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 05/04/2026 regarding application 18/505,535. Claims 1, 8, 10, 17 and 19 have been amended. Claims 7 and 16 have been canceled. Claims 1-6, 8, 10-15, 17 and 19 are pending and have been rejected.
Response to Amendments
2. Applicant’s amendment filed on 05/04/2026 necessitated new grounds of rejection in this office action.
Response to Arguments
3. Applicant’s arguments, see pages 13-14 filed on 05/04/2026, with respect to the 35 U.S.C. § 103 Claim Rejections for Claims 1-6, 10-15 and 19 have been fully considered and is found to be persuasive. Therefore, the 35 U.S.C. § 103 Claim Rejections for Claims 1-6, 8, 10-15, 17 and 19 have been withdrawn. See Examining Claims with Respect to Prior Art Section shown below.
Response to 35 U.S.C. § 101 Arguments
4. Applicant’s 35 U.S.C. § 101 arguments, filed with respect to Claims 1-6, 8, 10-15, 17 and 19 have been fully considered, but they are found not persuasive (see Applicant Remarks, Pages 8-13 dated 05/04/2026). Examiner respectfully disagrees.
Argument #1:
(A). Applicant argues that Claims 1-6, 8, 10-15, 17 and 19 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 11-13, dated 05/04/2026). Examiner respectfully disagrees.
Specifically, Applicant argues that none of the limitations listed above for Independent Claims 1, 10 and 19 amount to mere mental processes nor methods of organizing human activities even under the broadest reasonable interpretation (see Applicant Remarks, Pages 11-13, dated 05/04/2026). Examiner respectfully disagrees.
The arguments have been fully considered, but are not persuasive. Claims 1-8, 10-17 and 19 remain rejected under 35 U.S.C. §101 because the claims, viewed as a whole, are directed to an abstract idea and do not integrate the judicial exception into a practical application. Further, the claims fail to recite significantly more than the abstract idea itself.
Applicant’s arguments do not overcome the determination that the claims recite abstract subject matter. Independent Claims 1, 10 and 19 are directed to collecting evaluation information, analyzing and weighting the information, generating evaluation scores, and presenting/publishing the evaluation results.
Such activities fall squarely within recognized categories of abstract ideas, including:
Mental processes and Certain Methods of Organizing Human Activities. The claims concern evaluating suppliers or other enterprise entities using questionnaire responses and quantitative indicators to produce performance scores for enterprise consumption. Supplier evaluation and organizational performance assessment are fundamental business practices and commercial management activities.
The claims further recite concepts that can be practically performed mentally or with pen and paper, including: receiving responses, determining scores, applying weights, evaluating entities, and generating overall rankings or assessments.
Moreover, some of these claim limitations for example can be performed as “Mathematical Concepts” which pertains to mathematical calculations or mathematical relationships. With respect to “Mathematical Concepts” category, Examiner refers Applicant to MPEP § 2106.04 (a) (2) (I) (C): “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping.” “It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea).” Furthermore, see MPEP § 2106.05 (c): “For data, mere "manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’" has not been deemed a transformation. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360, 31 USPQ2d 1754, 1755, 1759 (Fed. Cir. 1994)).”
Although the claims are implemented using generic computer components and machine learning terminology, the focus of the claims remains the abstract process of evaluating enterprise entities using collected information. As explained in Electric Power Group, LLC v. Alstom S.A., merely collecting information, analyzing the information, and displaying certain results of the collection and analysis is abstract even where the information is limited to a particular technological environment. Similarly, in SAP America, Inc. v. InvestPic, LLC, claims directed to performing financial analysis using statistical techniques were held abstract despite implementation through computerized mathematical models.
Applicant’s reliance on machine learning and GAN terminology does not remove the claims from abstraction. Courts have repeatedly explained that merely applying generic machine learning or mathematical models to an abstract business problem does not render claims patent eligible. The claims do not recite a technological improvement to machine learning itself, GAN architectures themselves, database structures themselves, or network communication protocols themselves. Instead, the claimed machine learning model is used as a tool to perform the abstract business objective of supplier evaluation.
Applicant argues that the human mind cannot practically perform the claimed operations (see Applicant Remarks, last ¶ of Page 11, dated 05/04/2026). Examiner respectfully disagrees.
This argument is not persuasive because the claims merely automate conventional business evaluation activities using generic computer technology. Humans can conceptually evaluate suppliers, review questionnaires, apply weights to evaluation criteria, compare accurate versus inaccurate evaluations, generate composite scores, and distribute evaluation reports. That the claims use computers to perform these activities more quickly or at larger scale does not remove the claims from the mental-process category.
With respect to “Mental Processes” category, Examiner refers Applicant to MPEP § 2106.04 (a) (2) (III) (C): “Claims can recite a mental process even if they are claimed as being performed on a computer. The Examiner has reviewed Applicant’s Specification and determined that the claimed invention is described as concepts that are performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer (e.g., see Applicant’s Specification ¶ [0139]: “These various aspects or features can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.”), or 2) in a computer environment (e.g., see Applicant’s Specification ¶ [0090]: “The applications can include various add-in functionalities or can be standalone computing products and/or functionalities. Upon activation within the applications, the functionalities can be used to generate the user interface provided via the input/output device 940. The user interface can be generated and presented to a user by the computing system 900 (e.g., on a computer screen monitor, etc.”), or 3) is merely using a computer as a tool.
Additionally, according to MPEP § 2106.04 (a) (2) (III) (B): “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, 839 F.3d at 1139, 120 USPQ2d at 1474 (holding that claims to the mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper").”
Further, the claims concern organizational evaluation and enterprise management activities, which fall within certain methods of organizing human activity under the Revised Guidance. Also, Examiner refers Applicant to MPEP § 2106.04 (a) (2) II which states that: “the sub-groupings encompass both activity of a single person and activity that involves multiple people, and thus, certain activity between a person and a computer may fall within the "Certain Methods of Organizing Human Activities" groupings. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings.”
In conclusion, Examiner maintains that Claims 1-6, 8, 10-15, 17 and 19 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 #2:
(B). Applicant argues that Claims 1-6, 8, 10-15, 17 and 19 recite additional elements that integrate the judicial exception into a practical application under revised step 2a prong two of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Pages 9-11, dated 05/04/2026). Examiner respectfully disagrees.
Specifically, Applicant argues that Independent Claims 1, 10 and 19 recite additional elements, and those elements integrate the abstract ide into a practical application as the claims improve the technical field of enterprise resource planning (ERP) applications (see Applicant Remarks, 2nd ¶ of Page 9, dated 05/04/2026). Examiner respectfully disagrees.
Applicant’s arguments regarding alleged improvements to ERP systems are not persuasive because the claims do not recite a technological improvement to computer functionality or another technical field. Instead, the claims merely use generic computer technology as a tool to automate an otherwise abstract business evaluation process. First, the alleged improvement is a business improvement and not a technological improvement. Applicant asserts that the claims improve ERP applications by allowing more efficient supplier evaluations. However, improving the efficiency of supplier evaluation is itself a business or organizational improvement, not a technological improvement.
The claims do not improve processor operation, memory architecture, database functionality, network transmission techniques, user interface technology, or machine learning algorithms themselves. Rather, the claims merely use computing components to gather enterprise data, evaluate suppliers, calculate scores, and distribute results. Any alleged reduction in memory, bandwidth, or processor usage is described only at a high level and is merely a consequence of automating a business process using generic computing systems. The claims themselves do not recite any particular mechanism that technologically improves computer resource utilization.
Independent Claims 1, 10 and 19: With respect to reliance on (e.g., “evaluation harmonizer” & “generative adversarial network” & “machine learning (ML) model” & “content library” & “enterprise resource planning (ERP) system” & “a first user interface”) as additional elements shown in Independent Claims 1, 10 and 19 when considered individually and as an ordered combination (as a whole) in view of these claim limitations, this additional element does not provide limitations that are indicative of integration into a practical application under step 2a prong 2 due to the following: (1) recites mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions by providing the results to the user on a computer (see MPEP § 2106.05 (f)) or (2) limiting a particular field of use or technological environment pertaining to creating an evaluation service to evaluate one or more entities (e.g., in this case suppliers) by selecting a first scorecard template and a second scorecard template and publishing the results on a user interface showing an aggregated score of evaluating suppliers using a computer in a business operations enterprise environment (see MPEP § 2106.05(h)). While Independent Claims 1, 10 and 19 use a generative adversarial network (GAN), the claims do not recite how the GAN architecture is modified or improved. It simply uses the GAN to “apply weights” to scores, which is a use of a ML model for data processing.
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.
For Independent Claims 1, 10 and 19, the recited “evaluation harmonizer,” “database,” “machine learning model,” “content library,” “communication network,” “user interface,” “processor,” and “memory” are all recited functionally and at a high level of generality. The claims do not recite specialized hardware, improve database structures, improve GAN training techniques, new neural network architectures, or unconventional network protocols. Instead, the claims merely invoke generic computing infrastructure as tools to implement the abstract evaluation process. As explained in Alice Corp. v. CLS Bank International, generic computer implementation of an abstract idea does not confer eligibility.
Applicant argues that the recited generative adversarial network constitutes a technological improvement (see Applicant Remarks, Pages 10-11, dated 05/04/2026). Examiner respectfully disagrees.
However, the claims merely recite use of a known machine learning technique for its ordinary purpose: generating outputs from input data using training data. The claims do not recite a new GAN architecture, a new adversarial training methodology, improve convergence techniques, reduce training instability, improve neural network performance, or improvements to machine learning technology itself.
Instead, the GAN is simply applied to produce supplier evaluation scores. Under current precedent, applying known AI or machine learning techniques to an abstract business problem does not integrate the abstract idea into a practical application. The claims therefore resemble those found abstract in Recentive Analytics, Inc. v. Fox Corp., where machine learning was used as a generic analytical tool within an abstract business workflow.
Applicant’s arguments that the claims are rooted in ERP systems are unpersuasive (see Applicant Remarks, Pages 9-11, dated 05/04/2026). Examiner respectfully disagrees.
Limiting an abstract idea to a particular field of use, such as ERP applications or supplier management, does not integrate the exception into a practical application. The claims merely apply abstract scoring and evaluation techniques within the environment of enterprise resource planning systems. As explained in Affinity Labs of Texas, LLC v. DIRECTV, LLC, restricting an abstract idea to a particular technological environment is insufficient for eligibility.
Argument #3:
(C). Applicant argues that for Independent Claims 1, 10 and 19 that these amended steps represents an application of ML based techniques that is unconventional in the field of ERP systems, confining the claims to a particular useful application in which an ERP application can generate analytics for many different suppliers using a single consistent process, which saves memory, bandwidth, and processor resources and secondly that is recited at such a specific, granular level that is distinguishable from mere generic machine learning techniques (see Applicant Remarks, Page 10, dated 05/04/2026). Examiner respectfully disagrees.
Examiner responds by stating that the argument that using GANs is “unconventional in the field of ERP systems” is insufficient to establish eligibility. The Supreme Court (e.g., Alice Corp) and subsequent USPTO Guidance state that limiting an abstract idea to a particular technological field (e.g., ERP analytics) does not make it patent-eligible. Merely taking a known mathematical tool (GAN weighting) and applying it to a specific dataset (supplier scores) is a “field of use” limitation, not a technical improvement to the ERP system’s underlying code or hardware. The assertion that a “single, consistent process” saves memory, bandwidth, and processor resources is a conclusionary statement not supported by the claim language. No Technical Mechanism: Independent Claims 1, 10 and 19 do not recite how these resources are saved. For example, it does not describe a novel data compression algorithm or a specific memory management protocol. Any automated system is “faster” or “more consistent” than a human process. These are the inherent benefits of automation, not a “technical solution to a technical problem.” Speed or consistency gains derived from automating a business process does not constitute an inventive concept. The argument that the ML is recited at a “granular level” is misplaced. Functional vs. Structural: These claims describe the GAN functionally (what it does: “generate total score”, “apply weights”) rather than structurally (how it is built: specific layers, loss functions, or mathematical breakthroughs). Identifying specific training data (questionnaires and responses) is a data gathering step. Specifying the type of data an algorithm processes does not transform the algorithm into a technical invention. Because the “granular” detail focuses on the mathematical weighting of scores for an administrative outcome, it remains directed to a judicial exception (Mathematical Concept / Mental Processes) without adding an unconventional technical step that changes the nature of these claims. These claims are patent ineligible because they use generic tools to perform a fundamental business task. The cited “efficiencies” are the result of basic automation rather than a technical breakthrough in ERP system architecture.
Argument #4:
(D). Applicant argues that Claims 1-6, 8, 10-15, 17 and 19 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 12-13, dated 05/04/2026). Examiner respectfully disagrees.
Applicant’s arguments regarding inventive concept are also unpersuasive. The additional claim elements, individually and as an ordered combination, merely recite well-understood, routine, and conventional computer functions, including: data gathering, database retrieval, score calculation, interface population, and network publication.
The claims do not recite any unconventional technological implementation beyond the abstract idea itself. Moreover, merely invoking machine learning or a GAN does not provide an inventive concept absent a specific technological improvement to machine learning technology itself. The claims recite the GAN functionally, as a generic scoring engine that applies weights to input data.
The claims do not recite: specific neural network structures, training algorithms, parameter update rules, discriminator-generator interactions, or any unconventional machine-learning mechanics. Thus, the machine learning limitations merely append analytical technology to the abstract idea.
Applicant argues that the ordered combination of limitations is inventive.
However, the sequence of: collecting evaluation information, processing the information, generating scores, displaying results, and publishing reports is itself conventional and routinely performed in enterprise analytics systems.
The claims merely automate this longstanding business workflow using generic computing technology. Under Two-Way Media Ltd. v. Comcast Cable Communications, LLC, functional claiming of desired results without reciting specific technological means does not provide an inventive concept.
Moreover, 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, 10 and 19. 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, 10 and 19: With respect to reliance on (e.g., “evaluation harmonizer” & “generative adversarial network” & “machine learning (ML) model” & “content library” & “enterprise resource planning (ERP) system” & “a first user interface”) as additional elements shown in Independent Claims 1, 10 and 19 when considered individually and as an ordered combination (as a whole) in view of these claim limitations, this additional element does not provide limitations that are indicative of integration into a practical application under step 2a prong 2 due to the following: (1) recites mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions by providing the results to the user on a computer (see MPEP § 2106.05 (f)) or (2) limiting a particular field of use or technological environment pertaining to creating an evaluation service to evaluate one or more entities (e.g., in this case suppliers) by selecting a first scorecard template and a second scorecard template and publishing the results on a user interface showing an aggregated score of evaluating suppliers using a computer in a business operations enterprise environment (see MPEP § 2106.05(h)). While Independent Claims 1, 10 and 19 use a generative adversarial network (GAN), the claims do not recite how the GAN architecture is modified or improved. It simply uses the GAN to “apply weights” to scores, which is a use of a ML model for data processing.
With respect to Independent Claims 1, 10 and 19, certain/particular limitations shown recite (1) “mere data gathering” (e.g., “receiving one or more responses to the one or more messages” (see Independent Claims 1, 10 and 19) & “obtaining one or more second scores from a database, the one or more second scores comprising one or more quantitative key indicators associated with the one or more entities” (see Independent Claims 1, 10 and 19)) and “mere data outputting” (e.g., “causing one or more messages to be sent to one or more evaluators” (see Independent Claims 1, 10 and 19)) wherein which each of these claim limitations reflects mere insignificant extra-solution activities (see MPEP § 2106.05 (g)). Furthermore, these certain/particular claim limitations as demonstrated above for Independent Claims 1, 10 and 19 reflects Well-Understood, Routine and Conventional Activities (WURC) under MPEP § 2106.05 (d) ii: See Receiving or transmitting data over a network, e.g., using the Internet to gather data, 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).
Applicant cites the August 4th, 2025 Memo regarding subject matter eligibility of claims under 35 U.S.C. 101 which provides an important reminder that the analysis in Step 2A Prong 2 considers the claim as a whole, rather than viewing the additional elements in isolation (see Applicant’s Remarks, last ¶ Page 12, dated 05/04/2026).
Applicant correctly notes that claims must be evaluated as a whole. The Office Action has done so. Even considering the claims as an ordered combination, the claims merely recite: collection of evaluation data, machine-learning-based score generation, and publication of results within an enterprise environment. The additional elements interact only to implement the abstract business evaluation process using generic computer technology. No claim limitation or ordered combination transforms the claims into a technological improvement to computers or another technical field.
In conclusion, Claims 1-6, 8, 10-15, 17 and 19 are directed to the abstract idea of evaluating enterprise entities using collected information and machine-learning-based scoring techniques. The additional claim elements merely implement the abstract idea using generic computer technology within a conventional ERP environment. The claims do not improve computer functionality, do not improve machine learning technology, do not improve network technology, and do not recite significantly more than the abstract idea itself. Accordingly, Claims 1-8, 10-17, and 19 remain patent ineligible under 35 U.S.C. §101.
Claim Rejections - 35 USC § 101
5. 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.
6. Claims 1-6, 8, 10-15, 17 and 19 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-6, 8, 10-15, 17 and 19 are each focused to a statutory category namely, a “system” or an “apparatus” (Claims 1-6 and 8), a “process” or a “method” (Claims 10-15 and 17), and a “non-transitory computer readable medium” or an “article of manufacture” (Claim 19).
Step 2A Prong One: Independent Claims 1, 10 and 19 recites limitations that set forth the abstract idea(s), namely (see in bold except where strikethrough):
“” (see Independent Claim 1);
“:” (see Independent Claim 1);
“” (see Independent Claim 19);
“creating, an evaluation service by at least configuring the evaluation service to evaluate one or more entities in an enterprise , the configuring comprising selecting a first template and a second template” (see Independent Claims 1, 10 and 19);
“in response to create of the evaluation service, the operations further comprise:” (see Independent Claims 1, 10 and 19);
“causing one or more messages to be sent to one or more evaluators” (see Independent Claims 1, 10 and 19);
“receiving one or more responses to the one or more messages” (see Independent Claims 1, 10 and 19);
“determining one or more first scores based on the one or more responses” (see Independent Claims 1, 10 and 19);
“obtaining one or more second scores , the one or more second scores comprising one or more quantitative key indicators associated with the one or more entities” (see Independent Claims 1, 10 and 19);
“inputting the one or more first scores and the one or more second scores to that is configured to output a total score for an entity of the one or more entities, trained to generate the total score for the entity by applying one or more weights to the one or more first scores and the one or more second scores, being trained using training data comprising a) questionnaires for evaluation of the one or more entities and b) responses to the questionnaires” (see Independent Claims 1, 10 and 19);
“ trained using training data comprising scores for a particular entity considered to be accurate given a set of input data and scores for the particular entity considered to be inaccurate given the set of input data” (see Independent Claims 1, 10 and 19);
“in response to the total score for the entity being output populating with the one or more first scores, the one or more second scores, and the total score for the entity, generated at least in part based on the second template” (see Independent Claims 1, 10 and 19);
“publishing to a content accessible by users in the enterprise such that any of the users can access for consumption, wherein provides an evaluation of the entity of the one or more entities” (see Independent Claims 1, 10 and 19).
Here, for Independent Claims 1, 10 and 19, these steps recite an abstract idea directed to a methodical, automated evaluation and scoring of business entities (or ERP systems) by collecting, aggregating, and analyzing both subjective survey data (questionnaires) and objective quantitative key performance indicators (KPIs) to generate a consolidated score. The steps of “receiving responses”, “determining first scores” and “harmonizing” scores are essentially types of evaluations and judgments under “Mental Processes”. People can review questionnaires, apply weighted criteria, combine qualitative and quantitative scores and can determine a final evaluation.
The steps of “causing one or more messages to be sent to one or more evaluators” and “publishing the populated first user interface … for consumption” are methods of managing interactions between people and managing personal behavior within an enterprise. These steps organize how evaluators provide data and how users consume the final product, and thus henceforth are categorized under “Certain Methods of Organizing Human Activities”. The claims concern enterprise evaluations, evaluators, questionnaires, scoring entities, performance assessment and publishing evaluations to organizational users. These resemble business management, personnel/enterprise assessment, commercial interactions and organization decision-making. These steps are analogous to activities of collecting business information, evaluating organizational entities and distributing business analytics.
Thus, data gathering and evaluations result in (Mental Processes), calculating weights and total scores result in (Mathematical Concepts) and managing evaluator communications and enterprise data access result in (Certain Methods of Organizing Human Activities).
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, which 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 “Certain Methods of Organizing Human Activities” which pertains to (3) 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 (4) mathematical calculations.
That is, other than reciting (e.g., “at least one processor” & “at least one memory” & “a first user interface” & “content library” & “enterprise resource planning (ERP) system” & “a communication network” & “a database” & “program code” & “an evaluation harmonizer”, etc…), nothing in the claim elements precludes the steps from being performed 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 and additionally or alternatively as “Certain Methods of Organizing Human Activities” which pertains to (3) 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 (4) mathematical calculations.
Therefore, at step 2a prong 1, Yes, Claims 1-6, 8, 10-15, 17 and 19 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 Claims 1, 10 and 19 recites additional elements directed to: (e.g., “at least one processor” & “a communication network” & “at least one memory” & “a database” & “program code”). These additional elements have been considered 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). The “evaluation harmonizer” is described in terms of a business or administrative problem (organizing and scoring evaluations) rather than a technical one.
Independent Claims 1, 10 and 19: With respect to reliance on (e.g., “evaluation harmonizer” & “generative adversarial network” & “machine learning (ML) model” & “content library” & “enterprise resource planning (ERP) system” & “a first user interface”) as additional elements shown in Independent Claims 1, 10 and 19 when considered individually and as an ordered combination (as a whole) in view of these claim limitations, this additional element does not provide limitations that are indicative of integration into a practical application under step 2a prong 2 due to the following: (1) recites mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions by providing the results to the user on a computer (see MPEP § 2106.05 (f)) or (2) limiting a particular field of use or technological environment pertaining to creating an evaluation service to evaluate one or more entities (e.g., in this case suppliers) by selecting a first scorecard template and a second scorecard template and publishing the results on a user interface showing an aggregated score of evaluating suppliers using a computer in a business operations enterprise environment (see MPEP § 2106.05(h)). While Independent Claims 1, 10 and 19 use a generative adversarial network (GAN), the claims do not recite how the GAN architecture is modified or improved. It simply uses the GAN to “apply weights” to scores, which is a use of a ML model for data processing.
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 1-6, 8, 10-15, 17 and 19 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 Claims 1, 10 and 19 recites additional elements directed to: (e.g., “at least one processor” & “a communication network” & “at least one memory” & “a database” & “program code”). 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 (e.g., see at least Applicant’s Specification ¶ [0111]: “These various aspects or features can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.”).
Independent Claims 1, 10 and 19: With respect to reliance on (e.g., “evaluation harmonizer” & “generative adversarial network” & “machine learning (ML) model” & “content library” & “enterprise resource planning (ERP) system” & “a first user interface”) as additional elements shown in Independent Claims 1, 10 and 19 when considered individually and as an ordered combination (as a whole) in view of these claim limitations, these additional elements do not amount to significantly more than the judicial exceptions under step 2B due to the following: (1) recites mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions by providing the results to the user on a computer (see MPEP § 2106.05 (f)) or (2) limiting a particular field of use or technological environment pertaining to creating an evaluation service to evaluate one or more entities (e.g., in this case suppliers) by selecting a first scorecard template and a second scorecard template and publishing the results on a user interface showing an aggregated score of evaluating suppliers using a computer in a business operations enterprise environment (see MPEP § 2106.05(h)).
With respect to Independent Claims 1, 10 and 19, certain/particular limitations shown recite (1) “mere data gathering” (e.g., “receiving one or more responses to the one or more messages” (see Independent Claims 1, 10 and 19) & “obtaining one or more second scores from a database, the one or more second scores comprising one or more quantitative key indicators associated with the one or more entities” (see Independent Claims 1, 10 and 19)) and “mere data outputting” (e.g., “causing one or more messages to be sent to one or more evaluators” (see Independent Claims 1, 10 and 19)) wherein which each of these claim limitations reflects mere insignificant extra-solution activities (see MPEP § 2106.05 (g)). Furthermore, these certain/particular claim limitations as demonstrated above for Independent Claims 1, 10 and 19 reflects Well-Understood, Routine and Conventional Activities (WURC) under MPEP § 2106.05 (d) ii: See Receiving or transmitting data over a network, e.g., using the Internet to gather data, 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 elements of “machine learning model” in the claims do not amount to significantly more than the judicial exceptions under step 2B due being expressly recognized as Well-Understood, Routine and Conventional (WURC) in the art. See for example; US PG Pub (US 2021/0019674 A1) hereinafter Crabtree, et. al. Crabtree at ¶ [0117]: “System 2000 may also take into consideration feedback from a plurality of feedback sources 2210a-n, which may include, expert judgement 2210b, generative adversarial networks (GAN's) 2210c.” See also Crabtree at ¶ [0118]: “From this analysis of business impact 2412, a network resilience rating is assigned 2405, representing a weighted and adjusted total of relative exposure the organization has to various types of risks, each of which may be assigned a sub-rating. The network resilience rating 2405 may be a single score for all factors, a combination of scores, or a score for a particular risk or area of concern.” See also Crabtree at ¶ [0127]: “The risk rating engine 3111 then sums all scores and produces a risk rating a profile 3140 to the client comprising the knowledge graph and numerical risk score.” See for example; US PG Pub (US 2022/0366345 A1) hereinafter Jones, et. al. Jones at ¶ [0036]: “It is also contemplated that the analytics engine 126 may include one or more machine learning algorithms that may analyze questionnaire response to determine performance scores associated with an institution, such as a police agency. In the manner, the analytics engine 126 may leverage historical questionnaire response and performance scores to train the machine learning algorithms and better predict performance scores based on questionnaire responses. The analytics engine 126 may also include one or more machine learning algorithms that analyze performance scores to determine correlations between performance scores and any data in the databases 104.” See for example; US PG Pub (US 2023/0186219 A1) – “System and Method for Enterprise Change Management Evaluation”, hereinafter Savage, et. al. Savage at ¶ [0027]: “The present invention is directed to more than merely a computer implementation of a routine or conventional activity previously known in the industry as it provides a specific advancement in the area of electronic record availability, consistency, and analysis by providing improvements in the operation of a computer system that uses machine learning and a weighted average model to implement a change management evaluation.” 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 2-6, 8, 11-15 and 17 recite substantially the same or similar additional elements as addressed above and when considered individually and as an ordered combination (as a whole) with these limitations recite the same abstract idea(s) as shown in Independent Claims 1, 10 and 19 along with further steps/details pertaining to “Mental Processes” such as (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” which pertains to (3) 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 (4) mathematical calculations.
Dependent Claims 3, 5-6, 12 and 14-15 further narrow the abstract ideas, and are therefore still ineligible for the reasons previously provided in Steps 2A Prong 2 and 2B for Independent Claims 1, 10 and 19. Dependent Claims 2, 4, 8, 11, 13, and 17: With respect to reliance on the additional elements of (e.g., “a library” (see Dependent Claims 2 & 11) & “second user interface” (see Dependent Claims 4 & 13) & “machine learning (ML) model” (see Dependent Claims 8 and 17)), these additional elements do not provide limitations that are indicative of integration into a practical application under step 2a prong 2 and also do not recite additional elements that amount to significantly more than the recited judicial exceptions under step 2B due to: (1) limiting a particular field of use or technological environment pertaining to the second scorecard template of the suppliers being selected from a library using a computer in a business operations enterprise environment (see MPEP § 2106.05(h)) or (2) alternatively recites mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions by providing the results to the user on a computer (see MPEP § 2106.05 (f)).
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 1-6, 8, 10-15, 17 and 19 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. Thus, Claims 1-6, 8, 10-15, 17 and 19 are ineligible with respect to the 35 U.S.C. § 101 analysis.
Examining Claims with Respect to Prior Art
7. Applicant’s arguments, see pages 13-14 filed on 05/04/2026, with respect to the 35 U.S.C. § 103 Claim Rejections for Claims 1-6, 10-15 and 19 have been fully considered and are found to be persuasive. Therefore, Claims 1-6, 8, 10-15, 17 and 19 overcome the prior art only. Please note that the following issues still remain: (1) 35 U.S.C. § 101 Claim Rejections for Claims 1-6, 8, 10-15, 17 and 19.
Regarding Independent Claims 1, 10 and 19, 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:
- “inputting the one or more first scores and the one or more second scores to a machine learning model that is configured to output a total score for an entity of the one or more entities, wherein the machine learning model comprises a scoring engine that includes a generative adversarial network trained to generate the total score for the entity by applying one or more weights to the one or more first scores and the one or more second scores, the generative adversarial network being trained using training data comprising a) questionnaires for evaluation of the one or more entities and b) responses to the questionnaires, wherein the generative adversarial network is further trained using training data comprising scores for a particular entity considered to be accurate given a set of input data and scores for the particular entity considered to be inaccurate given the set of input data”.
The closest prior arts are as follows:
#1) US PG Pub (US 2015/0039359 A1) – “Component Based Mobile Architecture with Intelligent Business Services to Build Extensible Supply Chain Ready Procurement Platforms”, hereinafter Katakol, et. al.
#2) US PG Pub (US 2013/0132233 A1) – “Sustainability Based Supplier Evaluation”, hereinafter Rothley, et. al.
#3) US PG Pub (US 2021/0019674 A1) – “Risk Profiling and Rating of Extended Relationships Using Ontological Databases”, hereinafter Crabtree, et. al.
#4) US PG Pub (US 2012/0209890 A1) – “Inter-enterprise Ingredient Specification Compliance”, hereinafter Nowacki, et. al.
#5) US PG Pub (US 2007/0055564 A1) – “System for Facilitating Management and Organizational Development Processes”, hereinafter Fourman.
Regarding the Katakol reference, Katakol system for an evaluation harmonizer teaches the following:
- at least one processor (see at least Katakol: ¶ [0149]. Katakol notes that “for example, it is well known that a “computer processor” is also called a “central processing unit (CPU).”)
- at least one memory including program code which when executed by the at least one processor causes operations (see at least Katakol: ¶ [0149]. Katakol notes that as another example, it is well known that the terms “flash RAM” and “flash memory” are used interchangeably.) comprising:
- creating, by an evaluation harmonizer (see at least Katakol: Fig. 3 & Fig. 6 & ¶ [0088-0090]. Katakol notes automated aggregation, cleansing, and normalizing of data using an Artificial Intelligence based system that learns. Real-time human feedback and ability to change classification.), an evaluation service for an enterprise resource planning (ERP) system (see at least Katakol: ¶ [0015] & ¶ [0070] & ¶ [0129]. Katakol notes that the platform further includes a unified supplier portal for sourcing and procurement tasks and is easy to integrate with ERP systems. Integrates directly with ERP system to ensure real time updates of system. The scoring component can be used to evaluate suppliers during the RFP (Request for Proposal) proposal or can also be used for the strategic evaluation of the existing suppliers for balanced score card purposes.) by at least configuring the evaluation service to evaluate one or more entities in an enterprise (see at least Katakol: See also FIGS. 8, 12, 12-a, and 12-b of Katakol noting the evaluation of one or more suppliers.) associated with the ERP system (see at least Katakol: ¶ [0015] & ¶ [0070] & ¶ [0129].) , the configuring comprising selecting a first template and a second template (see at least Katakol: ¶ [0015] & ¶ [0070] & ¶ [0116]. Katakol notes category specific supplier performance and risk management templates. See also Katakol at ¶ [0070]: The scoring component can be used to evaluate suppliers during the RFP (Request for Proposal) proposal or can also be used for the strategic evaluation of the existing suppliers for balanced score card purposes. See also Katakol at ¶ [0116]: Katakol teaches providing templates and clause library for easy creation. See also FIGS. 8, 12, 12-a, and 12-b of Katakol noting the evaluation of one or more suppliers. Also Examiner notes guided sourcing events with category specific templates (FIG. 15 a, b, c and d).)
- in response to creation of the evaluation service, the operations further comprise (see at least Katakol: ¶ [0070] & Fig. 12, Fig. 12-a and Fig. 12-b. Katakol teaches that the scoring component can be used to evaluate suppliers during the RFP (Request for Proposal) proposal or can also be used for the strategic evaluation of the existing suppliers for balanced score card purposes.)
- causing one or more messages to be sent to one or more evaluators (see at least Katakol: ¶ [0050] & Fig. 12, Fig. 12-a and Fig. 12-b. Katakol notes that survey section (FIG. 1) which provides means to select pre-set questions for which answers may be required to be submitted by a supplier or vendor and which answers may be automatically scored and/or a rating provided. Returning a vendor list in accordance with characteristics selected by a user, or for employing the assigned rating to a supplier's response or responses. Also Examiner notes guided sourcing events with category specific templates (FIG. 15 a, b, c and d).)
- receiving one or more responses to the one or more messages (see at least Katakol: ¶ [0050] & Fig. 12, Fig. 12-a and Fig. 12-b. Katakol notes that the scoring engine for creating ratings related to a supplier's response to a set of questions served by the Survey Section. Also Examiner notes guided sourcing events with category specific templates (FIG. 15 a, b, c and d).)
- determining one or more first scores based on the one or more responses (see at least Katakol: Fig. 12, 12-a, 12-b & ¶ [0050]. Katakol at Survey Section (FIG. 1) which provides means to select pre-set questions for which answers may be required to be submitted by a supplier or vendor and which answers may be automatically scored and/or a rating provided, or for employing the assigned rating to a supplier's response or responses; Auction Engine (FIG. 1) for setting up and conducting auctions; Scoring Engine for creating ratings related to a supplier's response to a set of questions served by the Survey Section. See also Katakol at ¶ [0070]: This concept is also extended to business components such as a scoring process (see FIG. 1 at “Scoring Engine”) related to suppliers or a response from a supplier is identical using scoring component. The scoring component can be used to evaluate suppliers during the RFP (Request for Proposal) proposal or can also be used for the strategic evaluation of the existing suppliers for balanced score card purposes.)
However, neither Katakol, et. al and the other prior art of record do not reach or render obvious the sequence of limitations directed to:
- “inputting the one or more first scores and the one or more second scores to a machine learning model that is configured to output a total score for an entity of the one or more entities, wherein the machine learning model comprises a scoring engine that includes a generative adversarial network trained to generate the total score for the entity by applying one or more weights to the one or more first scores and the one or more second scores, the generative adversarial network being trained using training data comprising a) questionnaires for evaluation of the one or more entities and b) responses to the questionnaires, wherein the generative adversarial network is further trained using training data comprising scores for a particular entity considered to be accurate given a set of input data and scores for the particular entity considered to be inaccurate given the set of input data”.
Regarding the Rothley reference, Rothley system for an evaluation harmonizer teaches the following:
- obtaining one or more second scores from a database (see at least Rothley: Figs. 3-4 & Fig. 5 & ¶ [0035]. Rothley notes scoring each of the one or more suppliers based on the customized green sourcing metrics and providing a ranked list of suppliers according to their respective individual and overall scores. Performing the sourcing analysis includes identifying one or more suppliers whose green scores falls under a pre-defined threshold limit.), the one or more second scores comprising one or more quantitative key indicators associated with the one or more entities (see at least Rothley: Figs. 3-4 & ¶ [0039] & ¶ [0047]. Rothley notes that the product data may be received from the supplier 245 through a questionnaire or be extracted from product factsheets and supplier factsheets stored in databases within the ERP system 200. See also Rothley at ¶ [0017]: The product data for a supplier can be automatically extracted from supplier factsheets, product factsheets, supplier master records, product category factsheets, product master records, supplier invoices, contracts, surveys, questionnaires, integrated ERP systems, web services, and external data feeds. See also Rothley at ¶ [0047]: The product data relating to the chemical compound supplied by supplier C is (if applicable) automatically extracted from a filled-in Questionnaire sent along with an RFx. In addition, other product or supplier related data such as the supplier's location, climatic conditions, governing standards and laws pertaining to supplier's location etc., are received through external data source systems in real-time.).
However, neither Rothley, et. al and the other prior art of record do not reach or render obvious the sequence of limitations directed to:
- “inputting the one or more first scores and the one or more second scores to a machine learning model that is configured to output a total score for an entity of the one or more entities, wherein the machine learning model comprises a scoring engine that includes a generative adversarial network trained to generate the total score for the entity by applying one or more weights to the one or more first scores and the one or more second scores, the generative adversarial network being trained using training data comprising a) questionnaires for evaluation of the one or more entities and b) responses to the questionnaires, wherein the generative adversarial network is further trained using training data comprising scores for a particular entity considered to be accurate given a set of input data and scores for the particular entity considered to be inaccurate given the set of input data”.
Regarding the Crabtree reference, Crabtree system for an evaluation harmonizer teaches the following:
- inputting the one or more first scores and the one or more second scores to a machine learning model that is configured to output a total score for an entity of the one or more entities (see at least Crabtree: ¶ [0054] & ¶ [0118] & ¶ [0127]. Crabtree teaches that ML algorithms assist in determining the impact and severity of the risk by consulting actuarial tables and commercial-off-the-shelf (COTS) modeling tools, and together with the system's semantic computing, assign a summed total of the risk rating. The risk rating scale is customizable but as an example, it may be configured where a negative numerical score means a higher risk, a risk rating of zero is neutral, and a positive numerical rating is of low risk or beneficial relationship to the user. See also Crabtree at ¶ [0118]: From this analysis of business impact 2412, a network resilience rating is assigned 2405, representing a weighted and adjusted total of relative exposure the organization has to various types of risks, each of which may be assigned a sub-rating. The network resilience rating 2405 may be a single score for all factors, a combination of scores, or a score for a particular risk or area of concern. The network resilience rating 2411 may then be adjusted or filtered depending on the context in which it is to be used 2409. See also Crabtree at ¶ [0127]: This information is used by the risk rating engine's 3111 semantic computing and machine learning algorithms to determine the risk impact likelihood to the entity. Machine learning algorithm which identifies, categorizes, and scores each relation with a risk score. The risk rating engine 3111 then sums all scores and produces a risk rating a profile 3140 to the client comprising the knowledge graph and numerical risk score.), wherein the machine learning model (see at least Crabtree: Fig. 19 & ¶ [0103]. Crabtree notes machine learning models 1901 shown at Fig. 19.) comprises a scoring engine (see at least Crabtree: ¶ [0104] & Fig. 24. Crabtree teaches that Fig. 24 denoting an architecture diagram for the scoring engine. The cybersecurity profile is sent to the scoring engine 1910 along with event and loss data 1914 and context data 1909 for the scoring engine 1910 to develop a score and/or rating for the organization that takes into consideration both the cybersecurity profile, context, and other information.) that includes a generative adversarial network (see at least Crabtree: ¶ [0117] & ¶ [0123]. Crabtree teaches generative adversarial networks (GAN’s) in Fig. 22 denoted as 2210c and Fig. 28 denoted as 2812.) trained to generate the total score for the entity (see at least Crabtree: Figs. 32-33 & ¶ [0118] & ¶ [0127].) by applying one or more weights (see at least Crabtree: ¶ [0122] & ¶ [0149] & Fig. 24. Crabtree notes that the edges may also be assigned numerical weights or probabilities, indicating, for example, the likelihood of a successful attack gaining access from one node to another. The next step in the process is to assign a risk category 3303. This is critical as each category 3304 is weighted based on the impact the type of risk would have on the entity. See also Crabtree at ¶ [0093]: Operations may be assigned a score up to 400 points, along with up to 200 additional points for web/application recon results, 100 points for patch frequency, and 50 points each for additional endpoints and open-source intel results. This yields a weighted score incorporating all available information from all scanned sources, allowing a meaningful and readily-appreciable representation of an organization's overall cybersecurity strength. See also Fig. 32 of Crabtree.) to the one or more first scores and the one or more second scores (see at least Crabtree: ¶ [0054] & ¶ [0118] & ¶ [0127].), the generative adversarial network (see at least Crabtree: ¶ [0117] & ¶ [0123]. Crabtree teaches generative adversarial networks (GAN’s) in Fig. 22 denoted as 2210c and Fig. 28 denoted as 2812.) being trained using training data (see at least Crabtree: ¶ [0074]. Crabtree notes that machine learning algorithms develop models of behavior or understanding based on information fed to them as training sets, and can modify those models based on new incoming information.) comprises a) questionnaires for evaluation of the one or more entities (see at least Crabtree: Figs. 32-33 & ¶ [0078] & ¶ [0103]. Crabtree teaches that the directed computational graph module 155 retrieves one or more streams of data from a plurality of sources, which includes, but is in no way not limited to, a plurality of physical sensors, network service providers, web-based questionnaires and surveys, monitoring of electronic infrastructure, crowd sourcing campaigns, and human input device information. See also Crabtree at ¶ [0103]: The cyber-physical graph 1902 plus the analyses of data directed by the directed computational graph on the reconnaissance data received from the reconnaissance engine 1906 are combined to represent the cyber-security profile of the client organization whose network 1907 is being evaluated.) and b) responses to the questionnaires (see at least Crabtree: ¶ [0053] & ¶ [0078] & ¶ [0085]. Crabtree teaches receiving scan responses may be collected and processed through a plurality of data pipelines 155 a to analyze the collected information. See also Crabtree at ¶ [0053]: A knowledge graph is generated which may be presented to the user for advanced insight and analysis into the risk factors and relationships associated with the queried entity, but also is used by the system to answer additional queries through various procedures. Crabtree teaches that the directed computational graph module 155 retrieves one or more streams of data from a plurality of sources, which includes, but is in no way not limited to, a plurality of physical sensors, network service providers, web-based questionnaires and surveys.)
However, neither Crabtree, et. al and the other prior art of record do not reach or render obvious the sequence of limitations directed to:
- “inputting the one or more first scores and the one or more second scores to a machine learning model that is configured to output a total score for an entity of the one or more entities, wherein the machine learning model comprises a scoring engine that includes a generative adversarial network trained to generate the total score for the entity by applying one or more weights to the one or more first scores and the one or more second scores, the generative adversarial network being trained using training data comprising a) questionnaires for evaluation of the one or more entities and b) responses to the questionnaires, wherein the generative adversarial network is further trained using training data comprising scores for a particular entity considered to be accurate given a set of input data and scores for the particular entity considered to be inaccurate given the set of input data”.
Regarding the Nowacki reference, Nowacki system for an evaluation harmonizer teaches the following:
- in response to the total score for the entity being output by the machine learning model (see at least Nowacki: Fig. 9 & ¶ [0079-0080] & ¶ [0139-0141]. Nowacki notes that when the process 900 begins (901), normalized values are obtained (902). The normalized values may be received from a receiver entity, or the receiver entity may provide one or more actual values and one or more expected values, and the one or more normalized values may be generated using the received values. The normalized value may include a combined risk score for all or some ingredients and attributes provided by a particular supplier, or the normalized value may be a risk score for a particular ingredient or attribute provided by the particular supplier. The risk score may be a second derivative risk score, reflecting whether the particular supplier is trending in a good or bad direction. See also Nowacki at ¶ [0079-0080]: “The risk score may also be calculated using an algorithm or a look-up table that accepts the raw or actual deviation amount as an input, and that maps the raw or actual deviation amounts to normalized values. To make the normalized values meaningful for use in a comparison, different algorithms may be used to normalize the values to fit within different ranges.”), populating a first user interface with the one or more first scores, the one or more second scores, and the total score for the entity (see at least Nowacki: Figs. 5-6 & Fig. 8 & Fig. 10.), the first user interface generated at least in part based on the second template (see at least Nowacki: Figs. 5-6 & Figs. 8 & Fig. 10 & ¶ [0130]. Nowacki notes that a user of the user device 804 enters information through a user interface 815, where the information identifies a particular ingredient, supplier entity, and/or attribute, and/or information associated with a credibility or reliability expectation, such as information indicating that the user wishes to determine the extent to which a particular supplier satisfies or does not satisfy credibility or reliability expectations. See also Nowacki at ¶ [0043-0045]: The supplier-specific templates stored on the specification compliance server 101 may specify that, for a particular supplier, a particular value for a particular attribute is shown in a particular region of a certificate of analysis that is provided by that supplier. The templates may also be updated on an other-than-periodic basis, such as after determining that certain attribute values that have been automatically read from a certificate of analysis using a template fall outside the usual, normal, possible, or acceptable range of values that are associated with the supplier, attribute, and/or the ingredient. See also Nowacki at Fig. 8 & Fig. 10.).
However, neither Nowacki, et. al and the other prior art of record do not reach or render obvious the sequence of limitations directed to:
- “inputting the one or more first scores and the one or more second scores to a machine learning model that is configured to output a total score for an entity of the one or more entities, wherein the machine learning model comprises a scoring engine that includes a generative adversarial network trained to generate the total score for the entity by applying one or more weights to the one or more first scores and the one or more second scores, the generative adversarial network being trained using training data comprising a) questionnaires for evaluation of the one or more entities and b) responses to the questionnaires, wherein the generative adversarial network is further trained using training data comprising scores for a particular entity considered to be accurate given a set of input data and scores for the particular entity considered to be inaccurate given the set of input data”.
Regarding the Fourman reference, Fourman system for an evaluation harmonizer teaches the following:
- publishing the populated first user interface to a content library accessible by users (see at least Fourman: ¶ [0082] & ¶ [0218] & Fig. 10. Fourman notes that the KPIs, KSI and KTIs may be saved in a library for re-use within template definitions and/or plan definitions. See also Fourman at ¶ [0218]: Risk Management indicators are saved in a library by different master users and combined together in a Template to support the Hierarchy of Intent shown in FIG. 3 and reflected in FIG. 2. The same approach has been used in the following example of developing a Service Improvement Plan and scorecard for a local government organization. See also Fourman at Fig. 10.) in the enterprise via a communication network (see at least Fourman: ¶ [0168] & Figs. 1A-1B.), such that any of the users can access the populated first user interface for consumption (see at least Fourman: ¶ [0233-0235] & ¶ [0250]. Fourman notes that creators of expertise for re-use 703 access use the User Interface 704 to interact with the system. Communities of Practice 711 use the system, via the portal, as a means to collaborate. Users, or subscribers to the hub then access benchmark organizations information via the Hub shown schematically in with the linkages of three users to a central hub represented 1802. To request access to a further scorecard, the owner of Entity 1 has several options including use of e-mail. While the owner of Entity 1 knew of the existence of Entity 2, they were unaware of the Existance of Entity 3 until the system identified Entity 3 (using the Purposeful Clustering approach described below) as an appropriate organization for benchmarking and learning and displayed its name to the owner of Entity 1.), wherein the populated first user interface provides an evaluation of the entity of the one or more entities (see at least Fourman: (Claim 1 of Fourman) & ¶ [0171] & ¶ [0223-0225]. Fourman teaches that entities for which plans exist are labelled organization units or scorecards 1601. Additional frames within the portal 1602 and 1603 show associated knowledge related to the selected scorecard since no indicator is selected. Other approaches to feedback include Plan-Do-Check-Act, Plan-Do-Study-Act, Plan-Do-Review and Plan-Implement-Evaluate, all of the above being known in the field of management and particularly quality management and continuous improvement. See also Fourman at ¶ [0171]: The PC 102 supports a Graphical User Interface (GUI) capable of displaying a scorecard 210 (FIG. 2) that is a representation of an intention of an entity in a measurable form. See also Claim 1 of Fourman: “A graphical user interface arranged to display, when in use, a scorecard or other representation of information.” See also Fourman at Figs. 9-10.).
However, neither Fourman, et. al and the other prior art of record do not reach or render obvious the sequence of limitations directed to:
- “inputting the one or more first scores and the one or more second scores to a machine learning model that is configured to output a total score for an entity of the one or more entities, wherein the machine learning model comprises a scoring engine that includes a generative adversarial network trained to generate the total score for the entity by applying one or more weights to the one or more first scores and the one or more second scores, the generative adversarial network being trained using training data comprising a) questionnaires for evaluation of the one or more entities and b) responses to the questionnaires, wherein the generative adversarial network is further trained using training data comprising scores for a particular entity considered to be accurate given a set of input data and scores for the particular entity considered to be inaccurate given the set of input data”.
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, Applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached on 571-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-270-8853.
Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
/DERICK J HOLZMACHER/ Patent Examiner, Art Unit 3625A
/BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625
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