DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
The following is a Final Office Action for Application Serial Number: 19,221,191, filed on March 28, 2025. In response to Examiner’s Non-Final Rejection dated August 04, 2025, Applicant on October 23, 2025, amended claims 1, 2, 6, 8, 15 and 20, canceled claims 9 and 10 and added new claims 21 and 22. Claims 1-8 and 11-22 are pending in this application and have been rejected below.
Response to Amendment
Applicant's amendments are acknowledged.
Regarding the 35 U.S.C. 101 rejection, Applicants arguments and amendments have been considered but are insufficient to overcome the rejection.
Response to Arguments
Applicant's Arguments/Remarks filed October 23, 2025 (hereinafter Applicant Remarks) have been fully considered but are not persuasive. Applicant’s Remarks will be addressed herein below in the order in which they appear in the response filed October 23, 2025.
Regarding the 35 U.S.C. 101 rejection, Applicant submits that the claims are not directed to an abstract idea without significantly more. Nor are the alleged additional elements recited at a high level of generality to apply the exception. Specifically, the proposed amendments emphasize a technological improvement within the technical field of document feature comparison. For example, the technological improvement evaluates heterogeneous assessment texts by automatically detecting and removing disparities between assessment texts (i.e., by adding/removing/modifying questions in the assessment text).
In response, Examiner respectfully disagrees. Examiner finds Applicants arguments merely confines the use of the abstract idea to a particular technological environment and fails to add an inventive concept to the claims; MPEP 2106.05(h). Examiner finds Applicant’s arguments are directed to improvements to an existing business process (e.g. document management). Examiner maintains the amended claims recite mental processes that can be performed mentally by a combination of the human mind and a human using pen and paper, such as observations, evaluations, judgements and/or opinion, without providing meaningful limitations that transform the abstract idea into a patent eligible application of the abstract idea.
Regarding the 35 U.S.C. 101 rejection, Applicant submits in accordance with Examiner's suggestion to "tie in" the operations of all three ML models (i.e., reciting the first, second, and third ML model as a single ML model), and describe the improvement of the ML model, claim 1 as amended. Applicant cites ML limitations of claim 1, (see p. 12, Applicant Remarks).
As indicated in the originally-filed Specification at para. [0002]-[0005], conventional assessment systems face significant technical challenges in comparing and analyzing disparate assessment data, leading to fragmentation and inefficiencies in supply chain transparency. The disparity between assessments has hindered comparability and constrained performance improvement. Traditional approaches like consolidation, reduction, and conversion have proven inadequate as they either remove nuance, lack resolution, or lose data during conversion. As opposed to conventional systems, the claims, as amended, seek to improve the current technology of document feature comparisons by using a single machine learning model to adjust documents (e.g., assessments, assessment responses). This technical solution directly addresses the problems identified in the specification regarding fragmented and incomparable assessment data.
In response, Examiner respectfully disagrees. Regardless of complexity and/or granularity, using a machine learning model to adjust documents, mimics human thought processes that can be performed mentally by a combination of the human mind and a human using pen and paper, such as observations, evaluations, judgements and/or opinion. The functioning of the technical element (i.e., machine learning model) is not being improved, instead the improvements are directed towards addressing problems regarding fragmented and incomparable assessment data, which are improvements to an existing business process (e.g., document management) and not a technology, technological field or computer-related technology. Examiner maintains the machine learning model element is an instructional limitations performed by known machine learning technologies and thus solely used as a tool to perform the instructions of the abstract idea. Examiner maintains the claims are directed to an abstract idea.
Regarding the 35 U.S.C. 101 rejection, Applicant states the Office Action asserts that claim 1 allegedly recites "mental processes" that can be "performed by a combination of the human mind and a human using pen and paper." (See Office Action, p. 3). Applicant disagrees. Rather, the claim as amended adds meaningful limitations, not performable by a human mind or a human using pen and paper, that result in a technical improvement. The technical improvement is accomplished by using a single ML model that maps document(s) to the set of impact categories and generates a set of scores that include a score across a set of metrics for each mapped impact category. The ML model can compare the scores with other scores to determine how the document(s) can be improved, for example, by comparing the scores with other scores to determine a question to add to the document(s). Mapping different documents to the same impact categories enables the ML model to compare documents and reduces disparity between documents. For example, the Specification as filed describes a machine learning model that may be used to "determine whether a question applies to a category", "determine a score", "detect clauses, negations, options, or jargon in a question," and so forth. (Specification, paragraphs [0031], [0043], [0051].) This technology enables usage of documents in silos that, while relevant, are functionally inaccessible for scaled data integrations.
In response, Examiner respectfully disagrees. Examiner finds, mapping documents to a set of impact categories, generating a set of scores that include a score across a set of metrics for each mapped impact category, comparing the scores with other scores to determine how the documents can be improved, and mapping different documents to the same impact categories to reduces disparity between documents are all actions people can go through in their minds or by mathematical calculations using pen and paper. Examiner maintains the machine learning model is solely used a tool to perform the instructions of the abstract idea and does not take the claims out of the mental processes grouping.
Regarding the 35 U.S.C. 101 rejection, Applicant states one of skill in the art will appreciate that, unlike conventional document feature comparison systems that merely analyze and output data (such as claim 2 of Example 47 in the July 2024 Subject Matter Eligibility Examples) or implement abstract ideas on generic computers, the technology described in claim 1 orchestrates specific operations via a single ML model to integrate the detection and remediation of assessment disparities into concrete technological improvements. Claims 8 and 15 are amended to recite similar features as in claim 1. Thus, Applicant requests withdrawal of this rejection of claims 1, 8, 15, and their dependent claims.
Additionally, Applicant states in accordance with Examiner's suggestion during the Interview to describe a refinement of the model (i.e., how the model adjusts documents based on improvements to the model), claim 1 as amended (see p. 14, Applicant Remarks).
Even assuming arguendo that the recitations of proposed amended claim 1 relate to an abstract idea, these recitations integrate a practical application because they recite, with particularity, refinement of the machine learning model and using the refined machine learning model to evaluate (e.g., generate scores for) documents differently. As disclosed in the Specification, "[t]he assessment system may use labeled training data to train a machine learning model to determine a score for one or more metrics ...One or metrics described above may be further refined and enhanced with empirical data, allowing the assessment system 102 to be dynamically refined." (Specification, [0051]-[0053].) The additional elements add meaningful limitations that integrate the alleged judicial exception into a practical application since the disclosed technology: 1) receives modifications (e.g., feedback) from the computing system, 2) refines the machine learning model using the modification, and 3) executes the refined machine learning model (i.e., thereby creating a feedback loop),which cannot be practically performed in the human mind. Thus, Applicant requests withdrawal of this rejection of claim 1 and their dependent claims.
In response Examiner respectfully disagrees. Examiner finds the machine learning model limitations of the pending claims are similar to the ineligible subject matter disclosed in claim 2 of Example 47 disclosed in the AI-related SME examples issued in 2024. Specifically, the Step 2A- Prong Two and Step 2B analysis of claim 2 of Example 47 states, in part, all uses of the recited judicial exceptions require data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05. The recitation of “using a trained ANN” in limitations (d) and (e) also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a trained ANN” limits the identified judicial exceptions “detecting one or more anomalies in a data set using the trained ANN” and “analyzing the one or more detected anomalies using the trained ANN to generate anomaly data,” this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Unlike claim 3 of Example 47 that provides for improved network security using the information from the detection to enhance security by taking proactive measures to remediate danger by detecting, dropping and blocking the source address associated with potentially malicious packets. Examiner finds there are no similar technological improvements here. The machine learning model does not recite an improvement to the functioning of a machine learning technology, computer-related technology or any technological field, thus failing to add an inventive concept to the claims.
Regarding the 35 U.S.C. 101 rejection, Applicant states, in accordance with Examiner's suggestion during the Interview to describe the structural similarities between the claims as amended and the allowed claims of U.S. Pat. No. 12,190,265 (see p. 15-16, Applicant Remarks).
While the claims as amended herein are independently patentable for their own distinct features, the claims of U.S. Pat. No. 12,190,265 allowed by the Examiner recite features directed to "executing a machine learning algorithm" that "reiteratively adjust[s] [] forecast data" to be used in updating worker schedules. Likewise, the claims as amended herein are directed to "executing a [] machine learning model" that "reiteratively adjust[s] a set of gaps for [a] set of documents" (e.g., assessment texts, responses to the assessment texts) to be used in generating corrective actions for the documents. Both the claims as amended herein and the issued claims of U.S. Pat. No. 12,190,265 recite similar claim structures (while independently patentable), such as (a) a computer system with processors and database storage, (b) iterative machine learning model execution, (c) reiterative adjustment of particular parameters (e.g., forecast data vs. gap data), (d) generation of corrective measures (e.g., correction factors vs. corrective actions), (e) application of those measures (e.g., worker schedules vs. document remediation and reports), and so forth. In fact, the claims as amended herein recite additional technical features such as (a) executing the corrective actions, (b) comparing the corrected documents with the initial documents, (c) refining the machine learning model, and so forth.
In response, Examiner respectfully disagrees. Examiner finds Applicants arguments are not persuasive. The claims in US Patent No. 12,190,265 overcame the § 101 by integrating the abstract idea into a practical application and the particulars of the application are factually different from the present claim language. For example, the correction factors in US Patent No. 12,190,265 are not similar to correction actions of the pending claims. The correction factors is an adjustment to the data analysis where the machine learning algorithm generates a correction factor to evaluate the accuracy of forecast data and then generate updated forecast data by applying the generated correction factor, while the corrective actions recited in the pending claims is a process for fixing a problem by the machine learning algorithm generating a corrective action to remove disparities between documents and assessment text and evaluate a degree of improvement of a second set of documents. Examiner maintains the claims recite addition elements used as tools to perform the instructions of the abstract idea without disclosing limitations that integrates the abstract idea into a practical application, nor do these elements provide meaningful limitations that transforms the judicial exception into significantly more than the abstract idea itself. Additionally, Examiner notes the general use of a machine learning model does not provide a meaningful limitation to transform the abstract idea into a practical application and therefore, the machine learning models disclosed in the claims are also solely used as a tool to perform the instructions of the abstract idea. Examiner finds the claims currently do not disclose any unconventional computer functions that can be considered significantly more than the judicial exception. Applicant has not made any persuasive argument that would alter this analysis. For at least these reasons the claims remain rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Step 1: The claimed subject matter falls within the four statutory categories of patentable subject matter.
Claims 1-7 are directed towards a method, claims 8, 11-14, 21 and 22 are directed towards a non-transitory computer-readable medium and claims 15-20 are directed towards a system, which are among the statutory categories of invention.
Step 2A – Prong One: The claims recite an abstract idea.
Claims 1-8 and 11-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite document analysis and generation of corrective actions to remove disparities between documents and assessment text.
Claim 1 recites limitations directed to an abstract idea based on mental processes. Specifically, iteratively evaluates a first set of documents across a plurality of impact topics to generate a set of scores across a set of metrics, wherein the set of scores represent a set of disparities between the first set of documents and an assessment text, wherein the information including, for each assessment text, a plurality of questions; generate and reiteratively adjust a set of gaps for the first set of documents, using the generated set of scores, wherein the set of gaps is iteratively updated based on updates to the set of scores; generate a set of corrective actions to remove one or more disparities of the set of disparities between the first set of documents and the assessment text; executing the generated set of corrective actions to the set of scores to cause generation of a second set of documents for comparison with the first set of documents to evaluate a degree of improvement of the second set of documents; and generating a report indicative of the degree of improvement of the second set of documents constitutes methods based on observations, evaluations, judgements and/or opinion that can be performed mentally by a combination of the human mind and a human using pen and paper. The recitation of a machine learning model and a computer system having at least one processor in communication with a database storing an assessment module does not take the claim out of the mental processes grouping. Thus the claim recites an abstract idea.
Claim 8 recites limitations directed to an abstract idea based on mental processes. Specifically, evaluates a first set of documents across a plurality of impact topics to generate a set of scores representing a set of disparities between the first set of documents and an assessment text; generate a set of gaps for the first set of documents, using the generated set of scores, wherein the set of gaps is updated based on updates to the set of scores; generate a set of corrective actions to remove one or more disparities of the set of disparities between the first set of documents and the assessment text: executing the generated set of corrective actions to the set of scores to cause generation of a second set of documents for comparison with the first set of documents to evaluate a degree of improvement of the second set of documents; and generating, a report indicative of the degree of improvement of the second set of documents constitutes methods based on observations, evaluations, judgements and/or opinion that can be performed mentally by a combination of the human mind and a human using pen and paper. The recitation of machine learning model and a non-transitory, computer-readable medium comprising instructions executable by one or more processors and an assessment module does not take the claim out of the mental processes grouping. Thus the claim recites an abstract idea.
Claim 15 recites limitations directed to an abstract idea based on mental processes. Specifically, evaluates a first set of documents across a plurality of impact topics to generate a set of scores representing a set of disparities between the first set of documents and an assessment text, generate a set of gaps for the first set of documents, using the generated set of scores, wherein the set of gaps is updated based on updates to the set of scores; generate a set of corrective actions to remove one or more disparities of the set of disparities between the first set of documents and the assessment text; executing the generated set of corrective actions to the set of scores to cause generation of a second set of documents for comparison with the first set of documents to evaluate a degree of improvement of the second set of documents; and generating a report indicative of the degree of improvement of the second set of documents constitutes methods based on observations, evaluations, judgements and/or opinion that can be performed mentally by a combination of the human mind and a human using pen and paper. The recitation of artificial intelligence model and a computer system having at least one processor programmed with executable computer program instructions and in communication with a database storing an assessment module does not take the claim out of the mental processes grouping. Thus the claim recites an abstract idea.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application.
Claim 1 recites a database storing an assessment module, wherein the database comprises information associated with a plurality of assessment texts; and receiving, via the computer system, an updated training dataset that comprises one or more modifications to the set of metrics, which are limitations considered to be an insignificant extra-solution activity of collecting and delivering data; see MPEP 2106.05(g). Additionally, claim 1 recites computer system having at least one processor in communication with a database storing an assessment module at a high-level of generality such that they amount to no more than generic computer components used as tools to apply the instructions of the abstract idea; see MPEP 2106.05(f). Claim 1 also recites executing a machine learning model, and in response to receiving the undated training dataset, refining the machine learning model by applying tie one or more modifications to the set of metrics to generate a modified set of metrics: and executing the refined machine learning model, by the computer system, to iteratively evaluates a third set of documents across the plurality of impact topics to generate a second set of scores across the modified sot of metrics. The general use of machine learning does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, the machine learning models disclosed in the claim are solely used as tools to perform the instructions of the abstract idea. Thus, the additional elements do not integrate the abstract idea into practical application because it does not impose any meaningful limitations on practicing the abstract idea. Claim 1 is directed to an abstract idea. The non-transitory computer-readable medium comprising instructions executable by a processor recited in claim 8 and system comprising processors programmed with executable computer program instructions in claim 15 also amount to no more than mere instructions to apply the exception using a generic computer component; see MPEP 2106.05(f). The artificial intelligence models disclosed in claim 15 are also solely used as tools to perform the instructions of the abstract idea. Thus, the additional elements recited in claims 8 and 15 do not integrate the abstract idea into practical application for similar reasons as claim 1.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements in the claims other than the abstract idea per se, including computer system having at least one processor programmed with executable computer program instructions and in communication with a database storing an assessment module amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II). The machine learning and artificial intelligence models recited in the claims are disclosed at a high-level of generality (see at least Specification [0081]) and does not amount to significantly more than the abstract idea. Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, since there are no limitations in the claim that transform the abstract idea into a patent eligible application such that the claim amounts to significantly more than the abstract idea itself, the claims are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
§ 101 Analysis of the dependent claims.
Regarding the dependent claims, dependent claims 11, 14 and 16-18 recite obtaining, displaying presenting and transmitting limitations, respectively, which are considered an insignificant extra-solution activities of collecting and delivering data; see MPEP 2106.05(g). Claims 14, 16 and 17 recite a user interface, which amount to no more than a generic computer component used as a tool to apply the instructions of the abstract idea; MPEP 2106.05(f). Claims 2, 6, 21 and 22 recite machine learning model limitations and claim 20 recite artificial intelligence models. The general use of artificial intelligence techniques do not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, the machine learning and artificial intelligence models disclosed in the claims are solely used as tools to perform the instructions of the abstract idea. Additionally, claims 2-7, 11-13 and 19 recite steps that further narrow the abstract idea constituting mental processes. Therefore claims 2-7, 11-14 and 16-22 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gardener et al. (US 12008332 B1) – A method of generating summaries of content items using one or more large language models (LLMs) is disclosed. A first content item is identified. The first content item includes a set of sub-content items. A level of abstraction is determined for the content item. A prompt is automatically engineered for providing to the one or more LLMs. The prompt includes a reference to the first content item and the level of the abstraction for the first content item. A response to the prompt is received from the LLM. The response includes a second content item. The second content item includes a representation of the first content item that is generated by the LLM. The representation omits or simplifies one or more of the set of sub-content items based on the level of abstraction. The representation is used to control an output that is communicated to a target device.
Hailpern et al. (US 20140304264 A1) – A content platform for providing a mobile, web-based contextual alignment view of a corpus of documents is disclosed. A corpus of documents is mined to identify a set of topics. Each document in the corpus is analyzed to determine a set of opinions associated with the set of topics, the set of opinions including a corpus opinion. Each document in the corpus is classified based on alignment with the corpus opinion. The corpus of documents is presented to the user according to the document classification.
THIS ACTION IS MADE FINAL. 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 Crystol Stewart whose telephone number is (571)272-1691. The examiner can normally be reached 9:00am-5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patty Munson can be reached at (571)270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CRYSTOL STEWART/Primary Examiner, Art Unit 3624
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