DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim(s) 1-4, 6-14, and 16-21 are pending for examination. Claim(s) 1, 11, and 20 have been amended. Claim 21 is newly added. This action is Non-Final.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 8/10/2026 has been entered.
Response to Arguments
Applicant's arguments filed 8/10/2026 with respect to the 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant Argues: The claims do not recite an abstract idea under Prong One.
Applicant submits that the claims are patent-eligible as they do not recite a judicially recognizable exception or grouping of abstract ideas enumerated under Step 2A, Prong One.
In the Office Action, the Office alleged that elements of the claims "fall within the "Certain Methods of Organizing Human Activity' grouping of abstract ideas in that they recite 'commercial interactions' or 'legal interactions' include agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations." Id at 14. Applicant respectfully disagrees.
[...]
At least these elements are directed to technical steps for training a model, rather than any alleged commercial or legal interaction. In DDR Holdings v. Hotels.com, the Federal Circuitdetermined that claims directed to combining one webpage with a third-party merchant's webpage into a hybrid webpage was patent-eligible. 773 F.3d 1245, 1259-60 (Fed. Cir. 2014). In DDR, while the Court recognized that the claims addressed a business challenge, the challenge was "particular to the internet" and the claimed solution was "necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer networks." See id. at 1257. Like the claims in DDR, even assuming, arguendo, that the claims address a challenge relating to commercial transactions (which Applicant does not concede), the challenge is "particular to the Internet," and "the claimed solution is necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer networks." For example, Applicant's Specification explains emissions-related problems rooted in internet technology, including how databases store data in non-uniform formats. See Specification, [0004]. The claims reflect a solution including extracting data with a trained machine learning model, and accessing an emissions factor from a database based on the model, which is also rooted in internet technology. Therefore, contrary to the Office's assertion, like the claims in DDR, the pending claims are necessarily rooted in computer technology, and do not fall into alleged the category of abstract ideas.
.
Examiner’s Response: The examiner respectfully disagrees. The claims as presented, limitation-by-limitation, would be enumerated under “Certain Methods of Organizing Human Activity” grouping of abstract ideas as the claims recite “commercial interactions" or "legal interactions" in the form of business relations. The examiner notes as reasonably construed emissions data analysis corresponding to an activity region in order to generate an emissions line item based on structured emissions data and selected emission factor is a form of a business relation. Further, the examiner respectfully notes that the use of trained machine learning model to “extract data” and “access an emission factor from a database” is noted to be an element in the steps that is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, this additional element, even in combination, does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the examiner finds this argument not persuasive.
Applicant Argues: Second, Applicant maintains the arguments in the January 27 Response that the limitation-by-limitation analysis performed by the Office under Step 2A is improper and is inconsistent with the Alice framework articulated in the MPEP. January 27 Response, p. 10. The Office asserts that "[t]he claim as a whole was considered...the machine learning model that is trained and validated is noted to be an element in the steps that is recited at a high-level of generality." Office Action, 5. Applicant disagrees. MPEP § 2106.04(11) states that the "claim as a whole" must be analyzed to determine whether it is directed to a judicial exception. And according to Alice, if the claim as a whole is directed to an abstract idea, the adjudication then may then evaluate the elements on a limitation-by-limitation basis (or ordered combination) to see if they add significantly more. Alice at 2354- 2355 and 2358. By failing to analyze the recited machine learning model under Prong One, the Office fails to analyze the claim as a whole from the outset.
Therefore, under a proper analysis, Applicant respectfully submits that claim 1 should be found eligible at Prong One because the identified claim elements do not recite an abstract idea.
Examiner’s Response: The examiner respectfully disagrees. The claim was considered. See the 35 U.S.C. 101 rejection below. The examiner identified in Step 2A-Prong 2 that the machine learning model that is trained and validated is noted to be an element in the steps that is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, this additional element, even in combination, does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the examiner finds this argument not persuasive.
Applicant Argues: The claims integrate the alleged abstract idea into a practical application under Prong Two.
Further, even assuming the claims were to fall within the organizing human activity grouping as asserted (which the Applicant does not concede), the Office alleges that the additional elements "do not integrate the abstract idea into a practical application." Office Action, 4. Applicant respectfully disagrees, and Applicant submits that the claims integrate the alleged abstract idea into a practical application.
First, Applicant maintains that claim 1 is eligible for reasons analogous to the claims held eligible in Ex parte Desjardins. In Desjardins (which was designated precedential on November 4, 2025), the Appeals Review Panel (ARP) evaluated the claims under Prong Two by looking to the Specification and identifying improvements for training of a machine learning model such as "'effectively learn[ing] new tasks in succession" and allowing "artificial intelligence (AI) systems to 'us[e] less of their storage capacity."' Decision on Request for Rehearing, Appeal 2024-000567 (Sep. 26, 2025) at 9. The ARP held that,
[w]hen evaluating the claim as a whole, we discern at least the
following limitation of independent claim 1 that reflects the
improvement: 'adjust the first values of the plurality of
parameters to optimize performance of the machine learning
model on the second machine learning task while protecting
performance of the machine learning model on the first machine
learning task.' We are persuaded that constitutes an improvement
to how the machine learning model itself operates.
The ARP stated that "[w]e determine that although independent claim 1 may recite an abstract idea, it is not directed to an abstract idea. Instead, we determine that independent claim 1, when considered as a whole, integrates an abstract idea into a practical application." Id at 10.
Similarly, Applicant's Specification describes improvements to machine learning models, such as "improv[ing] upon the functioning of machine learning systems by improving the training of such systems and their ability to handle emissions data" including "improvements to the training and inference of machine learning models used to standardize emissions data." Specification, [0028]. These improvements are reflected in claim 1, as claim 1 recites "a machine learning model configured to standardize data, wherein the machine learning model has been trained with emissions training data and one or more emissions validation datasets by: validation, with a validation model, of an output of the machine learning model against the one or more emissions validation datasets, and adjustment of at least one parameter of the machine learning model based on an output of the validation model and the emissions training data." Thus, as a whole, claim 1 provides improvements to how the recited machine learning model itself is trained, analogous to the eligible claims of Desjardins. Accordingly, Applicant submits claim 1 is eligible under Prong 2.
In response to Applicant's previous arguments, the Office asserts that "the machine learning model that is trained and validated is noted to be an element in the steps that is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and merely invoke such additional elements as a tool to perform the abstract idea." Office Action, 6. Applicant disagrees. The Office's cursory analysis, without any further explanation, is precisely the type of reasoning the ARP rejected in Desjardins, where it warned against "essentially equat[ing] any machine learning with an unpatentable 'algorithm' and the remaining additional elements as 'generic computer components,' without adequate explanation." Decision on Request for Rehearing at 9. Applicant submits that the recited model is not recited at a high-level of generality, as claim 1 recites in detail that the machine learning model is "configured to standardize data, wherein the machine learning model has been trained with emissions training data and one or more emissions validation datasets by: validation, with a validation model, of an output of the machine learning model against the one or more emissions validation datasets, and adjustment of at least one parameter of the machine learning model based on an output of the validation model and the emissions training data."
Examiner’s Response: The examiner respectfully disagrees. The examiner notes that the machine learning model as claimed that is “...trained with emissions training data and one or more emissions validation datasets by: validation, with a validation model, of an output of the machine learning model against the one or more emissions validation datasets, and adjustment of at least one parameter of the machine learning model based on an output of the validation model and the emissions training data...” is not found to be an improvement to machine learning models itself. Applicant’s specification ⁋[0070] describes generic training and various generic machine learning algorithms. Thus, the machine learning that is trained and validated is found to be elements in the steps are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, these additional elements, even in combination, do 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.
Applicant Argues: Second, Applicant submits that the claims, which recite the use of multiple models, are eligible. In Ex parte Helenius, the Board reversed the Examiner's rejection of the claims as directed to ineligible subject matter when the claims recited the use of "outputs of models in combination allows for a product path with a higher likelihood of a winning outcome." Ex parte Helenius, Appeal No. 2024-000079, Decision on Appeal at 21-22 (PTAB August 15, 2024). At Prong 2, the Board determined that "claim 1 is directed to an improvement in the technology of generating a sequence in accordance with a product path, and, in particular, the improvement of utilizing a multi-model based recommender in the technological environment of data processing or the field of use of artificial intelligence." Id. at 21-22. Claim 1 recites that the machine learning model is "configured to standardize data, wherein the machine learning model has been trained with emissions training data and one or more emissions validation datasets by: validation, with a validation model, of an output of the machine learning model against the one or more emissions validation datasets, and adjustment of at least one parameter of the machine learning model based on an output of the validation model and the emissions training data." Thus, like the claims in Ex Parte Helenius, Applicant's present claims reflect the use of outputs of models in combination, and in particular, using the output of one model to improve the training of another model. Therefore, Applicant submits that the claims demonstrate integration into a practical application.
Examiner’s Response: The examiner respectfully disagrees. The examiner notes that the machine learning model as claimed that is “...trained with emissions training data and one or more emissions validation datasets by: validation, with a validation model, of an output of the machine learning model against the one or more emissions validation datasets, and adjustment of at least one parameter of the machine learning model based on an output of the validation model and the emissions training data...” is not found to be an improvement to machine learning models itself. Applicant’s specification ⁋[0070] describes generic training and various generic machine learning algorithms. Thus, the machine learning that is trained and validated is found to be elements in the steps are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, these additional elements, even in combination, do 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.
Applicant Argues: Third, Applicant maintains that the claims are analogous to Example 42, and disagrees with the Office's assertion that "Example 42's limitations are not analogous to that of the claims of the Instant Application." Office Action, 7. Again, the Office fails to provide any reasoning on why Example 42 is not applicable.
For the above reasons, claim 1 demonstrates integration into a practical application.
Examiner’s Response: The examiner respectfully disagrees. While Example 42 claim discusses converting to a standardized format, Example 42’s limitations are not analogous to that of the claims of the Instant Application. Thus, the reason for eligibility of Example 42 is not applicable to the claims of the Instant Application. The examiner notes the rejection below follows the proper streamlined analysis and has concluded that the claims as presented are ineligible under 35 U.S.C. 101. Therefore, the examiner finds this argument not persuasive.
Applicant Argues: C. The claims, as a whole, recite significantly more.
As explained above, Applicant submits that the claims are not "directed to" an abstract idea, which means that the claims qualify as patent eligible subject matter, and further analysis under Step 2B is not required. Even if, arguendo, the claims are found to be directed to an abstract idea, Applicant submits that, under Step 2B, the claims, as a whole, recite "significantly more" than the alleged abstract idea.
For example, claim 1 recites "the machine learning model is "configured to standardize data, wherein the machine learning model has been trained with emissions training data and one or more emissions validation datasets by: validation, with a validation model, of an output of the machine learning model against the one or more emissions validation datasets, and adjustment of at least one parameter of the machine learning model based on an output of the validation model and the emissions training data." Applicant submits that by utilizing a validation model to improve the training of a machine learning model, claim 1 reflects non-conventional activity for training a machine learning model used to select emissions factors. Thus, the recited claim elements add a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is present that an inventive concept may be present.
In view of the foregoing remarks and the amended claims, Applicant respectfully submits that the claims are patent-eligible under 35 U.S.C. § 101. As explained, amended claims 1, 11, and 20 do not recite an abstract idea. To the extent the Office disagrees, Applicant submits that the amended claims integrates the alleged abstract ideas into a practical application and recite significantly more than the alleged abstract ideas. The dependent claims are patent- eligible at least by virtue of their dependence from a patent-eligible independent claim as well as the additional features recited therein. Accordingly, Applicant respectfully requests withdrawal of the rejection under 35 U.S.C. § 101.
Examiner’s Response: The examiner respectfully disagrees. The examiner has concluded that claim is not directed to an improvement to the functioning of a computer or to any other technology or technical field, under Step 2A. Thus, turning to Step 2B the examiner respectfully notes that the additional elements of for claim 1, and for similar claim(s) 11 and 19, i.e., recite i.e., system w/ processor, medium, instructions and a use of machine learning module that was trained with data and validated with data by use of a validated model (i.e., adjusted); thus, amounts to no more than mere instructions to apply the exception using a generic computer component and do not add anything that is not already present when they are considered individually or in combination. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, under Step 2B, there are no meaningful limitations transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself. See MPEP 2106.05. While preemption is the concern underlying the judicial exceptions, it is not a standalone test for determining eligibility. Therefore, the examiner finds this argument not persuasive.
Of note - The examiner has not indicated such elements are well-understood, routine, or conventional; therefore, the evidentiary requirement under Berkheimer is not applicable.
Applicant's arguments filed 8/10/2026 with respect to the 35 U.S.C. 103 rejection have been considered but they are moot in view of new grounds of rejection.
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.
Claim(s) 1-4, 6-14, and 16-21 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1: claim(s) 1-4, 6-14, and 16-21 are directed to a machine, process, and/or manufacture. Therefore, the claims are directed to statutory subject matter under Step 1 (Step 1: YES). See MPEP 2106.03.
Prong 1, Step 2A: claim 1, and similar claim(s) 11 and 20, taken as representative, recites at least the following limitations that recite an abstract idea:
A
accessing emissions activity data from at least one emissions activity data source, wherein the emissions activity data corresponds to an entity and the at least one emissions activity data source corresponds to an activity region;
extracting structured emissions data from the emissions activity data by applying the emissions activity data to
identifying an emission factor database
accessing an emissions factor database, wherein the emissions factor database contains a plurality of emissions factors;
selecting,
generating an emissions line item based on the structured emissions data and the at least one selected emissions factor.
The above limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(II), in that they recite "commercial interactions" or "legal interactions" include agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations. The broadest reasonable interpretation of these limitations for claim 1, and similar claim(s) 11 and 20, includes accessing emissions activity data from at least one emissions activity data source...; extracting structured emissions data from the emissions activity data...; accessing an emissions factor database containing a plurality of emissions factors; selecting, from the emissions factor database, at least one emissions factor corresponding to the activity region; and generating an emissions line item based on the structured emissions data and the at least one selected emissions factor, thus, claim 1, and similar claim(s) 11 and 20, falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they recite “commercial interactions" or "legal interactions" in the form of business relations.
Accordingly, these claims recite an abstract idea. (Prong 1, Step 2A: YES). The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Prong 2, Step 2A: Limitations that are not indicative of integration into a practical application include: (1) 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 (MPEP 2106.05(f)), (2) Adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)). Claim 1, and for similar claim(s) 11 and 20, recite i.e., system w/ processor, medium, instructions and a use of machine learning module that was trained with data and validated with data by use of a validated model (i.e., adjusted). These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration (see Applicant’s Specification, ⁋[0029] and ⁋⁋ [0058]-[0059] and ⁋ [0070]). These elements in the steps are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, these additional elements, even in combination, do 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.
As such, under Prong 2 of Step 2A, when considered both individually and as a whole, the limitations of claim 1, and for similar claim(s) 11 and 20 are not indicative of integration into a practical application (Prong 2, Step 2A: NO). See MPEP 2106.04(d).
Since claim 1, and similar claim(s) 11 and 20recites an abstract idea and fails to integrate the abstract idea into a practical application, claim 1, and similar claim(s) 11 and 20 is “directed to” an abstract idea under Step 2A (Step 2A: YES). See MPEP 2106.04(d).
Step 2B: The recitation of the additional elements is acknowledged, as identified above with respect to Prong 2 of Step 2A. These additional elements do not add significantly more to the abstract idea for the same reasons as addressed above with respect to Prong 2 of Step 2A.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of for claim 1, and for similar claim(s) 11 and 20, i.e., recite i.e., system w/ processor, medium, instructions and a use of machine learning module that was trained with data and validated with data by use of a validated model (i.e., adjusted); thus, amounts to no more than mere instructions to apply the exception using a generic computer component and do not add anything that is not already present when they are considered individually or in combination. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, under Step 2B, there are no meaningful limitations in claim 1, and similar claim(s) 11 and 20 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (Step 2B: NO). See MPEP 2106.05.
Accordingly, under the Subject Matter Eligibility test, claim 1, and similar claim(s) 11 and 20 is ineligible.
Regarding Claims 2-4, 6-10, 12-14, 15-19, and 21; claims 2-4, 6-10, 12-14, 15-19, and 21 further defines the abstract idea that is present in their respective independent claims and hence are abstract for at least the reasons presented above w/ respect to “Certain Methods of Organizing Human Activity” as the claims recite further concepts of "commercial interactions" or "legal interactions" include agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations i.e., further features related to emissions data analysis. These dependent claim does not include any additional elements that integrate the abstract idea into a practical application; as such elements are recited at a high level of generality such that it amounts not more than mere instructions to apply the exception using a generic computer component (i.e., user interface as used in claims 6 and 16 and training data/updating the machine learning model as used in claims 7-8 and 17-18). Even in combination, these additional elements do not integrate the abstract idea into a practical application and do no not amount to significantly more than the abstract idea itself. Thus, the aforementioned claims are not patent-eligible.
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.
Claim(s) 1-4, 9-14, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Glenn et al. (US 2011/0099489 A1) in view of Umay (US 2023/0162203 A1) and Kahn (US 2022/0358515 A1) and Streit (US 10,938,852 A1) and Sun et al. (US 2017/0011308 A1).
Regarding Claim 1;
A ([0007] – ...computer system for computing emission values...), the system comprising:
at least one processor ([0007]); and
at least one computer-readable medium containing instructions that, when executed by the at least one processor ([0007]), cause the
accessing emissions activity data from at least one emissions activity data source ([0007] and [0046]-[0053] - The input module 30 may receive the raw activity data automatically through connectors or manually through direct user input, spreadsheets defined by templates, supplier bills, the enterprise client 20, the personal client 24, the API 22, the bulk data upload module 46, and the question tree module 48, for example), wherein the emissions activity data corresponds to an entity and the at least one emissions activity data source corresponds to an activity region ([0007] and [0046]-[0053] - The input module 30 is further operable to receive a user or client identifier to associate the raw activity data with the user of the system 10... An activity is an event that generates emissions and occurs relative to a geographic location over a defined period of time... a location where the activity occurred (e.g., Toronto, Mike's office));
extracting structured emissions data from the emissions activity data by applying the emissions activity data to a ([0059]-[0061] The data provisioning module 32 is further operable to convert the raw activity data into standardized activity data and store the standardized activity data in the activity database 42)
identifying an emissions factor database([0073]-[0075] - A component of the system 10 may send a message request to the factor data module 36 for emission factors for the standardized activity, where the message request includes the location hierarchy and the time interval. In response, the factor data module 36 is operable to determine which factors are valid for the time period of the standardized activity data and the locations defined by the location hierarchy... The factor data module 36 determines which valid factors are the most accurate for the given location hierarchy and time interval.);
accessing an emissions factor database wherein the emission factor database contains a plurality of emissions factors ([0072] -The factor data module 36 can store factor data locally in a database or access data stored remotely in third party databases. The factor data may be derived from protocols (e.g., GHG, IPCC, CDP), industry standards, emission databases, internal calculations, industry associations (e.g., EPA) and user specific databases, for example.)
selecting([0072]-[0075] - A component of the system 10 may send a message request to the factor data module 36 for emission factors for the standardized activity, where the message request includes the location hierarchy and the time interval. In response, the factor data module 36 is operable to determine which factors are valid for the time period of the standardized activity data and the locations defined by the location hierarchy... The factor data module 36 determines which valid factors are the most accurate for the given location hierarchy and time interval.); and
generating an emissions line item based on the structured emissions data and the at least one selected emissions factor ([0079] - The emission engine 40 is operable to compute at least one emission value for the activity. The emission value is the amount of emissions for an activity measured in a unit such as tonnes of CO2e (carbon dioxide equivalents), tonnes of CH4e (methane equivalents), and cubic metrics of water, for example and [0098] 0 The emission engine 40 is further operable to store the at least one emission value in an emission database, or transmit the emission value to the ESB 28 for access by the enterprise system 20, the API 22, the personal system 24 or a specialized calculator 26 and [0203] -In accordance with a further embodiment of the present invention, the data provisioning module 32 is further operable to receive a query for attributes of the at least one computed emission value and provide an additional emission value, calculation or report in response to the query. As previously established, once an emission value is computed it is stored in the emission database 44. This emission value includes the calculated amount of the emission as well as associated meta-information or attributes including the location, time interval, and other activity data from which the emission amount was derived from. Accordingly, the computed emission value may be a data structure associating the calculated amount of the emission with various attributes regarding relevant aspects of the raw activity data or standardized activity data, as stored in the activity database 42).
Glenn fails to explicitly disclose a machine learning system [for emissions data analysis... comprising]:
...[cause the] machine learning system [to perform operations comprising]:
[... a] machine learning [model configured to standardize data], wherein the machine learning model is trained with emissions training data and one or more emission validation data sets by:
validation, with a validation model, of an output of the machine learning model against the one or more emissions validation datasets, and
adjustment of at least one parameter of the machine learning model based on an output of the validation model and the emissions training data;
identifying an emissions factor database with the machine learning module;
selecting, with the machine learning model, from the emissions factor database...
However, in an analogous art, Umay discloses disclose a machine learning system [for emissions data analysis... comprising]: ...[cause the] machine learning system [to perform operations comprising]: [... a] machine learning [model configured to standardize data], wherein the machine learning model is trained with emissions training data ([0020] - In some cases, different emission reporting entities provided by different emission reporting entities may each use a standardized format to facilitate easier correlation. In other cases, one or more emission reporting entities may report emission data using non-standard formats, and the emission data may be standardized by the emission records ledger (and/or one or more supporting services) upon receipt (e.g., via template translators or previously-trained machine learning models)).
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Umay to the system for emissions data analysis of Glen to include a machine learning system [for emissions data analysis... comprising]: ...[cause the] machine learning system [to perform operations comprising]: [... a] machine learning [model configured to standardize data], wherein the machine learning model is trained with emissions training data.
One would have been motivated to combine the teachings of Umay to Glenn to do so as it provides / allows to beneficially enable tracking of GHG emissions in a manner that is transparent, standardized, and auditable (Umay, [0013]).
Further, in an analogous art, Kahn teaches wherein the machine learning model is trained with emissions training data and one or more emission validation data sets ([0022] - The machine learning engine can further support training the carbon emissions data analytics models (i.e., using historical data and algorithms), validation (i.e., optimizing data analytics model parameters and hyper-parameters), and deployment (e.g., integration into production use) different types of computing environments.)
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Kahn to the system for emissions data analysis of Glen in view of Umay to include wherein the machine learning model is trained with emissions training data and one or more emission validation data sets.
One would have been motivated to combine the teachings of Khan to Glenn in view of Umay to do so as it provides / allows learning from data and improving analysis via data analytics systems (Kahn, [0022]).
Further, in an analogous art, Streit teaches
...wherein the machine learning model is trained with ... training data and one or more ... validation data sets by (col. 17, lines 39-62 - In various embodiments, trained validation models are tailored during training so that validated outputs improve the entropy of the training data set, either expanding the circumstances in which trained models will authenticate correctly or refining the trained model to better distinguish between authentication classes and/or unknown results):
validation, with a validation model, of an output of the machine learning model against the one or more ... validation datasets (col. 13, lines 65-col. 14, lines 6 - In some implementations, false positives and false negatives are used repeatedly to train the model until the model is able to execute with no false positives or false negatives col. 17, lines 39-62 - In various embodiments, trained validation models are tailored during training so that validated outputs improve the entropy of the training data set, either expanding the circumstances in which trained models will authenticate correctly or refining the trained model to better distinguish between authentication classes and/or unknown results), and
adjustment of at least one parameter of the machine learning model based on an output of the validation model and the ... training data col. 17, lines 39-62 - In various embodiments, trained validation models are tailored during training so that validated outputs improve the entropy of the training data set, either expanding the circumstances in which trained models will authenticate correctly or refining the trained model to better distinguish between authentication classes and/or unknown results).
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Streit to emissions training data and one or more emission validation data sets of Glen in view of Umay and Khan to include ...wherein the machine learning model is trained with ... training data and one or more ... validation data sets by: validation, with a validation model, of an output of the machine learning model against the one or more ... validation datasets, and adjustment of at least one parameter of the machine learning model based on an output of the validation model and the ... training data
One would have been motivated to combine the teachings of Streit to Glenn in view of Umay and Kahn to do so as it provides / allows to improve the training data set (Streit, col. 17, lines 39-62).
Further, in an analogous art, Sun teaches wherein the operations further comprise: identifying the ... database with the machine learning model ([0039] - In some embodiments, the methods and systems described herein provide functionality for applying machine learning to problem solving. In contrast with many conventional systems, the methods and systems described herein may receive text (e.g., in “natural,” human-readable language), automatically discern from the text the relevant keywords and identify a problem being described by the text (e.g., through the use of machine learning models), automatically identify databases containing information for solving that problem (for example, and without limitation, by applying machine learning models to identify substantially similar problems and identifying information in a variety of databases that were useful in solving those problems), automatically identify a resolution for the problem, and provide, without human intervention, the user with a suggestion for solving a problem); and selecting, with the machine learning model, [the at least one information from the identified ... database] ([0039] - In some embodiments, the methods and systems described herein provide functionality for applying machine learning to problem solving. In contrast with many conventional systems, the methods and systems described herein may receive text (e.g., in “natural,” human-readable language), automatically discern from the text the relevant keywords and identify a problem being described by the text (e.g., through the use of machine learning models), automatically identify databases containing information for solving that problem (for example, and without limitation, by applying machine learning models to identify substantially similar problems and identifying information in a variety of databases that were useful in solving those problems), automatically identify a resolution for the problem, and provide, without human intervention, the user with a suggestion for solving a problem).
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Sun to the emissions factor/emissions factor database of Glen in view of Umay to include wherein the operations further comprise: identifying the ... database with the machine learning model; and selecting, with the machine learning model, [the at least one information] from the identified ... database.
One would have been motivated to combine the teachings of Sun to Glenn in view of Umay and Kahn to do so as it provides / allows automatically identify ... and provide, without human intervention, the user with a suggestion for solving... (Sun, [0039]).
Regarding Claim 2;
Glenn in view of Umay and Kahn and Streit and Sun disclose the system to Claim 1.
Glenn further discloses wherein the emissions factor database comprises a public database (([0072] -The factor data module 36 can store factor data locally in a database or access data stored remotely in third party databases. The factor data may be derived from protocols (e.g., GHG, IPCC, CDP), industry standards, emission databases, internal calculations, industry associations (e.g., EPA) and user specific databases, for example.) As construed an EPA database is from a government organization (i.e., public).
Regarding Claim 3;
Glenn in view of Umay and Kahn and Streit and Sun disclose the system to Claim 1.
Glenn further discloses wherein the emissions factor database corresponds to an emissions schema ([0072] -The factor data module 36 can store factor data locally in a database or access data stored remotely in third party databases. The factor data may be derived from protocols (e.g., GHG, IPCC, CDP), industry standards, emission databases, internal calculations, industry associations (e.g., EPA) and user specific databases, for example.) As construed use of protocols/standards are a form of schema.
Regarding Claim 4;
Glenn in view of Umay and Kahn and Streit and Sun disclose the system to Claim 1.
Glenn further discloses wherein the emissions factor database comprises a proprietary database or the at least one emissions factor comprises a proprietary emissions factor ([0072] -The factor data module 36 can store factor data locally in a database or access data stored remotely in third party databases. The factor data may be derived from protocols (e.g., GHG, IPCC, CDP), industry standards, emission databases, internal calculations, industry associations (e.g., EPA) and user specific databases, for example.) As construed a user specific database is a proprietary database.
Regarding Claim 9;
Glenn in view of Umay and Kahn and Streit and Sun disclose the system to Claim 1.
Glenn further discloses wherein the operations further comprising generating a user interface and displaying the generated emissions line item on the user interface ([0079] - The emission engine 40 is operable to compute at least one emission value for the activity. The emission value is the amount of emissions for an activity measured in a unit such as tonnes of CO2e (carbon dioxide equivalents), tonnes of CH4e (methane equivalents), and cubic metrics of water, for example and [0098] 0 The emission engine 40 is further operable to store the at least one emission value in an emission database, or transmit the emission value to the ESB 28 for access by the enterprise system 20, the API 22, the personal system 24 or a specialized calculator 26 and [0203] -In accordance with a further embodiment of the present invention, the data provisioning module 32 is further operable to receive a query for attributes of the at least one computed emission value and provide an additional emission value, calculation or report in response to the query. As previously established, once an emission value is computed it is stored in the emission database 44. This emission value includes the calculated amount of the emission as well as associated meta-information or attributes including the location, time interval, and other activity data from which the emission amount was derived from. Accordingly, the computed emission value may be a data structure associating the calculated amount of the emission with various attributes regarding relevant aspects of the raw activity data or standardized activity data, as stored in the activity database 42).
Regarding Claim 10;
Glenn in view of Umay and Kahn and Streit and Sun disclose the system to Claim 1.
Glenn further discloses wherein the operations further comprise: selecting, from the emissions factor database, emissions factors corresponding to a plurality of activity regions ([0073] - A component of the system 10 may send a message request to the factor data module 36 for emission factors for the standardized activity, where the message request includes the location hierarchy and the time interval. In response, the factor data module 36 is operable to determine which factors are valid for the time period of the standardized activity data and the locations defined by the location hierarchy); and generating emissions line items based on the structured emissions data and the plurality of selected emissions factors ([0079] - The emission engine 40 is operable to compute at least one emission value for the activity. The emission value is the amount of emissions for an activity measured in a unit such as tonnes of CO2e (carbon dioxide equivalents), tonnes of CH4e (methane equivalents), and cubic metrics of water, for example and [0098] 0 The emission engine 40 is further operable to store the at least one emission value in an emission database, or transmit the emission value to the ESB 28 for access by the enterprise system 20, the API 22, the personal system 24 or a specialized calculator 26 and [0203] -In accordance with a further embodiment of the present invention, the data provisioning module 32 is further operable to receive a query for attributes of the at least one computed emission value and provide an additional emission value, calculation or report in response to the query. As previously established, once an emission value is computed it is stored in the emission database 44. This emission value includes the calculated amount of the emission as well as associated meta-information or attributes including the location, time interval, and other activity data from which the emission amount was derived from. Accordingly, the computed emission value may be a data structure associating the calculated amount of the emission with various attributes regarding relevant aspects of the raw activity data or standardized activity data, as stored in the activity database 42).
Regarding Claim(s) 11-14 and 19; claim(s) 11-14 and 19 is/are directed to a/an method associated with the system claimed in claim(s) 1-4 and 9. Claim(s) 11-14 and 19 is/are similar in scope to claim(s) 1-4 and 9 and is/are therefore rejected under similar rationale.
Regarding Claim(s) 20; claim(s) 20 is/are directed to a/an medium associated with the system claimed in claim(s) 1. Claim(s) 20 is/are similar in scope to claim(s) 1 and is/are therefore rejected under similar rationale.
Claim(s) 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Glenn et al. (US 2011/0099489 A1) in view of Umay (US 2023/0162203 A1) and Kahn (US 2022/0358515 A1) and Streit (US 10,938,852 A1) and Sun et al. (US 2017/0011308 A1) and further in view of Feickert et al. (US 20230061787 A1).
Regarding Claim 6;
Glenn in view of Umay and Kahn and Streit and Sun disclose the system to Claim 1.
Glenn further discloses ...receiving an identification of the emissions factor database... ([0072] - The factor data module 36 can store factor data locally in a database or access data stored remotely in third party databases. The factor data may be derived from protocols (e.g., GHG, IPCC, CDP), industry standards, emission databases, internal calculations, industry associations (e.g., EPA) and user specific databases, for example) and selecting the at least one emissions factor from the identified emission factor database... ([0073]-[0075] - The factor data module 36 can store factor data locally in a database or access data stored remotely in third party databases. The factor data may be derived from protocols (e.g., GHG, IPCC, CDP), industry standards, emission databases, internal calculations, industry associations (e.g., EPA) and user specific databases, for example. A component of the system 10 may send a message request to the factor data module 36 for emission factors for the standardized activity, where the message request includes the location hierarchy and the time interval. In response, the factor data module 36 is operable to determine which factors are valid for the time period of the standardized activity data and the locations defined by the location hierarchy... The factor data module 36 determines which valid factors are the most accurate for the given location hierarchy and time interval.)
Glenn in view of Umay and Kahn and Streit and Sun fail to explicitly discloses wherein the operations further comprise: receiving ... from a user interface; and selecting the at least one emissions factor ... based on an input received from the user interface.
However, in an analogous art, Feickert teaches wherein the operations further comprise: receiving ... from a user interface and selecting the at least one emissions factor ... based on an input received from the user interface ([0005] and [0037] and Claim 16)
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Feickert to the emissions factor/emissions factor database of Glenn in view of Umay and Kahn and Streit and Sun to include wherein the operations further comprise: receiving ... from a user interface and selecting the at least one emissions factor ... based on an input received from the user interface.
One would have been motivated to combine the teachings of Feickert to Glenn in view of Umay and Kahn and Streit and Sun to do so as it provides / enables fast and efficient calculations of footprints of products (Feickert, [0016]).
Regarding Claim(s) 16; claim(s) 16 is/are directed to a/an method associated with the system claimed in claim(s) 6. Claim(s) 16 is/are similar in scope to claim(s) 6 and is/are therefore rejected under similar rationale.
Claim(s) 7-8 and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Glenn et al. (US 2011/0099489 A1) in view of Umay (US 2023/0162203 A1) and Kahn (US 2022/0358515 A1) and Streit (US 10,938,852 A1) and Sun et al. (US 2017/0011308 A1) and further in view of Chatterjee et al. (US 2024/0078215 A1).
Regarding Claim 7;
Glenn in view of Umay and Kahn and Sun disclose the system to Claim 1.
Glenn in view of Umay and Kahn and Streit and Sun fail to explicitly disclose wherein the emissions training data is entity-specific, and wherein the emissions training data comprises at least one of emissions entity training data or emissions activity training data.
However, in an analogous art, Chatterjee teaches wherein the emissions training data is entity-specific, and wherein the emissions training data comprises at least one of emissions entity training data or emissions activity training data ([0007] - ... returning search results comprising one or more found emission dataset records out of the plurality of emission dataset records; presenting the one or more found emission dataset records in a user interface for confirmation; receiving a selection of a confirmed emission dataset record out of the one or more found emission dataset records; training a machine learning model with training data comprising a mapping between the created item component record and the confirmed emission dataset record; and predicting with the trained machine learning model a new mapping between a new created item component record and a new emission dataset record. [0152] - Training data can be drawn from sources such as reusing data from internal as well as external data to determine what kind of master data and enterprise data as well as physical goods movement data can be incorporated and [0154] - Training data and pre-trained machine learning models can be provided, but actual enterprise data can be provided via a product footprint management tool).
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Chatterjee to the machine learning system of Chatterjee to Glenn in view of Umay and Kahn and Streit and Sun to include wherein the emissions training data is entity-specific, and wherein the emissions training data comprises at least one of emissions entity training data or emissions activity training data.
One would have been motivated to combine the teachings of Chatterjee to Glenn in view of Umay and Kahn and Streit and Sun to do so as it provides / allows mapping [to] be achieved sooner and with fewer resources, leading to earlier adoption of product footprint management and improved attention to sustainability considerations (Chatterjee, [0035]).
Regarding Claim 8;
Glenn in view of Umay and Kahn and Streit and Sun disclose the system to Claim 1.
Glenn in view of Umay and Kahn and Streit and Sun fail to explicitly disclose wherein training the machine learning model comprises: obtaining the emissions training data; receiving user input from a user interface; and updating the machine learning model based on the emissions training data and the user input.
However, in an analogous art, Chatterjee teaches wherein training the machine learning model comprises: obtaining the emissions training data ([0007] - ... returning search results comprising one or more found emission dataset records out of the plurality of emission dataset records; presenting the one or more found emission dataset records in a user interface for confirmation; receiving a selection of a confirmed emission dataset record out of the one or more found emission dataset records; training a machine learning model with training data comprising a mapping between the created item component record and the confirmed emission dataset record; and predicting with the trained machine learning model a new mapping between a new created item component record and a new emission dataset record); receiving user input from a user interface ([0007] - ... returning search results comprising one or more found emission dataset records out of the plurality of emission dataset records; presenting the one or more found emission dataset records in a user interface for confirmation; receiving a selection of a confirmed emission dataset record out of the one or more found emission dataset records; training a machine learning model with training data comprising a mapping between the created item component record and the confirmed emission dataset record; and predicting with the trained machine learning model a new mapping between a new created item component record and a new emission dataset record); and updating the machine learning model based on the emissions training data and the user input ([0007] - ... returning search results comprising one or more found emission dataset records out of the plurality of emission dataset records; presenting the one or more found emission dataset records in a user interface for confirmation; receiving a selection of a confirmed emission dataset record out of the one or more found emission dataset records; training a machine learning model with training data comprising a mapping between the created item component record and the confirmed emission dataset record; and predicting with the trained machine learning model a new mapping between a new created item component record and a new emission dataset record).
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Chatterjee to the machine learning system of Chatterjee to Glenn in view of Umay and Kahn and Streit and Sun to include wherein training the machine learning model comprises: obtaining the emissions training data; receiving user input from a user interface; and updating the machine learning model based on the emissions training data and the user input.
One would have been motivated to combine the teachings of Chatterjee to Glenn in view of Umay and Kahn and Streit and Sun to do so as it provides / allows mapping [to] be achieved sooner and with fewer resources, leading to earlier adoption of product footprint management and improved attention to sustainability considerations (Chatterjee, [0035]).
Regarding Claim(s) 17-18; claim(s) 17-18 is/are directed to a/an method associated with the system claimed in claim(s) 7-8. Claim(s) 17-18 is/are similar in scope to claim(s) 7-8 and is/are therefore rejected under similar rationale.
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Glenn et al. (US 2011/0099489 A1) in view of Umay (US 2023/0162203 A1) and Kahn (US 2022/0358515 A1) and Streit (US 10,938,852 A1) and Sun et al. (US 2017/0011308 A1) and further in view of Bin Zainal Abidin et al. (US 2024/0202403 A1).
Regarding Claim 21;
Glenn in view of Umay and Kahn and Streit and Sun disclose the system to Claim 1.
Glenn discloses generating an emissions line item ... based on the ... emissions data and the at least one selected emissions factor ([0079] - The emission engine 40 is operable to compute at least one emission value for the activity. The emission value is the amount of emissions for an activity measured in a unit such as tonnes of CO2e (carbon dioxide equivalents), tonnes of CH4e (methane equivalents), and cubic metrics of water, for example and [0098] 0 The emission engine 40 is further operable to store the at least one emission value in an emission database, or transmit the emission value to the ESB 28 for access by the enterprise system 20, the API 22, the personal system 24 or a specialized calculator 26 and [0203] -In accordance with a further embodiment of the present invention, the data provisioning module 32 is further operable to receive a query for attributes of the at least one computed emission value and provide an additional emission value, calculation or report in response to the query. As previously established, once an emission value is computed it is stored in the emission database 44. This emission value includes the calculated amount of the emission as well as associated meta-information or attributes including the location, time interval, and other activity data from which the emission amount was derived from. Accordingly, the computed emission value may be a data structure associating the calculated amount of the emission with various attributes regarding relevant aspects of the raw activity data or standardized activity data, as stored in the activity database 42).
Glenn in view of Umay and Kahn and Streit and Sun fail to explicitly disclose determining that the emissions activity data is missing emissions data for a first time period of a plurality of time periods; generating, with the machine learning model, projected emissions data for the missing emissions data of the first time period based on the structured emissions data corresponding to one or more other time periods of the plurality of time periods; and generating an ... the missing emissions data of the first time period based on the projected emissions data ....
However, in an analogous art, Bin Zainal Abidin teaches determining that the emissions activity data is missing emissions data for a first time period of a plurality of time periods (Abstract - For example, a computer-implemented method may include receiving projected production parameters and emissions reduction strategy information associated with one or more operational systems for a period of time and generating, using a methane emissions intensity prediction model, methane emissions intensity predictions based on the projected production parameters and the emissions reduction strategy information along with historical operational data and historical emissions data for the one or more operational systems. The methane emissions intensity prediction model may be a machine learning model trained on the historical operational data and historical emissions data and possibly simulated emissions data and [0069]); generating, with the machine learning model, projected emissions data for the missing emissions data of the first time period based on the structured emissions data corresponding to one or more other time periods of the plurality of time periods (Abstract and [0069] - The apparatus may be configured to determine (e.g., from the projected production parameters) that an operational system is scheduled or planned to operate in a particular manner (e.g., according to particular parameters, in a particular mode and/or capacity), to determine (e.g., from the historical data) that the operational system has not previously operated in that particular manner, and, in response, to generate the simulated data (e.g., simulated emissions data) corresponding to and/or based on the projected production parameters in order to fill in missing data in the input and/or training data sets that may be useful or necessary for making the prediction); and generating ... the missing emissions data of the first time period based on the projected emissions data ... (Abstract and [0069]).
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Bin Zainal Abidin to the machine learning system of Chatterjee to Glenn in view of Umay and Kahn and Streit and Sun to include determining that the emissions activity data is missing emissions data for a first time period of a plurality of time periods; generating, with the machine learning model, projected emissions data for the missing emissions data of the first time period based on the structured emissions data corresponding to one or more other time periods of the plurality of time periods; and generating an ... the missing emissions data of the first time period based on the projected emissions data.
One would have been motivated to combine the teachings of Bin Zainal Abidin to Glenn in view of Umay and Kahn and Streit and Sun to do so as it provides / allows predicting methane emissions intensity (Bin Zainal Abidin, [0002]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASFAND M SHEIKH whose telephone number is (571)272-1466. The examiner can normally be reached Mon-Fri: 7a-3p (MDT).
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/ASFAND M SHEIKH/Primary Examiner, Art Unit 3626
