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 16-20 are pending for examination. Claim(s) 5 and 15 have been cancelled. This action is Final.
Response to Arguments
Applicant's arguments filed 1/26/2026 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.
[...]
While the Office alleges that the claims "recite 'commercial interactions' or 'legal interactions,"' the claims, as a whole, are not directed to any alleged method of organizing human activity. Rather, the claims recite specific technical language, including "extracting structured emissions data ... by applying the emissions activity data to a machine learning model," "identifying an emissions activity database with the machine learning model," "selecting with the machine learning model, from the emissions factor database, at least one emissions factor corresponding to the activity region" and "generating an emissions line item." At least these claim elements address known problems in conventional emissions data generation systems, such as the lack of ability to handle non-uniform input data as explained in Applicant's Specification. See, e.g., Specification, [0032]. In addition, Applicant submits that these technical claim elements, including the recited machine learning model that extracts structured data, are dissimilar to the examples of "business relations" provided in the MPEP (e.g., "processing an application for financing a loan" or "processing information through a clearing-house"). MPEP 2106.04(a)(2)(II)(B). The MPEP also makes clear that "[t]he term "certain" qualifies the "certain methods of organizing human activity" grouping as a reminder of several important points. First, not all methods of organizing human activity are abstract ideas ... Second, this grouping is limited to activity that falls within the enumerated sub-groupings of fundamental economic principles or practices, commercial or legal interactions, and managing personal behavior and relationships or interactions between people, and is not to be expanded beyond these enumerated sub-groupings except in rare circumstances." Id. Thus, contrary to the Office's assertions, the claims are not directed to business relations.
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 machine learning model to “"extracting structured emissions data ... by applying the emissions activity data... to standardize data," "identifying an emissions activity database ...," "selecting ... from the emissions factor database, at least one emissions factor corresponding to the activity region" and "generating an emissions line item,” 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: Furthermore, Applicant submits that the limitation-by-limitation analysis performed by the Office under Step 2A is improper and is inconsistent with the Alice framework articulated above and the MPEP. Claim 1 recites "extracting structured emissions data from the emissions activity data by applying the emissions activity data to a machine learning model configured to standardize data, wherein the machine learning model is trained with emissions training data" however, the Office fails to evaluate claim elements such as the "machine learning model" "trained with emissions training data" in the Prong 1 analysis. Applicant submits that such a dissection of the claim is improper. According to the MPEP, "Examiners may not dissect a claimed invention into discrete elements and then evaluate the elements in isolation. Instead, the claim as a whole must be considered." See MPEP § 2103 (citing Diamond v. Diehr, 450 U.S. 175, 188-89, 209 USPQ 1, 9 (1981) ("In determining the eligibility of respondents' claimed process for patent protection under § 101, their claims must be considered as a whole."); see also MPEP § 2106.04(11) (stating 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.
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 as a whole was considered. The examiner respectfully notes that the abstract idea was identified in Step 2A-Prong One, i.e., extracting structured emissions data from the emissions activity data by applying the emissions activity data to standardize data. Further in Step 2A-Prong 2 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.
[...]
First, Applicant submits that claim 1 is eligible for reasons analogous to the claims held eligible in Ex parte Desjardins. [...]
Similarly, Applicant's present Specification explains a technical problem in conventional emissions analysis systems as they are "inaccurate in ingesting, analyzing, and processing emissions data including large amounts of data in various formats." Specification, [0003]. Like Desjardins, Applicant's Specification also describes improvements to the recited emissions analysis system, 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" and "provid[ing] improvements to the training and inference of machine learning models used to standardize emissions data." Specification, [0027]. These improvements are reflected in claim 1, as claim 1 recites a "machine learning system for emissions data analysis" that performs operations including "extracting structured emissions data from the emissions activity data by applying the emissions activity data to a machine learning model configured to standardize data, wherein the machine learning model is trained with emissions training data and one or more emissions validation datasets." Thus, as a whole, claim 1 provides improvements to how the recited machine learning model and system for emissions data analysis, analogous to the eligible claims of Desjardins. Accordingly, Applicant submits claim 1 is eligible under Prong 2.
Examiner’s Response: The examiner respectfully disagrees. As noted above, “extracting structured emissions data from the emissions activity data by applying the emissions activity data to standardize data” is noted to be part of the abstract idea. The examiner notes 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: Second, the claimed subject matter is similar to the Office's analysis in the Subject Matter Eligibility October 2019 Update, in which the Office gave the following example (Example 42, Claim 1) as being integrated into a practical application: [...]
The present claims, similar to Example 42, provide a technical solution to a specific technical problem of converting emissions data into a structured, standardized format, wherein that data can originate from a wide variety of data sources and data formats that lack uniformity. See Specification, [0032], [0047].
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: Furthermore, Applicant submits that the limitation-by-limitation analysis performed by the Office under Step 2A is improper and is inconsistent with the Alice framework articulated above and the MPEP. Claim 1 recites "extracting structured emissions data from the emissions activity data by applying the emissions activity data to a machine learning model configured to standardize data, wherein the machine learning model is trained with emissions training data" however, the Office fails to evaluate claim elements such as the "machine learning model" "trained with emissions training data" in the Prong 1 analysis. Applicant submits that such a dissection of the claim is improper. According to the MPEP, "Examiners may not dissect a claimed invention into discrete elements and then evaluate the elements in isolation. Instead, the claim as a whole must be considered." See MPEP § 2103 (citing Diamond v. Diehr, 450 U.S. 175, 188-89, 209 USPQ 1, 9 (1981) ("In determining the eligibility of respondents' claimed process for patent protection under § 101, their claims must be considered as a whole."); see also MPEP § 2106.04(11) (stating 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.
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. As noted above, “extracting structured emissions data from the emissions activity data by applying the emissions activity data to standardize data” is noted to be part of the abstract idea. The examiner notes 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: 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.
The Office alleges 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." Office Action, 5. Applicant respectfully disagrees. In BASCOM, the Federal Circuit confirmed that a specific, discrete implementation of an abstract idea contains an "inventive concept" that can amount to significantly more. BASCOM Global Internet Serv., Inc. v. AT&T Mobility LLC, 827 F.3d 1342, 1349-1350 (Fed. Cir. 2016). In that case, while the Federal Circuit found that the claims were directed to the abstract idea of filtering content, it also observed that the claims did not "preempt all ways of filtering content on the Internet; rather, they recite a specific, discrete implementation of the abstract idea of filtering content" and thus contains an inventive concept amounting to "significantly more" than the abstract idea. Id. The Federal Circuit confirmed that "the 'inventive concept' may arise in one or more of the individual claim limitations or in the ordered combination of the limitations." Id. at 1346; MPEP § 2106.05(I)(B). The Federal Circuit emphasized that "an inventive concept can be found in the non-conventional and non-generic arrangement of known, conventional pieces." Id. at 1350. The Federal Circuit further demanded that the "inventive concept" inquiry requires an "explanation." Id.
Like the claims in BASCOM, Applicant's claims do not preempt all ways of performing the alleged abstract idea, but rather recite a specific, discrete way of analyzing emissions data to generate an emissions line item. The claims also do not attempt to preempt all ways of generating data using a model, but rather generating emissions data based on structured emissions data and specific emissions factors. The fact that the claims do not preempt all ways of performing the alleged abstract idea is by itself sufficient to overcome this rejection. In addition, claim 1 recites "identifying an emissions factor database with the machine learning model; accessing the emissions factor database, wherein the emissions factor database contains containing a plurality of emissions factors; and selecting with the machine learning model, from the emissions factor database, at least one emissions factor corresponding to the activity region." As described in Applicant's Specification, the selection of emissions factors for generating emissions data analysis "provid[es] improvements in generating emissions insights by using more accurate emissions factors." Specification, [0050]. Thus, the claims reflect non-routine activity that provides an improvement to the generation of emissions insights. For these additional reasons, Applicant's claims recite "significantly more" than any alleged abstract idea.
Accordingly, for at least the above reasons, the rejection of claim 1 under 35 U.S.C. § 101 is improper and should be withdrawn. Although of different scope, independent claims 11 and 20 are similarly allowable. Dependent claims 2-10 and 12-19 depend on one of independent claims 1 or 11 and are directed to statutory subject matter at least by virtue of their dependence from an allowable base claim.
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., system w/ processor, medium, instructions and a use of machine learning module that was trained with data and validated with data; 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 (Step 2B: NO). 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.
Applicant's arguments filed 1/27/2026 have been considered but they are moot in view of new grounds of rejection.
Of note, Applicant Argues: “Umay is also silent regarding "selecting with the machine learning model, from the emissions factor database, at least one emissions factor corresponding to the activity region." Sun, Feickert, and Chatterjee, alone or in combination with Glenn or Umay, fail to cure such deficiencies. Accordingly, the cited references do not teach or suggest each element of claim 1, and therefore fail to render claim 1 obvious.”
Examiner’s Response: The examiner respectfully notes that the combination of Glenn and Sun disclose the features of "selecting with the machine learning model, from the emissions factor database, at least one emissions factor corresponding to the activity region.” Glen is shown to disclose 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 Sun is shown to teach 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). Motivation was provided for such a combination and further the metes and bounds of the claim have been met. Therefore, the examiner finds this argument not persuasive.
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 16-20 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 16-20 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 19, 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 19, 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 19, 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 19, recite i.e., system w/ processor, medium, instructions and a use of machine learning module that was trained with data and validated with data. 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). 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 19 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 19 recites an abstract idea and fails to integrate the abstract idea into a practical application, claim 1, and similar claim(s) 11 and 19 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 19, i.e., system w/ processor, medium, instructions and a use of machine learning module that was trained with data and validated with data; 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 19 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 19 is ineligible.
Regarding Claims 2-4, 6-10, 12-14, and 15-19; claims 2-4, 6-10, 12-14, and 15-19further 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 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;
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, 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 and Sun 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 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 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 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 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 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 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 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 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 Glen in view of Umay 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 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 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 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 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 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 Sun disclose the system to Claim 1.
Glenn in view of Umay and Kahn and Sun fail to explicitly 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 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 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kumar et al. (US 2024/0020279 A1) discusses To this end, in some embodiments, database selection module 114 can include a deep learning algorithm, such as a multilayer perception (MLP) or an artificial neural network (ANN), that is trained and tested using machine learning techniques with a modeling dataset generated from the organization's historical database transaction metadata and the database attribute metadata. For example, the historical database transaction metadata and the database attribute metadata used to generate the modeling dataset may be collected by data collection module 110, as previously described herein. In some embodiments, the deep learning algorithm can be trained and tested using the modeling dataset to build a multiclass classification model (sometimes referred to herein more simply as a “multiclass classifier”). Once the deep learning algorithm is trained, the trained multiclass classification model can, in response to input of a particular set of requirements for a database, predict database that is optimal for the input set of requirements. Further description of the deep learning algorithm(s) and other processing that can be implemented within database selection module 114 is provided below at least with respect to FIGS. 2-4. ([0036]).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to 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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JESSICA LEMIEUX can be reached at (571)270-3445. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ASFAND M SHEIKH/Primary Examiner, Art Unit 3626
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