DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . The following FINAL office action is in response to Applicant communication filed on 03/02/2026 regarding application 18/327,793. Claims 1 and 10-20 have been amended. Claims 1-20 are pending and have been rejected.
Response to Amendments
2. Applicant’s amendment filed on 03/02/2026 necessitated new grounds of rejection in this office action.
Priority
3. The Examiner has noted the Applicants claiming Priority from Provisional (PRO) Application # 63/365,823 filed on 06/03/2022. Therefore, the earliest effective filing date considered for this case is of 06/03/2022.
Response to Arguments
4. Applicant’s arguments, see page 21-22 of 25 filed on 03/02/2026, with respect to the previous 35 U.S.C. § 112 (b) Claim Rejections for Claims 12-20 have been fully considered and are found to be persuasive. Therefore, the previous 35 U.S.C. § 112 (b) Claim Rejections for Claims 12-20 have been withdrawn.
5. Applicant’s arguments, see pages 22-24 of 25, filed on 03/02/2026, with respect to the 35 U.S.C. § 103 Claim Rejections for Claims 1-2, 4, 8-12, 14 and 18-20 have been fully considered and are found to be persuasive. Therefore, the 35 U.S.C. § 103 Claim Rejections for Claims 1-20 have been withdrawn. See Examining Claims with Respect to Prior Art Section shown below.
Response to 35 U.S.C. § 101 Arguments
6. Applicant’s 35 U.S.C. § 101 arguments, filed with respect to Claims 1-20 have been fully considered, but they are found not persuasive (see Applicant Remarks, Pages 19-21 of 25 dated 03/02/2026). Examiner respectfully disagrees.
Argument #1:
(A). Applicant argues that Claims 1-20 do not recite an abstract idea, law of nature of natural phenomenon under revised step 2a prong one of the 35 U.S.C § 101 analysis (see Applicant Remarks, Pages 19-21 of 25, dated 03/02/2026). Examiner respectfully disagrees.
Specifically, Applicant argues the claims provide a technical improvement through AI summary and AI modeling engines that improve determination of financial and sustainability impacts arising from legislation. Additionally, Applicant analogizes the amended claim limitations of Independent Claims 1 and 11 of the claimed invention to the Ex Parte Desjardins case. Examiner respectfully disagrees.
The claims do not improve computer technology itself, but instead merely use generic computer components and generic AI functionality as tools to perform abstract analysis of legislation and financial impacts.
The alleged improvement concerns: the content of legal and financial analysis,and not the operation of computers, AI architectures, neural network functionality, database technology, processor efficiency, memory structures, or machine-learning training techniques.
Independent Claims 1 and 11 merely automate collecting legislation information, summarizing legislation, applying legislation to organizational data, calculating impacts, and presenting results. These activities constitute abstract information analysis and evaluation. Unlike claims directed to improvements in computer functionality itself, the present claims merely use conventional AI tools to perform abstract legal and financial reasoning faster. Courts have repeatedly held that applying generic AI or machine learning to abstract information analysis does not confer eligibility absent a specific technological improvement. See: Electric Power Group, LLC v. Alstom S.A (Fed. Cir. 2016) and SAP America, Inc. v. InvestPic, LLC (Fed. Cir. 2018) court cases.
Independent Claims 1 and 11: With respect to the additional elements of (e.g., “an AI modeling engine” & “AI summary engine” & “machine learning”) when considered with the recited claim limitations both individually and as an ordered combination (as a whole), these additional elements do not integrate the abstract idea into a practical application under step 2a prong 2 according to the following: (1) the claims as a whole are limited to a particular field of use or technological environment for calculating a sustainability impact indicating an overall impact of a plurality of legislations on the organization based on the financial impact of the legislation in a business enterprise environment (see MPEP § 2106.05(h)) or (2) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)). Under USPTO guidelines, integrating an AI or abstract algorithm into a practical application requires more than just telling a computer to "apply it". These claims recite an AI summary engine and an AI modeling engine, but it fails to define how these engines function mechanically, structurally, or computationally in a new way. These claims use general computing elements (a "server computing device," "one or more processors," "associated memory," "client device"). Using a generic computer to perform mental or mathematical steps does not transform the abstract idea into a practical application; it merely reduces the abstract idea to digital automation. Because the claims lack specific, concrete implementations (e.g., a novel neural network architecture, a specialized data structure, or a specific technological solution to a computing problem), the claims do not integrate the judicial exceptions into a practical application under Prong 2.
In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Applicant argues the claims integrate any alleged abstract idea into a practical application. The claims do not meaningfully integrate the abstract idea into a practical application because they: merely collect information, analyze information, and present results. The claims do not: improve computer functionality, control industrial machinery, transform physical articles, improve networking, improve database operations, or improve machine-learning technology itself. Instead, the claims merely use generic processors and AI tools to automate abstract legal and financial analysis. The claimed “sustainability planner” merely serves as a generic computing environment for performing abstract information processing.
Therefore, in conclusion, Examiner maintains that Claims 1-20 do not recite additional elements that integrate the judicial exception into a practical application under step 2a prong 2 of the 35 U.S.C. § 101 analysis.
Argument #2:
(B). Applicant argues that Independent Claims 1 and 11 are analogous to the Ex Parte Desjardins court case decided on September 26, 2025 and designated by as precedential by USPTO on November 4th, 2025, because the AI-generated knowledge is protected in a database and allegedly improves sustainability-impact analysis (see Applicant Remarks, Pages 20 of 25, dated 03/02/2026). Examiner respectfully disagrees.
Applicant’s reliance on Ex Parte Desjardins is unpersuasive because the present claims do not recite a comparable technological improvement. The claims here do not specify: a new AI architecture, specialized model training, improved inference techniques, novel database structures, enhanced data retrieval methods, or improved computer operations. Instead, the claims recite result-oriented functional language such as: “generate a summary,” “generate a legislation model,” “calculate a financial impact,” “calculate a sustainability impact metric.” The claims merely invoke generic “AI summary” and “AI modeling” engines without describing: how the AI operates, how the models are trained, what machine-learning techniques are used, or how the computer itself is technologically improved.
Examiner points out that the provided Independent Claims 1 and 11 claim limitations remain patent-ineligible under 35 U.S.C. § 101 despite the 2025 precedential decision in Ex parte Desjardins. While Desjardins signaled a more favorable environment for AI, it specifically protected inventions that provide technical improvements to the computer or AI model itself. The current Independent Claims 1 and 11 are not analogous to Desjardins and is ineligible for the following reasons: Reason #1: Nature of the Improvement: Content vs. Function -> Desjardins Analogy: In Desjardins, the claims were eligible because they improved the functioning of the machine learning (ML) model, specifically reducing storage requirements and solving "catastrophic forgetting" during training. It changed how the computer learns. Current Independent Claims 1 and 11: Independent Claims 1 and 11 are directed to what the computer learns—specifically a sustainability planning concerning legislation, taxes, sustainability regulations, organizational compliance, financial impacts, and reporting business-impact metrics, which is not an improvement to the underlying AI or ML technology itself. Reason #2: Abstract Idea: Certain Methods of Organizing Human Activities & Mental Processes: -> The claim recites multiple categories of abstract ideas that were not "saved" by the technical improvements seen in Desjardins: Certain Methods of Organizing Human Activity: The claims do substantially more than merely “calculate and report.” They ingest legal information, model legislation, apply legal rules to organizational parameters, evaluate financial impact, determine sustainability compliance metrics, and report organizational consequences. These activities are directed to business and legal analysis regarding organizational compliance and financial exposure. Such activities are abstract because they involve evaluating organizational obligations, analyzing regulatory effects, and assisting business decision-making. These are traditional business and management functions. Unlike Desjardins, where math was integrated into a model-training benefit, the math here is used solely for information analysis. Reason #3: Failure to Integrate into a Practical Application (Step 2A, Prong 2): The claim fails to integrate these abstract ideas because: Lack of Technical "How": Independent Claims 1 and 11 recite “AI summary engine” & “AI modeling engine” & “machine learning”, but it fails to define how these engines function mechanically, structurally, or computationally in a new way. These claims use general computing elements (a "server computing device," "one or more processors," "associated memory," "client device"). Using a generic computer to perform mental or mathematical steps does not transform the abstract idea into a practical application; it merely reduces the abstract idea to digital automation. Because the claims lack specific, concrete implementations (e.g., a novel neural network architecture, a specialized data structure, or a specific technological solution to a computing problem), the claims do not integrate the judicial exceptions into a practical application under Prong 2. Merely labeling components as “AI” does not confer patent eligibility. The Federal Circuit has repeatedly rejected claims that: invoke generic AI or analytical engines, while claiming only abstract data analysis results. Because Independent Claims 1 and 11 of the instant application are directed to an abstract business/management method and uses generic AI and networking tools as mere "black boxes" to achieve that method, it lacks the system-level technical benefit found in Desjardins and remains ineligible under 35 U.S.C. § 101 analysis.
Argument #3:
(C). Applicant argues that Claims 1-20 do not recite an abstract idea, law of nature of natural phenomenon under revised step 2a prong one of the 35 U.S.C § 101 analysis (see Applicant Remarks, last ¶ of Page 20 and 1st ¶ of Page 21, dated 03/02/2026). Examiner respectfully disagrees.
Applicant argues that humans could not realistically process the claimed volume of legislation data and therefore the claims are not mental processes,particularly under the December 4, 2025 USPTO memorandum.
The claims remain directed to abstract mental-process-type activities despite their scale and computer implementation. The claimed operations fundamentally involve reviewing legislation, summarizing legal information, modeling legal rules, applying rules to organizational parameters, calculating impacts, and reporting results. These are forms of conceptual analysis and evaluative reasoning. The fact that a computer performs these operations more quickly or on larger datasets does not remove the claims from the abstract-idea category. Courts consistently hold: automating tasks humans could conceptually perform remains abstract, even where computers are necessary for speed or scale. See: CyberSource Corp. v. Retail Decisions, Inc (Fed. Cir. 2011) and FairWarning IP, LLC v. Iatric Systems, Inc (Fed. Cir. 2016). The claims merely perform conventional information analysis at computer scale. Moreover, the claims do not recite any technological limitation preventing mental performance, nor any specialized machine implementation beyond generic processors and AI engines.
Argument #4:
(D). Applicant argues in Independent Claims 1 and 11 that the AI summary engine and AI modeling engine meaningfully limit the claims under 35 U.S.C § 101 analysis (see Applicant Remarks, last ¶ of Page 20 and 1st ¶ of Page 21, dated 03/02/2026). Examiner respectfully disagrees.
The recited AI engines are described only functionally and generically. The claims do not recite: a specific neural network architecture, transformer model improvements, novel training methodology, feature extraction techniques, tokenization methods, or improvements to machine-learning performance itself. Instead, the claims merely invoke AI as a generic tool to: summarize information, create models, and calculate impacts. Merely implementing an abstract idea using generic AI components does not supply eligibility. See: Alice Corp. v. CLS Bank International (Fed. Cir. 2014) where generic computer implementation did not transform abstract concepts into patent-eligible subject matter.
Argument #5:
(E). Applicant argues that Independent Claims 1 and 11 are not directed to “Certain Methods of Organizing Human Activity” under 35 U.S.C. § 101 step 2a prong 1 of the 35 U.S.C. § 101 analysis (see Applicant Remarks, 2nd ¶ of Page 21, dated 03/02/2026). Examiner respectfully disagrees.
Applicant argues the claims are not directed to fundamental economic practices, commercial interactions, or legal interactions. The claims directly concern legislation, taxes, sustainability regulations, organizational compliance, financial impacts, and reporting business-impact metrics. These are quintessential commercial and legal activities. The claims are directed to: evaluating regulatory obligations, assessing financial consequences of legislation, and providing compliance-related analysis. Such activities fall squarely within: commercial interactions, legal obligations, and business analytics. The claims therefore recite certain methods of organizing human activity. The fact that the claims involve sustainability regulations rather than traditional insurance or hedging does not remove them from this category.
Argument #6:
(F). Applicant argues that “calculating and reporting a metric” recited in Independent Claims 1 and 11 are not directed to “Certain Methods of Organizing Human Activity” under 35 U.S.C. § 101 step 2a prong 1 of the 35 U.S.C. § 101 analysis (see Applicant Remarks, 2nd ¶ of Page 21, dated 03/02/2026). Examiner respectfully disagrees.
The claims do substantially more than merely “calculate and report.” They ingest legal information, model legislation, apply legal rules to organizational parameters, evaluate financial impact, determine sustainability compliance metrics, and report organizational consequences. These activities are directed to business and legal analysis regarding organizational compliance and financial exposure. Such activities are abstract because they involve evaluating organizational obligations, analyzing regulatory effects, and assisting business decision-making.
Moreover, the claim limitations for Independent Claims 1 and 11 are directed to certain methods of organizing human activities (specifically, managing business compliance, regulatory tax rules, and financial risk) combined with mental processes (analyzing, modeling, and comparing rules to client data). Evaluating how tax laws and sustainability rules affect an organization’s bottom line is an age-old practice that humans (tax attorneys, accountants, and business consultants) have historically done in their heads, on paper, or using basic commercial logic. Using AI and servers to automate these familiar human tasks doesn't change the foundational concept being claimed.
The first step of “Ingest legislation data to generate a summary of legislation data using an AI summary engine..." is a Mental Process or a Certain Method of Organizing Human Activity. Summarizing legal texts is a known mental process. Using AI to ingest text and output a summary does not add a technical, inventive component; it just automates a task traditionally done by human readers. The second step of "Ingest sustainability legislation data to generate a legislation model using an AI modeling engine..." is a Mental Process or a Certain Method of Organizing Human Activity. Modeling the impact of regulations is a fundamental economic/business practice. Training a machine learning model to simulate legislation is a mathematical concept executed on data to achieve a business goal. The third step of "Receive client data of an organization..." is a Certain Method of Organizing Human Activity which is a mere data gathering step. Reciting the basic receipt of data is insignificant extra-solution activity. It does not limit the scope of the underlying abstract idea. The fourth step of "Apply the parameters of the client data to the legislation model to calculate a financial impact..." is a Mathematical Concept or a Mental Process. This is the core abstract idea—applying rules to parameters to calculate a financial outcome. It describes nothing more than a mathematical calculation and economic analysis performed by a computer. The fifth step of "Calculate a sustainability impact metric indicating an overall impact of a plurality of legislation..." is a Mathematical Concept. Generating a custom "impact metric" is a calculation and a method of organizing human activity (business/compliance management). The sixth step of “Send the financial impact and the sustainability impact metric to a client device..." is a Certain Method of Organizing Human Activity and is a mere data transmission step.
In conclusion, Examiner maintains that Claims 1-20 are directed to abstract ideas under “Mental Processes” or “Certain Methods of Organizing Human Activities” or “Mathematical Concepts” Groupings under 35 U.S.C. § 101 Step 2A Prong 1.
Argument #7:
(G). Applicant argues that Claims 1-20 recite additional elements that amount to significantly more than the recited judicial exceptions under revised step 2B of the 35 U.S.C. 101 analysis (see Applicant Remarks, Pages 19-21, dated 03/02/2026). Examiner respectfully disagrees.
Applicant argues the ordered combination of AI summary and modeling engines provides significantly more. The additional claim elements merely recite: generic server computing devices, generic processors, generic memory, generic AI engines, and conventional data processing operations. The claims do not recite: unconventional hardware, specialized AI architectures, novel training mechanisms, or technological improvements to machine learning itself. The ordered combination merely performs: data ingestion, summarization, modeling, analysis and reporting. This is an abstract-information-processing workflow. The claims therefore fail to provide an inventive concept beyond the judicial exception itself.
Examiner refers Applicant to Examiner’s 35 U.S.C. § 101 analysis section (e.g., Claim Rejections - 35 U.S.C. § 101 section shown below) shown for step 2B particularly for Independent Claims 1 and 11. The claims do not recite additional elements that amount to significantly more than the recited judicial exceptions, because they are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exceptions. The limitations are directed to limitations referenced in MPEP § 2106.05I.A. that are not enough to qualify as significantly more when recited in these claims with the abstract idea which include: (1) adding the words “apply it” (or an equivalent) with the judicial exception, (2) or mere instructions to implement an abstract idea on a computer and providing the results to the user on a computer, and (3) generally linking the use of the judicial exception to a particular technological environment or field of use.
Independent Claims 1 and 11: With respect to the additional elements of (e.g., “an AI modeling engine” & “AI summary engine” & “machine learning”) when considered with the recited claim limitations both individually and as an ordered combination (as a whole), these additional elements do not recite additional elements that amount to significantly more than the recited judicial exceptions under step 2B due to: (1) the claims as a whole are limited to a particular field of use or technological environment for calculating a sustainability impact indicating an overall impact of a plurality of legislations on the organization based on the financial impact of the legislation in a business enterprise environment (see MPEP § 2106.05(h)) or (2) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)).
For Independent Claims 1 and 11, even if the steps of (1) mere data gathering such as (e.g., “receive client data of an organization, the client data including parameters of products covered by the legislation”) and (2) mere data transmitting such as (e.g., “send the financial impact and the sustainability impact metric to a client device”) are evaluated as additional elements, these activities at most amount to insignificant extra-solution activities (see MPEP § 2106.05 (g)), which have been expressly recognized as Well-Understood, Routine and Conventional (WURC) under step 2B, and thus insufficient to add significantly more to the abstract idea. See MPEP § 2106.05(d) ii – Receiving or Transmitting Data over a Network, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359,1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
The ordered combination of elements in the Dependent Claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself. Therefore, under Step 2B, Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions.
Thus, Claims 1-20 are ineligible with respect to the 35 U.S.C. § 101 analysis.
Claim Rejections - 35 USC § 101
7. 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.
8. Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-20 are each focused to a statutory category namely, a “system” or an “apparatus” (Claims 1-10) and a “method” or a “process” (Claims 11-20).
Step 2A Prong One: Independent Claims 1 and 11 recites limitations that set forth the abstract idea(s), namely (see in bold except where strikethrough):
“” (see Independent Claim 1);
“ingest legislation data to generate a summary of legislation data the legislation data including tax rules published ” (see Independent Claim 1);
“ingest sustainability legislation data to generate a legislation model , the sustainability legislation data including tax rules of a legislation related to sustainability regulations” (see Independent Claim 1);
“generating, a summary of legislation data including tax rules published ” (see Independent Claim 11);
“generating, a legislation model based on sustainability legislation data including tax rules of a legislation related to sustainability regulations” (see Independent Claim 11);
“receive client data of an organization, the client data including parameters of products covered by the legislation” (see Independent Claims 1 and 11);
“apply the parameters of the client data to the legislation model to calculate a financial impact of the legislation on the organization” (see Independent Claims 1 and 11);
“calculate a sustainability impact metric indicating an overall impact of a plurality of legislation, including the sustainability legislation related to sustainability regulations and other legislation to which the organization is subject, based in part on the financial impact of the legislation” (see Independent Claims 1 and 11);
“send the financial impact and the sustainability impact metric ” (see Independent Claims 1 and 11);
“ is trained on legislation data ” (see Independent Claims 1 and 11);
“wherein at runtime, generates aspect of the legislation summary” (see Independent Claims 1 and 11).
Here, the claim limitations for Independent Claims 1 and 11 are directed to Certain Methods of Organizing Human Activities (specifically, managing business compliance, regulatory tax rules, and financial risk) combined with Mental Processes (analyzing, modeling, and comparing rules to client data). Evaluating how tax laws and sustainability rules affect an organization’s bottom line is an age-old practice that humans (tax attorneys, accountants, and business consultants) have historically done in their heads, on paper, or using basic commercial logic. Using AI and servers to automate these familiar human tasks doesn't change the foundational concept being claimed.
The first step of “Ingest legislation data to generate a summary of legislation data using an AI summary engine..." is a Mental Process or a Certain Method of Organizing Human Activity. Summarizing legal texts is a known mental process. Using AI to ingest text and output a summary does not add a technical, inventive component; it just automates a task traditionally done by human readers. The second step of "Ingest sustainability legislation data to generate a legislation model using an AI modeling engine..." is a Mental Process or a Certain Method of Organizing Human Activity. Modeling the impact of regulations is a fundamental economic/business practice. Training a machine learning model to simulate legislation is a mathematical concept executed on data to achieve a business goal. The third step of "Receive client data of an organization..." is a Certain Method of Organizing Human Activity which is a mere data gathering step. Reciting the basic receipt of data is insignificant extra-solution activity. It does not limit the scope of the underlying abstract idea. The fourth step of "Apply the parameters of the client data to the legislation model to calculate a financial impact..." is a Mathematical Concept or a Mental Process. This is the core abstract idea—applying rules to parameters to calculate a financial outcome. It describes nothing more than a mathematical calculation and economic analysis performed by a computer. The fifth step of "Calculate a sustainability impact metric indicating an overall impact of a plurality of legislation..." is a Mathematical Concept. Generating a custom "impact metric" is a calculation and a method of organizing human activity (business/compliance management). The sixth step of “Send the financial impact and the sustainability impact metric to a client device..." is a Certain Method of Organizing Human Activity and is a mere data transmission step.
Therefore, these abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Mental Processes” which pertains to (1) concepts performed in the human mind (including observations or evaluations or judgments) or (2) using pen and paper as a physical aid, in order to help perform these mental steps does not negate the mental nature of these limitations. The use of "physical aids" in implementing the abstract mental process, does not preclude the claim from reciting an abstract idea. See MPEP § 2106.04(a) III C.
Additionally, or alternatively, these abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Certain Methods of Organizing Human Activities” which pertains to (3) commercial interactions (including marketing or sales activities or behaviors; business relations) or (4) managing personal behavior or relationships or interactions between people (including teachings or following rules or instructions) and additionally or alternatively as “Mathematical Concepts” which pertains to (5) mathematical calculations.
That is, other than reciting the additional elements of (e.g., “a client device” & “memory” & “a server computing device” & “one or more processors” & “an AI modeling engine” & “AI summary engine”, “a public legislation publication repository”, etc…), nothing in the claim elements precludes the steps from being performed as “Mental Processes” Grouping which pertains to (1) concepts performed in the human mind (including observations or evaluations or judgments) or (2) using pen and paper as a physical aid, and additionally or alternatively as “Certain Methods of Organizing Human Activities” Grouping which pertains to (3) commercial interactions (including marketing or sales activities or behaviors; business relations) or (4) managing personal behavior or relationships or interactions between people (including teachings or following rules or instructions) and additionally or alternatively as “Mathematical Concepts” which pertains to (5) mathematical calculations.
Moreover, the mere recitation of generic computer components such as (e.g., “a client device” & “memory” & “a server computing device” & “one or more processors”) does not take the claims out of “Certain Methods of Organizing Human Activities” or “Mental Processes” or “Mathematical Concepts” Groupings.
Therefore, at step 2a prong 1, Yes, Claims 1-20 recite an abstract idea. We proceed onto analyzing the claims at step 2a prong 2.
Step 2A Prong Two: With respect to Step 2A Prong Two of the eligibility inquiry (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Independent Claim 1 recites additional elements directed to: (e.g., “a client device” & “memory” & “a server computing device” & “one or more processors” & “a public legislation publication repository”). Independent Claim 11 recites additional elements directed to: (e.g., “a client device” & “a public legislation publication repository”). These additional elements have been considered both individually and in combination, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment. See MPEP § 2106.05(f) and MPEP § 2106.05(h). Furthermore, in Independent Claims 1 and 11, even if the steps of (e.g., “receive client data of an organization, the client data including parameters of products covered by the legislation”) and (e.g., “send the financial impact and the sustainability impact metric to a client device”) are evaluated as additional elements, these activities at most amounts to first “mere data receiving” or “mere data collecting” and secondly as “mere data outputting” or “mere data transmitting” in which each of these steps shown above reflect insignificant extra-solution activities (see MPEP § 2106.05 (g)).
Independent Claims 1 and 11: With respect to the additional elements of (e.g., “an AI modeling engine” & “AI summary engine” & “machine learning”) when considered with the recited claim limitations both individually and as an ordered combination (as a whole), these additional elements do not integrate the abstract idea into a practical application under step 2a prong 2 according to the following: (1) the claims as a whole are limited to a particular field of use or technological environment for calculating a sustainability impact indicating an overall impact of a plurality of legislations on the organization based on the financial impact of the legislation in a business enterprise environment (see MPEP § 2106.05(h)) or (2) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)). Under USPTO guidelines, integrating an AI or abstract algorithm into a practical application requires more than just telling a computer to "apply it". These claims recite an AI summary engine and an AI modeling engine, but it fails to define how these engines function mechanically, structurally, or computationally in a new way. These claims use general computing elements (a "server computing device," "one or more processors," "associated memory," "client device"). Using a generic computer to perform mental or mathematical steps does not transform the abstract idea into a practical application; it merely reduces the abstract idea to digital automation. Because the claims lack specific, concrete implementations (e.g., a novel neural network architecture, a specialized data structure, or a specific technological solution to a computing problem), the claims do not integrate the judicial exceptions into a practical application under Prong 2.
In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception. Therefore, at step 2a prong 2, Claims 1-20 are directed to the abstract idea and do not recite additional elements that integrate into a practical application.
Step 2B: (As explained in MPEP § 2106.05), it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Independent Claim 1 recites additional elements directed to: (e.g., “a client device” & “memory” & “a server computing device” & “one or more processors” & “a public legislation publication repository”). Independent Claim 11 recites additional elements directed to: (e.g., “a client device” & “a public legislation publication repository”). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (computing environment) and does not amount to significantly more than the abstract idea itself. See MPEP § 2106.05(f) and MPEP § 2106.05(h). Notably, Applicant’s Specification suggests that the claimed invention relies on nothing more than a general-purpose computer executing the instructions to implement the invention (see at least Applicant’s Specification ¶ [0029]: “Computing system 700 may take the form of one or more personal computers, server computers, tablet computers, home-entertainment computers, network computing devices, gaming devices, mobile computing devices, mobile communication devices (e.g., smartphone), and/or other computing devices, and wearable computing devices such as smart wristwatches and head mounted augmented reality devices.” and also Applicant’s Specification ¶ [0041]: “The specific routines or methods described herein may represent one or more of any number of processing strategies.”).
Independent Claims 1 and 11: With respect to the additional elements of (e.g., “an AI modeling engine” & “AI summary engine” & “machine learning”) when considered with the recited claim limitations both individually and as an ordered combination (as a whole), these additional elements do not recite additional elements that amount to significantly more than the recited judicial exceptions under step 2B due to: (1) the claims as a whole are limited to a particular field of use or technological environment for calculating a sustainability impact indicating an overall impact of a plurality of legislations on the organization based on the financial impact of the legislation in a business enterprise environment (see MPEP § 2106.05(h)) or (2) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)).
For Independent Claims 1 and 11, even if the steps of (1) mere data gathering such as (e.g., “receive client data of an organization, the client data including parameters of products covered by the legislation”) and (2) mere data transmitting such as (e.g., “send the financial impact and the sustainability impact metric to a client device”) are evaluated as additional elements, these activities at most amount to insignificant extra-solution activities (see MPEP § 2106.05 (g)), which have been expressly recognized as Well-Understood, Routine and Conventional (WURC) under step 2B, and thus insufficient to add significantly more to the abstract idea. See MPEP § 2106.05(d) ii – Receiving or Transmitting Data over a Network, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359,1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
The additional element of “artificial intelligence” or “machine learning” in Independent Claims 1 and 11 does not amount to significantly more than the judicial exception under step 2B due to being expressly recognized as Well-Understood, Routine and Conventional (WURC) in the art.
See for example; US PG Pub (US 2021/0173711 A1) – “Integrated Value Chain Risk-Based Profiling and Optimization”, hereinafter Crabtree, et. al. Crabtree noting at ¶ [0025]: “A directed computational graph (DCG) module orchestrates a data ingestion workflow that ingests, extracts, validates, and enriches the data using a combination of natural language processors, ontological processors, provenance metadata extraction, and machine learning to train an algorithm for categorization and labelling of data. The model(s) that can be built with this data will be enable better understanding of business cycles and long-term growth and navigate technological change.” Crabtree noting at ¶ [0039]: “Artificial intelligence” or “AI” as used herein means a computer system or component that has been programmed in such a way that it mimics some aspect or aspects of cognitive functions that humans associate with human intelligence, such as learning, problem solving, and decision-making.” Crabtree noting at ¶ [0056]: “The ability to handle data provenance and metadata tracking is of prime importance (given restrictions on usage of different data under HIPAA, CPRA, GDPR, etc.) when creating the best overall data set, which may be partially common and partially distinct for different use cases.” Crabtree noting at ¶ [0062]: “If a risk query was initiated by a mask manufacturing company that produced N-95 masks, the knowledge graph generated would include vertices and edges derived from public/legal discourse as well as proposed governmental legislation that a potential law requiring all citizens to wear a mask may be imminent.”
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent Claims 2-10 and 12-20 recite the same abstract ideas as Independent Claims 1 and 11 along with further steps/details that could (1) are concepts performed in the human mind as “Mental Processes” (which include observations or evaluations or judgments) or (2) using pen to paper as a “physical aid” and additionally or alternatively as “Certain Methods of Organizing Human Activities” which pertains to (3) commercial interactions (including marketing or sales activities or behaviors; business relations) or (4) managing personal behavior or relationships or interactions between people (including teachings or following rules or instructions) and additionally or alternatively as “Mathematical Concepts” which pertains to (5) mathematical calculations.
Furthermore, Dependent Claims 2-3, 5-7, 9-10, 12-13, 15-17 and 19-20 further narrows the abstract ideas with the same or similar additional elements identified in Independent Claims 1 and 11, and are therefore ineligible for the same reasons previously provided in Step 2A Prong 2 and Step 2B. Dependent Claims 4, 8, 14 and 18: With respect to reliance on (e.g., “server computing device” (see Dependent Claims 4 and 8) & “automatically” (see Dependent Claims 8 and 18)) as additional elements when considered individually and in combination (as a whole) with these recited claim limitations, these additional elements do not integrate the abstract idea into a practical application under step 2a prong 2 and also secondly do not amount to significantly more than the judicial exceptions under step 2B due to: (1) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)) or (2) the claims as a whole are limited to a particular field of use or technological environment for calculating a sustainability impact indicating an overall impact of a plurality of legislations on the organization based on the financial impact of the legislation in a business enterprise environment (see MPEP § 2106.05(h)).
The additional element of “artificial intelligence” or “AI modeling engine” in Independent Claims 1 and 11 does not amount to significantly more than the judicial exception under step 2B due to being expressly recognized as Well-Understood, Routine and Conventional (WURC) in the art.
See for example; US PG Pub (US 2021/0173711 A1) – “Integrated Value Chain Risk-Based Profiling and Optimization”, hereinafter Crabtree, et. al. Crabtree noting at ¶ [0025]: “A directed computational graph (DCG) module orchestrates a data ingestion workflow that ingests, extracts, validates, and enriches the data using a combination of natural language processors, ontological processors, provenance metadata extraction, and machine learning to train an algorithm for categorization and labelling of data. The model(s) that can be built with this data will be enable better understanding of business cycles and long-term growth and navigate technological change.” Crabtree noting at ¶ [0039]: “Artificial intelligence” or “AI” as used herein means a computer system or component that has been programmed in such a way that it mimics some aspect or aspects of cognitive functions that humans associate with human intelligence, such as learning, problem solving, and decision-making.” Crabtree noting at ¶ [0056]: “The ability to handle data provenance and metadata tracking is of prime importance (given restrictions on usage of different data under HIPAA, CPRA, GDPR, etc.) when creating the best overall data set, which may be partially common and partially distinct for different use cases.” Crabtree noting at ¶ [0062]: “If a risk query was initiated by a mask manufacturing company that produced N-95 masks, the knowledge graph generated would include vertices and edges derived from public/legal discourse as well as proposed governmental legislation that a potential law requiring all citizens to wear a mask may be imminent.”
The ordered combination of elements in the Dependent Claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself. Therefore, under Step 2B, Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. Thus, Claims 1-20 are ineligible with respect to the 35 U.S.C. § 101 analysis.
Examining Claims with Respect to Prior Art
9. Applicant’s arguments, see pages 22-24 of 25 filed on 03/02/2026, with respect to the 35 U.S.C. § 103 Claim Rejections for Claims 1-2, 4, 8-12, 14 and 18-20 have been fully considered and are found to be persuasive. Therefore, Claims 1-20 have overcome the prior art rejections. Please note that the following issues still remain: (1) 35 U.S.C. § 101 Claim Rejections for Claims 1-20.
Regarding Independent Claims 1 and 11, there is no disclosure in the existing prior art or any new art that either teaches and/or discloses the sequence operation of features either individually or in combination relating to:
- ingest legislation data to generate a summary of legislation data using an AI summary engine, the legislation data including tax rules published in a public legislation publication repository;
- ingest sustainability legislation data to generate a legislation model using an AI modeling engine, the sustainability legislation data including tax rules of a legislation related to sustainability regulations;
- the AI summary engine is trained on legislation data through machine learning;
- at runtime, the AI summary engine generates aspects of the legislation summary.
The closest prior arts are as follows:
#1) US PG Pub (US 2023/0404128 A1) – “System and Method for Determining the Environmental Impact and Sustainability Score of a Food Item Using a Food Ingredient Repository”, hereinafter O’Kelly, et. al.
#2) US PG Pub (US 2022/0405590 A1) – “Machine Learning Models for Automated Sustainability Data Source Ingestion and Processing”, hereinafter Hebets.
#3) US PG Pub (US 2018/0300793 A1) – “Augmenting Sustainable Procurement Data with Artificial Intelligence”, hereinafter Chen, et. al.
#4) US PG Pub (US 2025/0131451 A1) – “Method or System for Determining and/or Evaluating a Sustainability of a Product, a Service, and Organization and/or a Person”, hereinafter Dert.
Regarding Independent Claim 1, O’Kelly of a sustainability planner teaches or suggests that at ¶ [0150]: “the processor may determine whether the requester provided a suggested profit margin and taxation information so that a recommended retail price (RRP) may be calculated for the food item. In some jurisdictions, the amount of tax charged on a food item may relate to the EISS values for the food item.” See also O’Kelly at ¶ [0165]: “The methods may include determining, by a processor, relevant unit types for the requester (e.g., mass, volume, portion size, energy), determining EISS values based on the relevant unit types, determining relevant environmental impact and sustainability factors for the requester (e.g., greenhouse gas emissions, water consumption), adjusting EISS values based on the factors determined to be relevant, determining required label format(s) for the requester, generating EISS labels based on the required label formats (including external accreditation mark(s) on the label if applicable, etc.), determining the total cost of the food item (e.g., based on cost and quantity information of each food ingredient, the requester's energy consumption, etc.), determining a recommended retail price based on suggested profit margin and/or taxation information.” Moreover, O’Kelly teaches that at ¶ [0219]: “In block 1122, the processor may ingest complex food item descriptions from one or more sources.”
However, neither O’Kelly and the other prior art of record do not reach or render obvious the sequence of limitations directed to:
- ingest legislation data to generate a summary of legislation data using an AI summary engine, the legislation data including tax rules published in a public legislation publication repository;
- ingest sustainability legislation data to generate a legislation model using an AI modeling engine, the sustainability legislation data including tax rules of a legislation related to sustainability regulations;
- the AI summary engine is trained on legislation data through machine learning;
- at runtime, the AI summary engine generates aspects of the legislation summary.
Regarding Hebets reference, Hebets of a sustainability planner teaches or suggests that at ¶ [0036]: “Example systems may scrape data based on specific keywords, phrases, etc., which may include automatically crawling and scraping open source datasets for updates. Multiple brands may be compared by displaying detailed score breakdowns side by side. Example systems may auto-generate text summaries of each entity's ranking or score, may allow for user interfaces to be modified based on user preferences, may provide entity sustainability reports to users, may automatically add new brands to a scoring output, may automatically update scores based on new data, may verify collected information based on automated internal validation, may upload approved brands into a marketplace, etc.” See also Hebets at ¶ [0032]: “Example sustainability data that may be ingested according to specified ingestion criteria includes, but is not limited to, sustainability reports and environmental performance of entities (e.g., apparel brands) taken from website scraping and PDF extraction, third party sustainability audit verifier information, revenue and financial performance data.”
However, neither Hebets and the other prior art of record do not reach or render obvious the sequence of limitations directed to:
- ingest legislation data to generate a summary of legislation data using an AI summary engine, the legislation data including tax rules published in a public legislation publication repository;
- ingest sustainability legislation data to generate a legislation model using an AI modeling engine, the sustainability legislation data including tax rules of a legislation related to sustainability regulations;
- the AI summary engine is trained on legislation data through machine learning;
- at runtime, the AI summary engine generates aspects of the legislation summary.
Regarding Chen reference, Chen of a sustainability planner teaches or suggests the following:
- receive client data of an organization including parameters covered by the legislation (see at least Chen: ¶ [0030-0031] & ¶ [0040] & ¶ [0221]. Chen notes evaluating the benefits captured in sales order receipts generated from transactions made through e-procurement systems, or bids and contracts won. In showing buyers and sellers how new ways of aggregating and integrating existing data can help them make faster, more accurate purchasing decisions that benefit our planet, people, local economies, public procurement organizations like cities and schools can save many hours per year (e.g., 10,000-50,000) in time freed up by the system, and between 2%-5% cost savings on average for categories in which economies of scale have been achieved for green purchasing by our analytics, and previously undiscovered local vendors with sustainable practices can gain market visibility by the datasets. See also “product/material categories of text of a law or regulation in Chen at Fig. 6A-6B, Fig. 7 & Fig. 14.”)
- apply the parameters of the client data to the legislation model (see at least Chen: Figs. 1-2 & ¶ [0031] & ¶ [0230-0231] & ¶ [0240-0242]. Chen notes that if decision theory and game theory are broadened to encompass other-regarding preferences, they may become capable of modeling all aspects of decision making involved in finding, comparing, purchasing, tracking and reporting on product information, including those normally considered for cost (finance), quality (end user) and compliance (health and safety, sustainability). See also Chen at ¶ [0068-0070]: Specification data 240 is related to one or more specifications, and may include product category data 242, impact area data 244, offset data 246, and product data 248. The data 240-248 may be raw and/or annotated, as discussed elsewhere herein. See also Chen at ¶ [0230-0231]: Coffee B may have a product benefit efficiency score of 60% and may cost $3 per lb., while Coffee C has a product benefit efficiency score of 65% and has a cost of $4 per lb. The product benefit efficiency improvement rate for Coffee B can be calculated as: (60%−50%)/($3−$2)=10%/$1=10% per dollar.) to calculate a financial impact of the legislation on the organization (see at least Chen: ¶ [0032] & ¶ [0041] & ¶ [0075] & ¶ [0213]. Chen notes that purchasing dollars become a powerful asset in influencing sustainability (e.g., environmental impacts on the planet, social impacts on people and animals, and fiscal impacts on local economies). See also Chen at ¶ [0041]: Governments have a systems-level infrastructure opportunity for increasing the transparency of environmental, social, and fiscal impacts behind the things they buy with regulatory compliance. See also Chen at ¶ [0075]: The data annotation engine 112 annotates various products and/or product categories according to the one or more raw data sources, and a determination can be made as to the various environmental, social, and fiscal impacts associated with production of various products. See also Chen at ¶ [0213]: Chen teaches that the price a product based on its True Value or True Cost, i.e., its financial cost adjusted for environmental/social/fiscal impacts per product category.)
- calculate a sustainability impact metric (see at least Chen: ¶ [0051] & ¶ [0170] & ¶ [0221-0222]. Chen notes that offers a data-driven sustainability management system which uses structured datasets to help organizations accurately measure, mitigate, and report on their total consumption environmental and social impact from procurement spend. For example, if 100 specifications discuss the product category “paper,” and 85 of the specifications discuss the impact of paper on generating waste, then the weight value of the “waste” impact area on the “paper” product category equals 85/100=85%.) indicating an overall impact of a plurality of legislation including the legislation and other legislation on the organization (see at least Chen: Fig. 10 & ¶ [0146-0149] & ¶ [0231-0233]. Chen notes that where c.sub.1,a.sub.1 are fitted coefficients and p, the x-axis value, is the number of similar practices required between the product categories. c.sub.1—sets the height of the curve and can be tuned to ensure that the maximum value is the max number of stages (e.g., 6). a.sub.1—sets the gradient of the curve. See at least Chen at ¶ [0231-0233] & ¶ [0242-0244]: The rate of improvement per dollar for Coffee C with respect to Coffee A is only 7.5% per dollar, which is below the average. Switching from Paper A to Paper B would cost $1/case, and $100 in total spend. As such, if this single change was made, the organization's total spend would increase from $2100 to $2200, but its green spend efficiency would increase from 0% to $600/$2200=27%. In other words, the organization could spend $100 to increase green spend efficiency by 27%. The rate of improvement would be 27%/$100=0.27% per dollar. See at least Chen at ¶ [0311]: Processes used to verify the compliance of a person, process, product, service, or system to either a standard or a regulation (eg. testing, certification, inspection). See at least Chen at ¶ [0319]: Federal Acquisition Regulation—Outlines mandatory federal sustainability purchasing requirements. See at least Chen at ¶ [0330]: Technical Regulation: Technical specification for a person, process, product, service, or system—compliance is MANDATORY.) based at least on the financial impact of the legislation (see at least Chen: ¶ [0032] & ¶ [0041] & ¶ [0075] & ¶ [0213]. Chen notes that purchasing dollars become a powerful asset in influencing sustainability (e.g., environmental impacts on the planet, social impacts on people and animals, and fiscal impacts on local economies). See also Chen at ¶ [0041]: Governments have a systems-level infrastructure opportunity for increasing the transparency of environmental, social, and fiscal impacts behind the things they buy with regulatory compliance. See also Chen at ¶ [0075]: The data annotation engine 112 annotates various products and/or product categories according to the one or more raw data sources, and a determination can be made as to the various environmental, social, and fiscal impacts associated with production of various products. See also Chen at ¶ [0213]: Chen teaches that the price a product based on its True Value or True Cost, i.e., its financial cost adjusted for environmental/social/fiscal impacts per product category.).
However, neither Chen and the other prior art of record do not reach or render obvious the sequence of limitations directed to:
- ingest legislation data to generate a summary of legislation data using an AI summary engine, the legislation data including tax rules published in a public legislation publication repository;
- ingest sustainability legislation data to generate a legislation model using an AI modeling engine, the sustainability legislation data including tax rules of a legislation related to sustainability regulations;
- the AI summary engine is trained on legislation data through machine learning;
- at runtime, the AI summary engine generates aspects of the legislation summary.
Regarding Dert reference, Dert of a sustainability planner teaches or suggests that at ¶ [0178]: “For example, the sustainability of a person 4 can be connected to other possible ways to ensure that the sustainability can be reached. For example, the sustainability of a person 4 can be connected to a tax system and/or other methods which affect the working or personal life of the person 4. Thus, incentives could be provided to improve the sustainability of the person 4.” ¶ [0258]: “A sustainability/sustainable economy can in particular be reached by changing sustainabilities, measuring the sustainability and in particular with regard to a taxation or taxation alternative concerning the sustainability.” ¶ [0844]: “SBT is particularly taxation based on the sustainability of products 1 and services 2 purchased (sales tax), overall income earned and spent (income tax) and location-based impacts (property tax). The larger tax basis especially allows a lower tax rate for the same tax revenues. Under SBT, initially only the non-sustainable product fraction is in particular taxable, where the non-sustainable product fraction is in particular calculated as 100% minus the product sustainability percentage.”
However, neither Dert and the other prior art of record do not reach or render obvious the sequence of limitations directed to:
- ingest legislation data to generate a summary of legislation data using an AI summary engine, the legislation data including tax rules published in a public legislation publication repository;
- ingest sustainability legislation data to generate a legislation model using an AI modeling engine, the sustainability legislation data including tax rules of a legislation related to sustainability regulations;
- the AI summary engine is trained on legislation data through machine learning;
- at runtime, the AI summary engine generates aspects of the legislation summary.
Therefore, when taken as a whole, the claims are not rendered obvious as the available prior art does not suggest or otherwise render obvious the noted features nor do the available art suggest or otherwise render obvious further modification of the evidence at hand. Such modification would require substantial reconstruction relying solely on improper hindsight bias, and thus would not be obvious.
Conclusion
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 DERICK HOLZMACHER whose telephone number is (571) 270-7853. The examiner can normally be reached on Monday-Friday 9:00 AM – 6:30 PM EST.
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/DERICK J HOLZMACHER/Patent Examiner, Art Unit 3625A
/BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625
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