DETAILED ACTION
Status of the Claims
The following is a Final Office Action in response to amendments and remarks filed 21 August 2025.
Claims 1, 3, 13, 14, and 18 have been amended.
Claims 1-20 are pending and have been examined.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicants argue that the 35 U.S.C. 101 rejection under the Alice Corp. vs. CLS Bank Int’l be withdrawn; however the Examiner respectfully disagrees. The Examiner notes that in order to be patent eligible under 35 U.S.C. 101, the claims must be directed towards a patent eligible concept, which, the instant claims are not directed. Applicant argues that the claims cannot be performed mentally; however the Examiner notes that this assertion was never made as the Examiner noted the claims as reciting a method of organizing human activity. Contrary to Applicants’ assertions, the Examiner notes that supply chain logistics and carbon footprint (such as emissions tracking) is a function that companies and government agencies have traditionally performed/provided. Next, the claims are not directed to a practical application of the concept. The claims do not result in improvements to the functioning of a computer or to any other technology or technical field. They do not effect a particular treatment for a disease. They are not applied with or by a particular machine. They do not effect a transformation or reduction of a particular article to a different state or thing. And they are not applied in some other meaningful way beyond generally linking the use of the judicial exception (i.e., providing recommendations based upon obtained and analyzed data) to a particular technological environment (i.e., data that resides in a blockchain). Here, again as noted in the previous rejection, mere instructions to apply an exception using a generic computer component cannot provide an inventive concept - MPEP 2016.05(f). The claims recitation of the “wherein the information is extracted from multiple specialized blockchains including an SCM-company-operated GHG accounting blockchain, an SCM-company-operated carbon offset commitment blockchain, and third party operated mandatory GHG emissions reporting blockchain” is/are only generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). The claim(s) is/are not patent eligible and the argument not persuasive. As such, the rejection was not overcome.
Applicant’s next argue that the claims are eligible as the claims cannot be “practically performed in the human mind;”( due to the optimizing of GHG emissions and probabilistic modelling) however the Examiner respectfully disagrees. While the specification may discuss sophisticated techniques and advanced functions such as probabilistic estimate modeling and probability learning, it is the claims that are deemed eligible or ineligible under §101. Here, the claims are more of a generalized guideline of how to arrange a software model to implement the overarching abstract idea. Furthermore, this argument appears to be whether or not the use of computer or computing components for increased speed and efficiency makes the claims eligible; however the Examiner respectfully disagrees. Nor, in addressing the second step of Alice, does claiming the improved speed or efficiency inherent with applying the abstract idea on a computer provide a sufficient inventive concept. See Bancorp Servs., LLC v. Sun Life Assurance Co. of Can., 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“[T]he fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.”); CLS Bank, Int’l v. Alice Corp., 717 F.3d 1269, 1286 (Fed. Cir. 2013) (en banc) aff’d, 134 S. Ct. 2347 (2014) (“[S]imply appending generic computer functionality to lend speed or efficiency to the performance of an otherwise abstract concept does not meaningfully limit claim scope for purposes of patent eligibility.” (citations omitted)). The claim(s) is/are not patent eligible and the rejection not withdrawn.
Applicant’s remarks with respect to the prior art have been fully considered and addressed below in the updated rejection, as necessitated by amendments.
In response to arguments in reference to any depending claims that have not been individually addressed, all rejections made towards these dependent claims are maintained due to a lack of reply by the Applicants in regards to distinctly and specifically pointing out the supposed errors in the Examiner's prior office action (37 CFR 1.111). The Examiner asserts that the Applicants only argue that the dependent claims should be allowable because the independent claims are unobvious and patentable over the prior art.
Claim Objections
Claims 1, 14, and 18 is objected to because of the following informalities: Claims 1, 14, and 18 recite the acronym “SCM-company” without further defining the term which renders the claim unclear and indefinite. The use of acronyms is permitted however they must first be defined whenever the acronym is first claimed in order to establish the proper metes and bounds of the claim. Appropriate correction is required.
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 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims are directed to a process (an act, or series of acts or steps), a machine (a concrete thing, consisting of parts, or of certain devices and combination of devices), and a manufacture (an article produced from raw or prepared materials by giving these materials new forms, qualities, properties, or combinations, whether by hand labor or by machinery). Thus, each of the claims falls within one of the four statutory categories (Step 1). The claims recite a method (process) and apparatus, however, the claim(s) recite(s) recommending at least one optimized set of partner entities based upon obtained greenhouse gas consumption or production which is an abstract idea of organizing human activities.
The limitations of “obtaining information relating to one or more of a greenhouse gas (GHG) consumption or a GHG production associated with a plurality of entities associated with a GHG-offset marketplace; receiving a request regarding a desired transaction related to the GHG-offset marketplace; and recommending, based on the request and the information relating to the one or more of the GHG consumption or the GHG production, at least one optimized set of partner entities in the plurality of entities for fulfilling the request, wherein recommending comprises selecting supply chain partners based on quantitative and qualitative data that is trackable and traceable to provide a single truth and evidence-based tracking of environmental activities among the supply chain partners, and wherein recommending comprises building optimization models for recommending partners based on current reported GHG emissions of partners that best achieve a GHG emission reduction target using probabilistic estimate modeling with probability learning operations that search global and nearest-best possible solution sets and apply rules to estimate marginal and joint probability distributions” as drafted, is a process that, under its broadest reasonable interpretation, covers organizing human activities--fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) but for the recitation of generic computer components (Step 2A Prong 1). Method claim 1 is devoid of structure whatsoever and thus can only be directed towards an abstract idea. Next, regarding claims 14 and 18, other than reciting “An apparatus comprising: a memory; and at least one processor coupled to the memory and, based at least in part on information stored in the memory, the at least one processor is configured to:,” in claim 14 or “A non-transitory computer-readable medium storing computer executable code, the computer executable code when executed by a processor causes the processor to:” in claim 18 nothing in the claim element precludes the step from the methods of organizing human interactions grouping. For example, but for the “An apparatus comprising: a memory; and at least one processor coupled to the memory and, based at least in part on information stored in the memory, the at least one processor is configured to:,” in claim 14 or “A computer-readable medium storing computer executable code, the computer executable code when executed by a processor causes the processor to:” language, “obtaining,” “receiving,” and “recommending” in the context of this claim encompasses the user manually obtaining greenhouse gas consumption or production information and providing a recommendation for optimized partner entities based upon a request which is business relation/fundamental economic practice/commercial or legal interaction. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as one of the methods of organizing human activities, but for the recitation of generic computer components, then it falls within the “Organizing Human Activities” grouping of abstract ideas. Accordingly, the claim(s) recite(s) an abstract idea (Step 2A, Prong One: YES).
This judicial exception is not integrated into a practical application (Step 2A Prong Two). Method claim 1 is devoid of structure whatsoever and thus cannot integrate the claims into a practical application. Next, regarding claims 14 and 18, the claims only recites one additional element – using a processor to perform the steps. The processor the steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of electronic data storage, query, and retrieval, some of the most basic functions of a computer) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Specifically the claims amount to nothing more than an instruction to apply the abstract idea using a generic computer or invoking computers as tools by 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 - see MPEP 2106.04(d)(I) discussing MPEP 2106.05(f). The recitation of “wherein the information is extracted from multiple specialized blockchains including an SCM-company-operated GHG accounting blockchain, an SCM-company-operated carbon offset commitment blockchain, and third party operated mandatory GHG emissions reporting blockchain” in the limitations also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “wherein the information is extracted from multiple specialized blockchains including an SCM-company-operated GHG accounting blockchain, an SCM-company-operated carbon offset commitment blockchain, and third party operated mandatory GHG emissions reporting blockchain” limits the identified judicial exceptions, this type of limitation merely confines the use of the abstract idea to a particular technological environment (blockchains for storing data) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Accordingly, the combination of these additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea, even when considered as a whole (Step 2A Prong Two: NO).
The claim does not include a combination of additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B). Method claim 1 is devoid of structure whatsoever and thus cannot amount to significantly more. As discussed above with respect to integration of the abstract idea into a practical application (Step 2A Prong 2), the combination of additional elements of using a processor to perform the steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Similarly, only limiting the abstract idea to a particular environment does not amount to significantly more. Therefore, when considering the additional elements alone, and in combination, there is no inventive concept in the claim. As such, the claim(s) is/are not patent eligible, even when considered as a whole (Step 2B: NO).
Claims 2-3, 5-9, 12, 15, 17, and 19-20 are dependent on claims 1, 14, and 18 and include all the limitations of claims 1, 14, and 18. Therefore, claims 2-3, 5-9, 12, 15, 17, and 19-20 recite the same abstract idea of “recommending at least one optimized set of partner entities based upon obtained greenhouse gas consumption or production.” The claim(s) recite(s) the additional limitation(s) further including mathematical concepts (model generation, usage, performing optimization functions) which is not an inventive concept that meaningfully limits the abstract idea. Again, as discussed with respect to claims 1, 14, and 18, the claims are simply limitations which are no more than mere instructions to apply the exception using a computer or with computing components. Accordingly, the additional element(s) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Even when considered as a whole, the claims do not integrate the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 4 and 10 are dependent on claim 1 and include all the limitations of claim 1. Therefore, claims 4 and 10 recite the same abstract idea of “recommending at least one optimized set of partner entities based upon obtained greenhouse gas consumption or production.” The claim(s) recite(s) the additional limitation(s) further limiting the entity data which is still directed towards the abstract idea previously identified and is not an inventive concept that meaningfully limits the abstract idea. Again, as discussed with respect to claims 1, 14, and 18, the claims are simply limitations which are no more than mere instructions to apply the exception using a computer or with computing components. Accordingly, the additional element(s) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Even when considered as a whole, the claims do not integrate the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 13 is dependent on claim 1 and include all the limitations of claim 1. Therefore, claim 13 recite the same abstract idea of “recommending at least one optimized set of partner entities based upon obtained greenhouse gas consumption or production.” The claim(s) recite(s) the additional limitation(s) further limiting the environment for which some of the data is stored which is not an inventive concept that meaningfully limits the abstract idea. Again, as discussed with respect to claims 1, 14, and 18, the claims are simply limitations which are no more than mere instructions to apply the exception using a computer or with computing components. Accordingly, the additional element(s) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Even when considered as a whole, the claims do not integrate the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 1-20 are therefore not eligible subject matter, even when considered as a whole.
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 (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.
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 for establishing a background for determining obviousness under 35 U.S.C. 103 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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Roberts (US PG Pub. 2009/0171722) further in view of Gruber et al. (US PG Pub. 2022/0343229) and Kumar et al. (US PG Pub. 2022/0327538)
As per claims 1, 14, and 18, Roberts discloses method, an apparatus comprising: a memory; and at least one processor coupled to the memory and, based at least in part on information stored in the memory, the at least one processor is configured to, and a non-transitory computer-readable medium storing computer executable code, the computer executable code when executed by a processor causes the processor to:, the method comprising (hardware, computers, servers, website, Roberts ¶70):
obtaining information relating to one or more of a greenhouse gas (GHG) consumption or a GHG production associated with a plurality of entities associated with a GHG-offset marketplace (upstream and downstream activities of suppliers, Roberts ¶81-¶86; stored in a database, ¶73);
receiving a request regarding a desired transaction related to the GHG-offset marketplace (search for company or products with Greenstar ratings, Roberts ¶33-¶34 and ¶40); and
recommending, based on the request and the information relating to the one or more of the GHG consumption or the GHG production, at least one optimized set of partner entities in the plurality of entities for fulfilling the request (The inclusion of upstream and downstream data in the calculation of the rating of member company 50 provides an incentive for member company 50 to pick the most efficient suppliers of goods and services, both upstream and downstream. The inclusion of the optional product-related rating described above may provide an additional incentive for member company 50 to develop and produce the most energy-efficient products possible, Roberts ¶86; higher rated supplier, based on other companies in their sector, ¶90; ranking for entity, algorithm, weightings, intensity metrics, ¶182).
Roberts does not expressly disclose wherein the information is extracted from multiple specialized blockchains including an SCM-company-operated GHG accounting blockchain, an SCM-company-operated carbon offset commitment blockchain, and third party operated mandatory GHG emissions reporting blockchain; wherein recommending comprises selecting supply chain partners based on quantitative and qualitative data that is trackable and traceable to provide a single truth and evidence-based tracking of environmental activities among the supply chain partners, and wherein recommending comprises building optimization models for recommending partners based on current reported GHG emissions of partners that best achieve a GHG emission reduction target using probabilistic estimate modeling with probability learning operations that search global and nearest-best possible solution sets and apply rules to estimate marginal and joint probability distributions.
However, Gruber teaches:
wherein the information is extracted from multiple specialized blockchains including an SCM-company-operated GHG accounting blockchain, an SCM-company-operated carbon offset commitment blockchain, and third party operated mandatory GHG emissions reporting blockchain (record transactional data in distributed ledger, Gruber ¶9 and ¶59; As the product continues to traverse the supply chain, the CI score of that product may be updated. A “product” that traverses through the supply chain may refer to a single product or a bundle of products that is being manufactured. Certain CI score formulas may be applied to single products, whereas other CI score formulas may be applied to bundled products. As the product enters the next step in the supply chain, the system receives input data at the next supply chain step (e.g., supply chain Step #2, Step #3, . . . Step #N, etc.) at step 414 in method 400. As mentioned with regard to step 406, the data received may comprise state information regarding the participant's processes in the supply chain. In addition to the state information that may be recorded by the stakeholder itself (or a trusted third-party, e.g., auditor), the system may also receive information from pre-installed IoT devices that may be attached to certain machines or areas where processing is occurring, ¶66; use of blockchains, ¶10 and ¶102; in order to offer a one single truth source, ¶97; regulations and laws in certain jurisdictions, ¶96) (Examiner interprets the use of trusted third-party data sources and auditors as the specialized blockchains);
wherein recommending comprises selecting supply chain partners based on quantitative and qualitative data that is trackable and traceable to provide a single truth and evidence-based tracking of environmental activities among the supply chain partners, and wherein recommending comprises building optimization models for recommending partners based on current reported GHG emissions of partners that best achieve a GHG emission reduction target (For example, data collection module 315 may have access to data in one or more external systems, such as content systems, distribution systems, marketing systems, supply chain participant/entity/partner profiles or preference settings, authentication/authorization systems, device manifests, or the like. Specifically, data collection module 315 may have access to at least one database of historical CI score data and up-to-date CI score data and analyses (e.g., analyses regarding the environmental impact—including predicted CI scores for particular products—of applying certain processes in a supply chain, etc.), which may inform the system as to which step within a supply chain a certain product should be shipped to next that may provide the product's CI score the most optimal chance of lowering its CI score or, alternatively, limiting the increase in the CI score as compared applying other processes to the product, Gruber ¶48; Smart contract module 320 and CI calculation module 325 may be configured to communicate with machine-learning (ML) suggestion module 330, and vice versa. ML suggestion module 330 may rely on information provided by smart contract module 320 and CI calculation module 325 to provide intelligent, machine-learning-model-driven suggestions to certain participants in the supply chain, specifically related to how a participant may alter its processing methods to reduce the CI score of future products. In alternative embodiments, the ML suggestion module 330 may provide real-time suggestions to the system as to which participant in a supply chain a product should be sent to next. For example, at step #3 in a supply chain, a product could be further processed at plant A or plant B. Based on the product's present CI score and the historical CI scores and state information from plant A and plant B, the ML suggestion module 330 may intelligently suggest to the system which plant (plant A or plant B) the product should be shipped to next for processing, based on a predictive output that one plant has a higher likelihood of producing a lower CI score for that particular product at the present time than the other plant, ¶54; In some aspects, ML suggestion module 330 may be configured with a pattern recognizer, wherein the pattern recognizer may pick up on certain historical trends to identify certain patterns (e.g., certain inputs typically reduce a CI score by X%, certain inputs typically increase a CI score by Y%, etc.), ¶57; train a machine learning model, ¶58; The output of the ML model(s) analysis is an intelligent suggestion, which is generated at step 612. The intelligent suggestion may suggest to a participant in the supply chain (or a third-party operator/controller) certain manufacturing/processing changes that could potentially lower a CI score in the future. Specifically, for example, after the product receives its intermediate CI score after completing step N (or step N+1) in the supply chain, the ML model output may provide a suggestion to that participant in the supply chain for tweaking its processes to potentially obtain a lower CI score in the next iteration through the supply chain. Alternatively, the ML model may generate an intelligent suggestion for the next step in the supply chain. For instance, after receiving data associated with the present CI score, the intelligent suggestion generated by the ML model(s) at step 612 may suggest to the supply chain participants (and/or operator, controller, etc.) where to send the product next in the supply chain. For example, if multiple participants in a supply chain are available to receive and process a product in the next step in the supply chain, the system described herein may analyze and assess each of these participants to determine which participant is the most optimal for the current product based on the current product's CI score. In one example, participant A in the supply chain may be deploying state-of-the-art green technology in its processing techniques, whereby a lower CI score is more likely to be obtained than participant B who may be applying fossil-fuel-based machinery for processing. If the CI score of the product at a certain step in the supply chain is above a certain threshold, the ML model(s) output may intelligently suggest that the product be provided to participant A (instead of participant B) for the next step in the supply chain. Conversely, if the present CI score is already sufficiently low, the ML model(s) may intelligently suggest that the product be provided to participant B (instead of participant A) because, among other reasons, participant B may have cheaper processing costs than participant A—and although participant B will likely increase the CI score, the increase (based on historical data from participant B) will not be enough to substantially affect the final CI score of the product, ¶75-¶76; receive algorithmic information related to a GREET model, ¶97) (Examiner interprets the intelligent suggestions from the machine learning models as the ability to recommend supply chain partners based upon the qualitative and quantitative data for a nearest best possible solution).
Both the Roberts and the Gruber references are analogous in that both are directed towards/concerned with reporting and understanding carbon and greenhouse gas output and offsets thereof. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to use Gruber’s distributed ledger aspects in Roberts’ Greenstar system to improve the system and method with reasonable expectation that this would result in a greenhouse gas/carbon footprint offset management system more securely.
The motivation being that one issue with carbon credits is the inefficiency in exchanging/trading the carbon credits. Buyers and sellers are usually unable to verify and validate the true value of a carbon credit and, at the same time, audit the carbon credit's value (i.e., determine that the carbon credit is derived from a legitimate environmentally conscious and carbon-friendly process). Buyers and sellers also usually must wait several days before their carbon credits are transferred and settled. As such, there is a need to more efficiently and transparently verify the value of a carbon credit and transfer it between entities (Gruber ¶8).
While the combination of Roberts and Gruber disclose the use of machine learning and optimization techniques, the combination does not expressly disclose doing so using probabilistic estimate modeling with probability learning operations that search global and nearest-best possible solution sets and apply rules to estimate marginal and joint probability distributions.
However, Kumar teaches using probabilistic estimate modeling with probability learning operations that search global and nearest-best possible solution sets and apply rules to estimate marginal and joint probability distributions (probability distributions for predictions, Kumar ¶47; machine learning modelling, probability distributions, carbon footprint, ¶61).
The Roberts, Gruber, and Kumar references are analogous in that both are directed towards/concerned with reporting and understanding carbon and greenhouse gas output and offsets thereof. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to use Kumar's modelling techniques in Gruber’s and Roberts’ Greenstar system to improve the system and method with reasonable expectation that this would result in a greenhouse gas/carbon footprint offset management system more securely.
The motivation being that one issue with carbon credits is the inefficiency in exchanging/trading the carbon credits. Buyers and sellers are usually unable to verify and validate the true value of a carbon credit and, at the same time, audit the carbon credit's value (i.e., determine that the carbon credit is derived from a legitimate environmentally conscious and carbon-friendly process). Buyers and sellers also usually must wait several days before their carbon credits are transferred and settled. As such, there is a need to more efficiently and transparently verify the value of a carbon credit and transfer it between entities (Gruber ¶8).
In addition, the Examiner asserts that claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure. However, examples of claim language, although not exhaustive, that may raise a question as to the limiting effect of the language in a claim are: (A) "adapted to" or "adapted for" clauses; (B) "wherein" clauses; and (C) "whereby" clauses (See MPEP 2111.04). In the instant case, the recited wherein clause " to provide a single truth and evidence-based tracking of environmental activities among the supply chain partners " is not a positive method step as it do not require any actual positive recited claim steps to be performed; nor does it modify any of the positively claimed method steps and is merely reciting the intended use. Similarly, the recited wherein clause is not a positive system element since it doesn’t structurally limit the system and merely describes the intended use of the system and/or the intended result of the use of the system.
As per claim 2, Roberts, Gruber, and Kumar disclose as shown above with respect to claim 1. Roberts further discloses generating a model representing a unified GHG marketplace based on the information relating to the one or more of the GHG consumption or the GHG production (The upper portion 150 of FIG. 1 shows the process by which the simplified green rating 165 is obtained. First, data from database 135 is fed into the Greenstar algorithm 152. The algorithm may be any algorithm which takes into account, with the proper weights, all of the data collected regarding member company 50 and all other sector companies within the same sector as member company 50. The algorithm may in fact be different for different industry sectors. In its simplest form for instance, the algorithm in box 152 may comprise ranking of all member firms in a sector by net emissions expressed as a rate per unit size, and awarding ratings indicia to member firms according to their percentile ranking, for example giving a full rating for those ranked in the top 20%. In boxes 154 and 156, the results of executing algorithm 152 using data extracted from database 135 for member company 50 is generated. The raw score 154 may be turned into a percentage score 156 which represents the percentage of the maximum score in the sector which has been achieved by member company 50. The percentage score 156 is then turned into a simplified rating 165 which consists of one or more stars or other symbolized indicia being awarded to the member firm 50. The number of indicia in the preferred embodiment is zero to three but it is understood that the number of indicia used may vary between zero and any number, Roberts ¶76).
As per claim 3, Roberts, Gruber, and Kumar disclose as shown above with respect to claim 2. Roberts further discloses wherein the model comprises: a first set of data relating to a set of commitments made by a set of entities in the plurality of entities; a second set of data relating to monitoring and extracting -GHG emission that is reported by each particular entity in the set of entities - to an external Mandatory/Statutory GHG emission reporting agency; and a third set of data used related to predicting one of GHG production or GHG consumption for each entity in the plurality of entities, wherein the third set of data is based on at least one of the first set of data and the second set of data (upstream and downstream activities of suppliers, Roberts ¶81-¶86; stored in a database, ¶73; scope 1 emissions for carbon auditing and regulatory submission, ¶9-¶10; scope 3 including the up and downstream activities, ¶13-¶15; improvement velocity metric, ¶157-¶158) (Examiner notes the scope 2 emissions as the equivalent to the first set of data; the scope 1 emissions as the equivalent to the second set of data; the improvement velocity metric as the equivalent to the third set of data).
As per claim 4, Roberts, Gruber, and Kumar disclose as shown above with respect to claim 1. Roberts further discloses wherein the request relates to a supply chain activity associated with one or more of a product, a project, an enterprise, or a service (products, services, supply chain environment, Roberts ¶216-¶217).
As per claim 5, Roberts, Gruber, and Kumar disclose as shown above with respect to claim 4. Roberts further discloses wherein the request indicates a target GHG emission parameter associated with a desired GHG offset (The rating itself may be based on the company's operational carbon footprint, taking into account only Scope 1 and 2 emissions, or, optionally, may include Scope 3 emissions, such as upstream and downstream carbon footprints and employee or consumer travel. It may also take other, sector-specific factors into account, which may include recycling practices and other pollutants emitted which are not readily reducible to equivalent units of carbon dioxide, Roberts ¶27; The analysis for such a scorecard could be performed by the Greenstar company or a third party, and the scorecard could cover, but is not limited to, such aspects as: degree of carbon data public disclosure, employee engagement schemes, consumer education drives, efforts made to preferentially promote lower carbon technologies (e.g. phasing out tungsten lightbulbs) or actively carbon-positive technologies (such as solar panels), investment practices of a company's pension funds, and so on. Optionally, non-climate related environmental considerations such as recycling practices, renewable materials and water usage could be incorporated into a Greenstar rating. Scorecard measures, when applied within a sector, would lead to a supplementary ranking, to be incorporated in a similar manner to the ranked product efficiency measures, and are shown to enter the ratings process in Module 11 of FIG. 21, and also weighted relative to the other ratings elements. In the early years of implementation of the Greenstar system, the weighting, W.sub.SC, applied to the scorecard component of the rating may be a relatively high percentage, to account for the fact that the accurate emissions data for the rigorous numerical emissions data part of the algorithm (i.e., Modules 3-7) of sufficient quality may not yet be widely available, while the scorecard judgments may be easier to make using publicly available data. For instance, if the scorecard applies a certain score, such as a points score out of a maximum 10 for a certain factor such as rating levels of transparency of public disclosure of emissions measurement systems, or scoring the policy stance of a rated company, the information is relatively easy to find in the public domain. In contrast, numerical emissions data is treated by some companies as confidential data and may be hard to attain, or may simply not be being measured yet in some sectors. As such, for some sectors, the initial weightings may optionally even be 100% for the scorecard system in the early years of the program, meaning that scorecard points make up the only source of data leading to a rating, ¶205; master discount rates for offsets, for entity’s portfolio, ¶61-¶62 and ¶151-¶156).
As per claim 6, Roberts, Gruber, and Kumar disclose as shown above with respect to claim 1. Roberts further discloses wherein the at least one optimized set of partner entities comprises at least one of a manufacturing entity, a construction entity, a transportation entity, a distributor entity, or a retail entity (entity within a sector, Roberts ¶156-¶157 manufacturer, ¶198) (Examiner notes the industry specific sectors to include manufacturing, construction, transportation, distribution and retail entities).
As per claim 7, Roberts, Gruber, and Kumar disclose as shown above with respect to claim 1. Roberts further discloses wherein recommending the at least one optimized set of partner entities comprises a first optimization of an objective function based on a set of reported GHG footprints (GFs) of candidate partner entities in the plurality of entities (The rating itself may be based on the company's operational carbon footprint, taking into account only Scope 1 and 2 emissions, or, optionally, may include Scope 3 emissions, such as upstream and downstream carbon footprints and employee or consumer travel. It may also take other, sector-specific factors into account, which may include recycling practices and other pollutants emitted which are not readily reducible to equivalent units of carbon dioxide, Roberts ¶27).
As per claim 8, Roberts, Gruber, and Kumar disclose as shown above with respect to claim 7. Roberts further discloses wherein recommending the at least one optimized set of partner entities further comprises computing a tradeoff factor, wherein, for a tradeoff factor above a threshold value, data is retrieved regarding at least one candidate partner entity regarding a potential GF offset by the at least one candidate partner entity (In yet another embodiment, shown as a detailed system module in FIG. 14, the MDR may be calculated based on actual emissions of the entity to be rated against other entities in its sector. For such a calculation, it is likely that only Scope 1 and Scope 2, or possibly, Ops-plus emissions will be taken in to account. This provides a proxy measure for those entities who have already made reductions by investing in efficiency measures to address the `low hanging fruit` emissions. In this embodiment, the entities leading their field on actual emissions performance would get the most favorable MDR and hence, the highest reward from the offsets purchased. If the entities who lag behind the leaders on the actual emissions catch or overtake their competitors by having addressed their own `low-hanging-fruit` emissions, they will be eligible for the more favorable discount rates for offsets. FIG. 14 shows the process by which the MDR for offsets is calculated. The input data for this process is shown in box 1401 and includes information regarding the actual emissions of the entities as well as the normalizing data. It is contemplated that only the actual emissions, that is Scope 1, Scope 2 and possibly int/int emissions of the company will be used to calculate the intensity metric rankings. The normalizing data will be dependent upon the intensity metrics which are selected for use with the particular sector, and may include, for example, such things as sales dollars, number of employees, etc. In box 1402, a ranking of the entities within the sector for each intensity metric is calculated, and, in box 1404, the calculation is performed to derive an outcome value for each entity in the sector, based upon each entity's ranking within each of the intensity metrics. The intensity measures may be weighted. In box 1406, the outcome values are converted into an MDR for offsets according the equations shown in box 1410, in FIG. 13. The output in 1408 is a listing of each entity in the sector and its MDR as shown in box 1412., Roberts ¶159-¶160).
As per claims 9 and 16, Roberts, Gruber, and Kumar disclose as shown above with respect to claims 7 and 14. Roberts further discloses wherein the first optimization of the objective function comprises, for a current set of candidate partner entities, iteratively, (1) evaluating the objective function based on the current set of candidate partner entities, (2) determining whether a different set of candidate partner entities may improve the objective function, and (3) one of (i) ending the first optimization based on determining that the different set of candidate partner entities does not improve the objective function or (ii) selecting an updated set of candidate partner entities based on determining that the different set of candidate partner entities may improve the objective function (Entities will be asked a series of questions which will eventually lead to the placement of the entity into a tightly defined sector. The questions are envisioned to change based on a specific entity's progression through the questions. For example, if the entity is asked to select a supersector and combined oil and gas is selected as an industry sector, the question regarding the geographical scope of the business may not be asked because it may be assumed that all entities in the combined oil and gas sector are global in their geographical scope. It should be noted that geographical scope may relate to areas of production or to areas of purchase by end user of the company's products, or both. There may also be entities that fit into more than one tightly defined sector. For example, a retailer may sell both clothing and groceries. The sector into which the entity is placed may be determined by determining which percentage of overall sales come from a particular type of goods. For example, if 70% of the retailers sales come from clothing and 30% of the sales of the entity comes from other goods, such as groceries, it may make sense to place the entity into a sector with other clothing retailers. The exact cut-offs for these `best-fit` placements into sectors may be determined on a sector-by-sector basis and translated into guidelines which may change over time in the light of new information and changes in business models, Roberts ¶126; sectorization, ¶128).
As per claim 10, Roberts, Gruber, and Kumar disclose as shown above with respect to claim 1. Roberts further discloses wherein the at least one optimized set of partner entities comprises multiple sets of optimized partner entities (best-fit, sectorization, Roberts ¶126 and ¶128).
As per claim 11, Roberts and Gruber disclose as shown above with respect to claim 1. Roberts further discloses receiving a selection of partner entities associated with a first optimized set of partner entities in the at least one optimized set of partner entities (higher rated supplier, based on other companies in their sector, Roberts ¶90; ranking for entity, algorithm, weightings, intensity metrics, ¶182).
As per claim 12, Roberts, Gruber, and Kumar disclose as shown above with respect to claim 11. Roberts further discloses providing a set of carbon credit commitments related to the selection of the partner entities to update the information relating to the one or more of the GHG consumption or the GHG production associated with the GHG-offset marketplace (Module 3 (Offset Quality Discount Rates) in FIG. 8 calculates credits for any offsets purchased by the entity. The credits may be discounted based on a discounting system which takes into consideration, among other factors, the quality of the offsets being purchased, and the `delivery-risk` of forward-purchased emissions credits. The discount rate which is applied to offsets purchased will account for lower quality or higher quality of the offsets by discounting those offsets of lower quality. Likewise the discount rate will further discount those advance-purchased credits with the highest risk ratio, Roberts ¶151).
As per claim 13, Roberts, Gruber, and Kumar disclose as shown above with respect to claim 12. Gruber further teaches wherein the set of carbon credit commitments is recorded in a blockchain ledger to provide a standardized, single truth, and traceable GHG marketplace model across different actors and different components of the supply chain for each actor, wherein the blockchain ledger enables locking and escrowing of carbon credits when a partner is accepted (record transactional data in distributed ledger, Gruber ¶9 and ¶59; FIG. 13 illustrates an example environment for generating and trading a CI token using, at least in part, the Corda® blockchain development platform available from R3 Ltd. In this specific implementation, environment 1300, referred to as “Verity”, combines a large and transparent voluntary carbon credit market with a supply-management system that ensures the reliability of low, neutral, and/or negative carbon intensity of the production of materials through an immutable and automated audit using blockchain technology. Verity's blockchain-based system offers one single source of truth across production value chains, wherein each economic actor interacts with other economic actors in the system. This interaction allows all parties to record and manage agreements amongst themselves in a secure, consistent, reliable, private, and auditable manner, ¶97) (Examiner interprets the immutable audit for carbon credits as the locking and escrowing of carbon credits within a blockchain).
Both the Roberts and the Gruber references are analogous in that both are directed towards/concerned with reporting and understanding carbon and greenhouse gas output and offsets thereof. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to use Gruber’s distributed ledger aspects in Roberts’ Greenstar system to improve the system and method with reasonable expectation that this would result in a greenhouse gas/carbon footprint offset management system more securely.
The motivation being that one issue with carbon credits is the inefficiency in exchanging/trading the carbon credits. Buyers and sellers are usually unable to verify and validate the true value of a carbon credit and, at the same time, audit the carbon credit's value (i.e., determine that the carbon credit is derived from a legitimate environmentally conscious and carbon-friendly process). Buyers and sellers also usually must wait several days before their carbon credits are transferred and settled. As such, there is a need to more efficiently and transparently verify the value of a carbon credit and transfer it between entities (Gruber ¶8).
In addition, the Examiner asserts that claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure. However, examples of claim language, although not exhaustive, that may raise a question as to the limiting effect of the language in a claim are: (A) "adapted to" or "adapted for" clauses; (B) "wherein" clauses; and (C) "whereby" clauses (See MPEP 2111.04). In the instant case, the recited wherein clause "to provide a standardized, singl