DETAILED ACTION
Status of the Application
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The following is a Non-Final Office Action in response to claims filed on November 6, 2024.
Claims 1-20 are currently pending and have been examined.
Information Disclosure Statement
The Information Disclosure Statements filed November 20, 2024 have been considered. Initialed copies of the Form 1449 are enclosed herewith.
Claim Rejections - 35 USC § 101
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.
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: Is the claim to a process, machine, manufacture or composition of matter? (MPEP 2106.03)
In the present application, claims 1-11 are directed to a device (i.e., a machine), claim 12 is directed to a method (i.e., a process), claim 13 is directed to a computer product (i.e. an article of manufacture). Thus, the eligibility analysis proceeds to Step 2A. prong one.
Step 2A. prong one: Does the claim recite an abstract idea, law of nature, or natural phenomenon? (MPEP 2106.04)
While claims 1, 12, and 13, are directed to different categories, the language and scope are substantially the same and have been addressed together below.
The abstract idea recited in claims 1, 12, and 13, is
extracting carbon-relevant data from monitored entities;
determining a calculation route based on the carbon-relevant data based on a relevance of a carbon product contribution of the monitored entities to a goal of the monitored entities;
calculating a carbon emission based on the carbon-relevant data and the calculation route by utilizing an agent-based simulation model that simulates a learned relationship between a supply chain system and the carbon-relevant data; and
performing a corrective action to the monitored entities based on the carbon emission to limit a carbon product of a supply chain system below a carbon product threshold.
The claimed invention is directed to an abstract idea of business optimization and supply chain management based on carbon emission.
The claim limitations above recite a fundamental economic practice long prevalent in our system of commerce in the form of advertising, marketing, or sales activity or behaviors for business process of optimization and supply chain management. Under the broadest reasonable interpretation, other than the additional elements of computer components, the limitations recite a process of extracting information, determining a calculation route, calculating a carbon mission, and perform correction action. These limitations above closely follow the steps collection and manipulation of information to optimize a business practice of commercial workflow. Thus, the claims recite an abstract idea consistent with the “certain methods of organizing human activity” grouping of the abstract ideas, set forth in MPEP 2106.04(a)(2)(II).
Additionally, under the broadest reasonable interpretation, the steps of calculating a carbon emission… by utilizing an agent-based simulation model is directed to a mathematical concept. A simulation model is a series of mathematical algorithm, which falls under “Mathematical Concept” category of the abstract ideas.
Accordingly, the above-mentioned limitations are considered as a single abstract idea, therefore, the claims recite an abstract idea and the analysis proceeds to Step 2A. prong two.
Step 2A. prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? (MPEP 2106.04)
This judicial exception is not integrated into a practical application because the additional elements merely add instructions to apply the abstract idea to a computer.
The additional elements considered include:
Claim 1: “computer-implemented”; “agent-based system”;
Claim 8: “system, comprising: a memory device; one or more processor devices operatively coupled with the memory device to:”;
Claim 15: “non-transitory computer program product comprising a computer-readable storage medium including program code for an agent-based carbon emission reduction system, wherein the program code when executed on a computer causes the computer to”;
In particular, the claim only recites the above-mentioned additional elements to extract, determine, and calculate information then perform a corrective action. The computer in the steps is recited at a high-level of generality (i.e., as generic computer components performing a generic computer function; See Applicant’s Specification at least at paragraphs [0072]-[0082) such that it amounts to no more than mere instructions to apply the exception using a generic computer component.
The function of limitations [A]-[D] are steps of adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea as discussed in MPEP 2106.05(f). specifically, the claims recite “performing a corrective action… to limit a carbon product.” However, the “corrective action” is described only by the result of limiting carbon to be achieved rather than a specific technical implementation. Thus, the combination of these additional elements is no more than mere instructions to apply the exception using a generic computer. Accordingly, even in combination, these additional element(s) do not integrate the abstract idea into a practical application because they do not improve a computer or other technology, do not transform a particular article, do not recite more than a general link to a computer, and do not invoke the computer in any meaningful way; the general computer is effectively part of the preamble instruction to “apply” the exception by the computer. Therefore, the claims are directed to an abstract idea and the analysis proceeds to Step 2B.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? (MPEP 2106.05)
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the bold portions of the limitations recited above, were all considered to be an abstract idea in Step2A-Prong Two. The additional elements and analysis of Step2A-Prong two is carried over. For the same reason, these elements are not sufficient to provide an inventive concept. Applicant has merely recited elements that instruct the user to apply the abstract idea to a computer or other machinery. When considered individually and in combination the conclusion, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer to perform the above-mentioned limitations [A]-[D] amount to no more than mere instructions to apply the function of the limitations to the exception using generic computer component, as discussed in MPEP 2106.05(f). The claim as a whole merely describes how to generally “apply” the concept for business optimization and supply chain management based on carbon emission. Thus, viewed as a whole, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. For these reasons there is no inventive concept in the claims and thus are ineligible.
As for dependent claims 2, 9, and 16, the claims provide further limitation of performing the corrective action comprising changing an operational parameter of a hardware component used by the monitored entity of the supply chain system. This limitation can be interpreted as a step performable by a person such as lowering temperature setting (operational parameter) on a thermostat (hardware component used by the monitored entity of the supply chain system), which does not change the abstract idea of the independent claim. The additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. The claims are ineligible. However, if the claim limitation is further narrowed to be a step specifically implemented by a system that cannot be a generic step performable by a person, for example “automatically adjusting, via the agent-based system, the voltage of a manufacturing robot” or “modifying, via the agent-based system, cooling cycle of a server rack” then the claims are eligible.
As for dependent claims 3-7, 10-14, and 17-20, these claims recite limitations that further define the abstract idea noted in the independent claims. The claims further recite additional abstract steps of generating, obtaining, selecting, and predicting information, which do not change the abstract idea of the independent claims. The claims recite the additional element of computer components at a high level of generality (i.e. as a generic computer system performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component, as discussed in MPEP 2106.05(f). Even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. The claims are ineligible.
In summary, the dependent claims considered both individually and as ordered combination do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. The claims do not recite an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment. Therefore, claims 1-20 are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
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 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.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kulkarni et al. (US 20230186217 A1), in view of Freier et al. (US 20230289911 A1), and further in view of Lee et al (US 20230177523 A1).
Claims 1, 8, and 15, Kulkarni discloses a computer-implemented method for reducing carbon emissions using an agent-based system, a system, and a non-transitory computer program product comprising a computer-readable storage medium including program code for an agent-based carbon emission reduction system (abstract), comprising:
a memory device; one or more processor devices operatively coupled with the memory device to (para. [0066]):
extract carbon-relevant data from entities (para. [0050], “obtaining enterprise-related data and carbon emissions-related data associated with the enterprise. In at least one embodiment, obtaining enterprise-related data includes obtaining one or more temporal features attributed to the enterprise and/or obtaining one or more spatial features attributed to the enterprise”);
determine a calculation route based on the carbon-relevant data based on a relevance of a carbon product contribution of the monitored entities to a goal of the monitored entities ([0016], “determining (for example, at the end of a given temporal period (e.g., month, quarter, year, etc.)) which strategic, tactical and/or operational decisions had an impact on the calculated carbon emissions, as well as performing sensitivity analysis” and [0036], “determining one or more strategic decisions such that a multi-year budget is met. For instance, such decisions can be related to total carbon emissions over n years≤Σi xi, given the carbon budget xi for year i. Additionally, such an embodiment can include determining one or more tactical decisions such that a yearly budget is met. For instance, such decisions can be related to total carbon emissions in year i, Σj yij≤xi, as well as computing the corresponding optimal budgets yij for each quarter in year i.” The sensitivity analysis is functional equivalent of determining the relevant of a carbon product contribution. The system determines a calculation route (the specific optimization workflow for a week, quarter, or year) based on how relevant a specific entity’s activities are to the stated goal (the pre-determined carbon budget));
calculate a carbon emission based on the carbon-relevant data and the calculation route by utilizing a model that simulates a learned relationship between a supply chain system and the carbon-relevant data (para. [0040], “training yearly, quarterly, and/or weekly machine learning-based models (e.g., supervised machine learning models such as a regression model that is based on different techniques such as tree-based methods, neural networks, etc.) for profits and/or emissions using at least a portion of the historical data. Also, based at least in part on the trained machine learning-based models, the example workflow depicted in FIG. 2 includes generating output 214 which includes one or more emissions” Kulkarni discloses the calculating carbon emissions using machine learning-based models trained on historical data); and
perform a corrective action to the monitored entities based on the carbon emission to limit a carbon product of a supply chain system below a carbon product threshold (Abstract, para. [0015], [0053] discloses performing one or more automated actions based at least on the one or more enterprise-related recommendation, and the actions are to meet carbon budget constraints).
However, Kulkarni does not expressly disclose the specific limitations (italic emphasis included):
from monitored entities;
utilizing an agent-based simulation model.
Nonetheless, Freier is the analogous field of a carbon emissions management system, which specifically teaches,
from monitored entities (Abstract, para. [0031], [0033], “client communicates a request for carbon emission data from a supplier of the client. The carbon emissions data comprises product-level data of the supplier. Based on communicating the request, the client receives the carbon emissions data.” Freier teaches the specifics of extracting and receiving of carbon emission data from supplier).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filling of the invention to modify the supply chain strategies based on carbon emission targets system and method of Kulkarni for providing of carbon emissions-related data to use the supplier request architecture for requesting carbon emissions-related data from supplier as taught by Freier for the motivation of providing an more efficient and reliable system for using the ecosystem management engine for standardized and automated way to extract product-level data from supplier that can improve the computing operations and interfaces for carbon emissions management (para. [0003]).
Still, the combination fails to expressly teach, utilizing an agent-based simulation model.
Lee is in similar field, directed to a method and an apparatus for a digital twin-based carbon emission management, which specifically teaches,
utilizing an agent-based simulation model (para. [0051]-[0052] teaches a digital twin dynamic model that performs a virtual operation according to change in input elements and models the structure, operation, behavior, current state, and change state of the observable object. The digital twin is an agent-based simulation model that represents a physical entity in a virtual space to simulate learned relationships).
Lee further teaches, perform a corrective action to the monitored entities based on the carbon emission to limit a carbon product of a supply chain system below a carbon product threshold (para. [0071], “If the total carbon emissions exceeds the carbon emission allowances, the carbon emission management system 330 may transmit information requesting adjustment/reduction of carbon emissions in the future to the digital twin of each operating entity and/or facility.” Lee teaches the requesting a reduction or adjustment to the monitored entity equipment when the threshold (allowance) is breached (below/exceed)).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filling of the invention to modify the machine-based models in the supply chain strategies based on carbon emission targets system and method of Kulkarni with digital twin simulations for the motivation of improving the accuracy of the carbon calculation within the supply chain system.
Claims 2, 9, and 16, the combination of Kulkarni, Freier, and Lee make obvious of the computer-implemented method of claim 1, the system of claim 8, and the non-transitory computer program product of claim 15. Lee further teaches,
wherein to perform the corrective action further comprises to change an operational parameter of a hardware component used by the monitored entity of the supply chain system (Lee: abstract, [0071] teaches the corrective action (adjustment) is performed on equipment operation at the facility). The rationale to modify the teaching of Kulkarni with/and the teachings of Lee is presented in the examining of independent claims 1, 8, and 15 and incorporated herein.
Claims 3, 10, and 17, the combination of Kulkarni, Freier, and Lee make obvious of the computer-implemented method of claim 1, the system of claim 8, and the non-transitory computer program product of claim 15. Kulkarni further discloses,
wherein to extract the carbon-relevant data further comprises to generate a monitored entity profile for the monitored entities (Kulkarni: para. [0040], “ingestion of historical data such as company and/or enterprise records”. Additionally and alternatively, Freier: para. [0034] teaches storing lists of suppliers).
Claims 4, 11, and 18, the combination of Kulkarni, Freier, and Lee make obvious of the computer-implemented method of claim 1, the system of claim 8, and the non-transitory computer program product of claim 15. Kulkarni further discloses,
wherein to determine the calculation route further comprises to associate a carbon product contribution level produced by the monitored entities to a goal of the monitored entity to obtain a goal contribution metric (Kulkarni: para. [0016] disclosing the determination of which strategic, tactical and/or operational decisions had an impact on the calculated carbon emissions, as well as performing sensitivity analysis which is function of determining carbon product contribution level. In para. [0020] disclosing a planner that optimizes supply chain decision while maximizing other enterprise objectives (e.g., profits) and meeting carbon budget limits. The use of sensitivity analysis to determine how a specific decision (contribution) affects an objective (goal) is the functional equivalent of obtaining a goal contribution metric).
Claims 5, 12, and 19, the combination of Kulkarni, Freier, and Lee make obvious of the computer-implemented method of claim 4, the system of claim 11, and the non-transitory computer program product of claim 18. Kulkarni further discloses,
wherein to determine the calculation route further comprises to select calculation routes based on the goal contribution metric and a route threshold (Kulkarni: para. [0015] disclosing “incorporating at least one dashboard configured to import carbon budget limits… carbon budget refers to a pre-determined upper-limit on carbon emissions”. In para. [0021] and [0042], discloses the determining whether to perform a strategic, tactical, or operational optimization (i.e., different calculation routes) based on whether the measured emission (goal contribution metric) exceed the pre-determined budget (route threshold). If a threshold is breached, the system selects a specific re-optimization route to bring the entity back into compliance. Additionally and alternatively, in Lee, para. [0071] teaches “If the total carbon emissions exceeds the carbon emission allowances,” which is system triggers an adjustment based on threshold (allowance)).
Claims 6, 13, and 20, the combination of Kulkarni, Freier, and Lee make obvious of the computer-implemented method of claim 1, the system of claim 8, and the non-transitory computer program product of claim 15. The combination further teaches,
wherein to calculate the carbon emission further comprises to generate carbon emission simulations to predict the carbon product of the supply chain system based on state changes (Kulkarni para. [0021] discloses that based on processed outputs, at least one estimation of emissions balance against available carbon budget can be generated. Lee: abstract “performing a simulation for carbon emission transaction and equipment operation” teaching the generating simulations to determine carbon outcomes. Lee para. [0051]-[0052] teaches the simulation model (digital twin) is driven by the states and change states of the entities being monitored). The rationale to modify the teaching of Kulkarni with/and the teachings of Lee is presented in the examining of independent claims 1, 8, and 15 and incorporated herein.
Claims 7 and 14, the combination of Kulkarni, Freier, and Lee make obvious of the computer-implemented method of claim 1 and the system of claim 8. The combination further teaches,
wherein calculating the carbon emission further comprises learning heuristics for the state changes using a state machine (Kulkarni para. [0038] teaches training machine learning models to identify patterns and rules from historical data. Further, Lee para. [0052] teaches a model that represents the current state, and change state of the observable object, which further teaches state machine for a model that transitions between states based on inputs and rules). It Would have been obvious to one ordinary skill in the art to implement the simulation of Lee using a state machine to combine the Kulkarni machine learning model with state-based model results in a system that leans heuristics for state changes using a state machine for improved system intelligence.
Relevant Prior Art Not Relied Upon
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure. The additional cited art, including but not limited to the excerpts below, further establishes the state of the art at the time of Applicant’s invention and shows the following was known:
Nguyen (US 20240127264 A1)
S. Jain, E. Lindskog and B. Johansson, "Supply chain carbon footprint tradeoffs using simulation," Proceedings of the 2012 Winter Simulation Conference (WSC), Berlin, Germany, 2012, pp. 1-12, doi: 10.1109/WSC.2012.6465242.
Conclusion
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/WENREN CHEN/Primary Examiner, Art Unit 3626
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