojo). 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. See also Tables 1-9 of Kojo.) ranking the replacement candidates based on real-time data collected from similar mitigation projects (see at least Kojo: ¶ [0095-0096] & ¶ [0120-0123]. Kojo notes that the Platform offers a mechanism to specify User Preferences for the Offsetting, such as preferred CIM types, methods and geographical locations. An example of a preference would be favoring nature-based methods, including methods like reforestation, forest protection and kelp farming, using mechanical carbon capture. The Platform also might offer sets of CIMs as portfolios and offer User Preferences to favor certain CIMs over the others. Example portfolios might include innovative capture projects or community-run projects. The Platform contains data on a number of Climate Impacting Measures (CIM) that represent carbon sequestration projects operated by independent CIM Suppliers all over the world. With each CIM are associated a number of External and Internal Factors describing that particular instance. See also Kojo at ¶ [0120-0123]: CIM type: This refers to 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; and CIM region: this refers to a geographical area at the specificity set by the Platform operator (e.g. continent, sub-continent or country). See also Tables 1-10 of Kojo.).
Regarding Dependent Claim 5, Kojo 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 task of the set of tasks exceeds the failure threshold for the one or more failure mechanisms (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 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 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 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 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 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 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 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 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 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.)
Claim Rejections - 35 USC § 103
11. 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.
12. 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.
13. 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.
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., and in view of US PG Pub (US 2024/0403776 A1) hereinafter Krishna, et. al.
Regarding Dependent Claim 8, Kojo 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 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 / 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 / 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., and in view of US PG Pub (US 2023/0394494 A1) hereinafter MacArthur.
Regarding Dependent Claim 14, Kojo 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 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., and in view of US PG Pub (US 2023/0290247 A1) hereinafter McBride, et. al.
Regarding Dependent Claim 18, Kojo 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 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
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, Applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
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
/BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625
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