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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Status of the Claims
The pending claims in the present application are claims 1-20 of the Amendment dated 19 December 2025.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations include: the recited “data acquisition engine” for performing the “retrieve ... data” limitation, the recited “machine learning engine” for performing the “perform ... a sentiment analysis,” and the “machine learning model tuning engine” for performing “testing cycles” limitations of claim 1; the recited “data acquisition engine” limitation, and the recited ”stream processing engine for continuous data processing” limitation of claim 2; and the recited “API compliance check module” limitation of claim 6. Similar interpretations are also applied to the same claim limitations in claims 8, 9, and 13, and to the same claim limitations in claims 15 and 16.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. The corresponding structure can be found in the specification in para. [0055] (describing “data acquisition engine 202”) and in FIGURE 2, and in the specification in para. [0062] (describing “trained machine learning model 232”) and in FIGURE 2.
If the applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The paragraphs below provide rationales for the rejection. The rationales are based on the multi-step subject matter eligibility test outlined in MPEP 2106.
Step 1 of the eligibility analysis involves determining whether a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 USC 101. (See MPEP 2106.03(I).) That is, Step 1 asks whether a claim is to a process, machine, manufacture, or composition of matter. (See MPEP 2106.03(II).) Referring to the pending claims, the “system” of claims 1-7 constitutes a machine under 35 USC 101, the “computer program product” of claim 8-14 constitutes a manufacture under the statute, and the “method” of claims 15-20 constitutes a process under the statute. Accordingly, claims 1-20 meet the criteria of Step 1 of the eligibility analysis. The claims, however, fail to meet the criteria of subsequent steps of the eligibility analysis, as explained in the paragraphs below.
The next step of the eligibility analysis, Step 2A, involves determining whether a claim is directed to a judicial exception. (See MPEP 2106.04(II).) This step asks whether a claim is directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea. (See id.) Step 2A is a two-prong inquiry. (See MPEP 2106.04(II)(A).) Prong One and Prong Two are addressed below.
In the context of Step 2A of the eligibility analysis, Prong One asks whether a claim recites an abstract idea, law of nature, or natural phenomenon. (See MPEP 2106.04(II)(A)(1).) Using pending claim 1 as an example, the claim recites the following abstract idea limitations:
“... advanced algorithmic compliance integration in API frameworks ... comprising: ...” - See below regarding MPEP 2106.04(a), mathematical concepts, certain methods of organizing human activity, and mental processes
“... perform the steps of: retrieve ... data from multiple sources, wherein the data comprises a subset of regulatory text data ..., a subset of public sentiment data, and a third subset of data obtained from ... continuously monitor one or more news sources; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... standardize and preprocess the retrieved data, generating structured analysis data; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... perform ... the sentiment analysis on the subset of public sentiment data; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... based on the sentiment analysis, calculate the sentiment alignment score, wherein the sentiment alignment score comprises a percentage value representing alignment between the subset of regulatory test data and the subset of public sentiment data; ...” - See below regarding MPEP 2106.04(a), mathematical concepts, certain methods of organizing human activity, and mental processes
“... retrieve ... updated regulatory text data and historical regulatory text data; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... analyze ... the updated regulatory text data and the historical regulatory text data and determine a change to a requirement; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... generate a recommendation for an API data handling practice based on both the sentiment alignment score and the change to the requirement, ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... identify an API action occurring within the API frameworks; and ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... based on an output ..., validate that the API action is compliant.” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
The above-listed limitations of claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, fall under enumerated groupings of abstract ideas outlined in MPEP 2106.04(a). For example, limitations of the claim can be characterized as: mathematical relationships and mathematical equations, associated with, among other things, use of algorithms and calculating of scores, which fall under the mathematical concepts grouping of abstract ideas (see MPEP 2106.04(a)). Limitations of the claim also can be characterized as: legal interactions, in the form of legal obligations associated with adapting to legal or regulatory changes; and managing personal behavior, associated with, among other things, determining compliance of IT architecture based on various criteria, which fall under the certain methods of organizing human activity grouping of abstract ideas (see MPEP 2106.04(a)). Limitations of the claim also can be characterized as: concepts performed in the human mind, including observation (e.g., the recited “retrieve” limitations), and evaluation, judgment, and/or opinion (e.g., the recited “standardize,” “perform,” “calculate,” “analyze,” “generate,” “identify,” and “validate” limitations), which fall under the mental processes grouping of abstract ideas (see MPEP 2106.04(a)). Accordingly, for at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong One of the eligibility analysis.
In the context of Step 2A of the eligibility analysis, Prong Two asks if the claim recites additional elements that integrate the judicial exception into a practical application. (See MPEP 2106.04(II)(A)(2).) Continuing to use claim 1 as an example, the claim recites the following additional element limitations:
“A system ...” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
“... a processing device; a non-transitory storage device containing instructions when executed by the processing device, causes the processing device to” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “retrieve” is “via data acquisition engine” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “regulatory text data” is “obtained by a web scraping application” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “data obtained” is from “a news feed aggregator” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
“... train a machine learning engine to perform a sentiment analysis and calculate a sentiment alignment score, wherein training the machine learning engine comprises: executing, using a machine learning model tuning engine, a plurality of testing cycles using the structured analysis data, wherein the machine learning model tuning engine is configured to vary one or more testing parameters for each testing cycle of the plurality of texting cycles; and deploying the training machine learning engine into a production environment; ...” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “perform” is “via the trained machine learning engine” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “retrieve” is “via the data acquisition engine” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “analyze” is “via the trained machine learning engine” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “output” is of the “trained machine learning engine” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The above-listed additional element limitations of claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, are analogous to: accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, mere automation of manual processes, which courts have indicated may not be sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)); a commonplace business method being applied on a general purpose computer, gathering and analyzing information using conventional techniques and displaying the result, selecting a particular generic function for computer hardware to perform from within a range of fundamental or commonplace functions performed by the hardware, which courts have indicated may not be sufficient to show an improvement to technology (see MPEP 2106.05(a)(II)); a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions, and merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions, which do not qualify as a particular machine or use thereof (see MPEP 2106.05(b)(I)); a machine that is merely an object on which the method operates, which does not integrate the exception into a practical application (see MPEP 2106.05(b)(II)); use of a machine that contributes only nominally or insignificantly to the execution of the claimed method, which does not integrate a judicial exception (see MPEP 2106.05(b)(III)); transformation of an intangible concept such as a contractual obligation or mental judgment, which is not likely to provide significantly more (see MPEP 2106.05(c)); use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea, a commonplace business method or mathematical algorithm being applied on a general purpose computer, and requiring the use of software to tailor information and provide it to the user on a generic computer, which courts have found to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process (see MPEP 2106.05(f)); mere data gathering in the form of obtaining information about transactions using the Internet to verify the transactions and consulting and updating an activity log, which courts have found to be insignificant extra-solution activity (see MPEP 2106.05(g)); and specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, and limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, which courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception (see MPEP 2106.05(h)). For at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong Two of the eligibility analysis.
The next step of the eligibility analysis, Step 2B, asks whether a claim recites additional elements that amount to significantly more than the judicial exception. (See MPEP 2106.05(II).) The step involves identifying whether there are any additional elements in the claim beyond the judicial exceptions, and evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept. (See id.) The ineligibility rationales applied at Step 2A, Prong Two, also apply to Step 2B. (See id.) For all of the reasons covered in the analysis performed at Step 2A, Prong Two, independent claim 1 fails to meet the criteria of Step 2B. Further, claim 1 also fails to meet the criteria of Step 2B because at least some of the additional elements are analogous to: receiving or transmitting data over a network, e.g., using the Internet to gather data, performing repetitive calculations, electronic recordkeeping, and storing and retrieving information in memory, which courts have recognized as well-understood, routine, conventional activity, and as insignificant extra-solution activity (see MPEP 2106.05(d)(II)). As a result, claim 1 is rejected under 35 USC 101 as ineligible for patenting.
Regarding pending claims 2-7, the claims depend from claim 1, and expand upon limitations introduced by claim 1. The dependent claims are rejected at least for the same reasons as claim 1. For example, the dependent claims recite abstract idea elements similar to the abstract idea elements of claim 1, that fall under the same abstract idea groupings as the abstract idea elements of claim 1 (e.g., the recited “continuous data processing and a batch data ... for scheduled data transfer” limitation of claim 2, the recited “wherein the standardizing and the preprocessing of data further comprises data normalization, entity extraction, and thematic analysis” limitation of claim 3, the recited “wherein the sentiment analysis and the calculation of sentiment alignment score are performed” limitation of claim 4, the recited “identify a most stringent standard between multiple regions; and generate a recommendation for the ... data handling practice aligning with the most stringent standard” limitation of claim 5, the recited “wherein the recommendation for the ... data handling practice further comprises updating ... and adjusting an ... data handling rule” limitation of claim 6, and the “displaying one or more compliance insights” limitation of claim 7). The dependent claims recite further additional elements that are similar to the additional elements of claim 1, that fail to warrant eligibility for the same reasons as the additional elements of claim 1 (e.g., the recited “system ... wherein the data acquisition engine comprises a stream processing engine ... warehouse” limitations of claim 2, the ”system” limitation of claim 3, the “system ... using Natural Language Processing (NLP) libraries” limitations of claim 4, the “system ..., wherein the system is further configured to: ... API” limitations of claim 5, the “system ... API ... an API compliance check module ... API” limitations of claim 6, and the “system ..., wherein the system is further configured to: transmit the recommendation for the API data handling practice via an interactive dashboard” of claim 7). Accordingly, claims 2-7 also are rejected as ineligible under 35 USC 101.
Regarding pending claims 8-14, while the claims are of different scope relative to claims 1-7, the claims recite limitations similar to the limitations of claims 1-7. As such, the rejection rationales applied to reject claims 1-7 also apply for purposes of rejecting claims 8-14. Claims 8-14 are, therefore, also rejected as ineligible under 35 USC 101.
Regarding pending claims 15-20, while the claims are of different scope relative to claims 1-5 and 7, and to claims 8-12 and 14, the claims recite limitations similar to the limitations of claims 1-5, 7-12, and 14. As such, the rejection rationales applied to reject claims 1-5, 7-12, and 14 also apply for purposes of rejecting claims 15-20. Claims 15-20 are, therefore, also rejected as ineligible under 35 USC 101.
Examiner Remarks
No prior art rejections are being asserted against the pending claims. The closest prior art of record is composed of the references cited in the claim rejections under 35 USC 103 from the Non-Final Office Action of 24 September 2025. The references include AU Pat. App. Pub. No. 2021203292 A1 to Ratna et al. (hereinafter referred to as “Retna”), in view of Uddin, Gias, et al. "Understanding how and why developers seek and analyze API-related opinions." IEEE Transactions on Software Engineering 47.4 (2019): 694-735 (hereinafter referred to as “Uddin”), and U.S. Pat. App. Pub. No. 2018/0285892 A1 to Brooks et al. (hereinafter referred to as “Brooks”).
As asserted in the Non-Final Office Action, Retna, Uddin, and Brooks disclose, teach, or suggest features that read on various elements of claim 1. To briefly summarize, Retna discloses utilizing AI to predict risk and compliance actionable insights, predict remediation incidents, and accelerate a remediation process. More specifically, a server and an intelligence platform utilize ML models to predict compliance. The intelligence platform receives historical data, including laws and regulations. Data is processed, including smart data manipulation and integration based on business rules, to generate processed data. The intelligence platform uses the ML and an associated structured semantic model on the data. The intelligence platform continually receives new regulatory data and existing regulatory data, to which the process is again applied. Actions are performed based on the insights, with actions relating to legal, IT, and cybersecurity risks. Retna does not disclose specifically tailoring any steps to APIs. Uddin discloses processes for understanding how and why developers seek and analyze API-related opinions in instances, for example, where they are selecting APIs. Legal and security issues are considered as part of the selection. Developers seek insights on API selection from forums, where forum posts include sentiments and opinions about APIs in terms of, for example, usability and performance. Uddin modifies the data and related analyses, of Retna, to also be applied on sentiments about APIs, as in Uddin. Brooks discloses sentiment and analytics for predicting future legislation, and more particularly, determining any new law or new legislation pending enactment in a legislative body that relates to a current topic, a law, or legislation having provisions determined to change an operation of an entity. Brooks also discloses determining a probability measure indicative of a relationships between a legislator sentiment momentum score and a public sentiment momentum score for a law or legislation. Brooks also discloses exploring alternatives based on likelihood of passage of legislation. Brooks modifies the analyses regarding laws and regulations, of the combination of Retna and Uddin, to include determining probabilities and scoring, of Brooks. Specific citations supporting the above assertions about Retna, Uddin, and Brooks can be found in the Non-Final Office Action.
While Retna, Uddin, and Brooks disclose, teach, or suggest elements that appear to explicitly read on many of the limitations of the claims, none of the references discloses, teaches, or suggests the specific relationship between those limitations, as claimed in claim 1. For example, Retna fails to disclose, teach, or suggest that training of the AI or ML models, and use thereof, involves the claimed “train a machine learning engine to perform a sentiment analysis and calculate a sentiment alignment score, wherein training the machine learning engine comprises: executing, using a machine learning model tuning engine, a plurality of testing cycles using the structured analysis data, wherein the machine learning model tuning engine is configured to vary one or more testing parameters for each testing cycle of the plurality of texting cycles; and deploying the training machine learning engine into a production environment” limitations. Nor does Retna disclose, teach, or suggest to “generate a recommendation for an API data handling practice based on both the sentiment alignment score and the change in the requirement; identify an API action occurring within the API frameworks; and based on an output of the trained machine learning engine, validate that the identified API action is compliant,” as claimed. While Uddin discloses API-related features, adding the forum posts in Uddin to the processes performed in Retna would not result in the specific “generate,” “identify,” and “validate” steps, as claimed. Brooks does not remedy this particular deficiency of Retna (or Uddin), and was not cited for such a purpose. Claim 1, therefore, distinguishes over the closest prior art of record. Claims 2-7 depend from claim 1, and distinguish over the closest prior art of record for the same reasons. Claims 8-20 recite limitations similar to those recited by claims 1-7, and distinguish over the closest prior art of record for similar reasons.
Response to Arguments
On pp. 8-13 of the Amendment, the applicant requests reconsideration and withdrawal of the claim rejection under 35 USC 101. More specifically, with respect to Step 2A, Prong One of the eligibility analysis, the applicant asserts, that the claims are not directed to a patent-ineligible concept or a judicial exception. (Amendment, p. 10.) According to the applicant, the claims are not directed to a mental process because the claims recite activity that falls outside the enumerated sub-groupings of methods that may be performed mentally. (Amendment, p. 10.) The applicant emphasizes the claimed training and retraining of ML, deploying of the trained ML, web scraping, API actions and framework, and the like. (Amendment, p. 10.)
The examiner disagrees with the assertions above. Most of the claim limitations that allegedly fall outside of the mental processes grouping are additional elements. Such elements are not considered at Step 2A, Prong One. In Step 2A, Prong One, additional elements are put aside (for eventual consideration at Step 2A, Prong Two and Step 2B), and only the remaining abstract idea elements are considered. (MPEP 2106(III) and MPEP 2106.04(II)(A)(1) and (2).) The abstract idea elements of the claims fall under the enumerated groupings. With respect to the mental processes grouping, in particular, the abstract idea elements require nothing more than individuals observing, evaluating, judging, and opining on data. See the 35 USC 101 rejection above for a more detailed explanation.
With respect to Step 2A, Prong Two of the eligibility analysis, the applicant asserts that any judicial exception is integrated into a practical application. (Amendment, p. 11.) The applicant emphasizes the claimed ML, training ML, web scraping, sentiment analysis, API, and validating API aspects of the claims. (Amendment, p. 11.) The applicant also emphasizes the improvements rationale from MPEP 2106.059(a). (Amendment, p. 11.) According to the applicant, the claimed solution is necessarily rooted in computer technology to so it can be implemented dynamically in real time, thereby allowing financial institutions to ensure API compliance. (Amendment, pp. 11 and 12.)
The examiner disagrees with the above assertions. The examiner asserts that the computer technology in the claims amounts to generic, conventional computer technology being applied for purposes of automating a process for mentally or manually determining compliance of an API, or assisting with the mental or manual determination of compliance of an API. This is an ineligible improvement to an abstract idea, not an improvement to the functioning of a computer or to any other technology or technical field (like machine learning). Note the claiming of generic, conventional computer hardware in the claims. Note also the description of generic, conventional machine learning processes (such as training, tuning, deploying, etc.) in the claims (see, e.g., the description of generic, conventional machine learning steps in the attached NPL documents authored by Awan and Kapoor). The claims fail to establish an eligibility-warranting improvement. Other rationales for eligibility also have not been established.
With respect to Step 2B of the eligibility analysis, the applicant asserts that the claims recite significantly more than the abstract idea. (Amendment, p. 12.) The applicant asserts that the claims recite additional elements that are beyond merely an abstract idea and that individually and in combination contribute to an inventive concept. (Amendment, p. 12.) The applicant also re-asserts the improvements rationale of MPEP 2106.05(a), while emphasizing consideration of each of the claims as a whole. (Amendment, pp. 12 and 13.) The applicant also asserts that eligibility is warranted for claiming a non-conventional and non-generic arrangement of known conventional pieces. (Amendment, p. 13.)
The examiner disagrees with the above assertions. When all claim limitations are considered, as a whole, the claimed solution reads like a generic, conventional computer running generic, conventional ML software, to receive input data, analyze the data, and output a report. Nothing about the claimed combination establishes an inventive concept, or an improvement per the meaning in MPEP 2106.05(a).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Such prior art includes the following:
U.S. Pat. App. Pub. No. 2020/0090059 A1 to Kim et al. discloses, “A device may receive input data associated with a legal regulation, and may process the input data to generate a record that includes: the input data in a knowledge representation format and a semantic representation format, data identifying a feature, data identifying an industry classification, or data identifying an entity of interest. The device may process the record, with machine learning models, to determine output data that includes: data indicating that the legal regulation is inconsistent, data indicating that the legal regulation is outdated, data indicating a sentiment for the legal regulation, data indicating a prescriptive nature of the legal regulation, data indicating a complexity of the legal regulation, data indicating a misrepresentation in the legal regulation, data indicating a compliance burden associated with the legal regulation, or data indicating an industry performance impact of the legal regulation. The device may perform actions based on the output data.” (Abstract.)
U.S. Pat. App. Pub. No. 2020/0273046 A1 to Biswas et al. discloses, “An electronic platform to measure a maturity or level of an entity in view of regulatory and business risks relating to regulatory compliance. The methods and systems can collect various data (e.g., regulatory agency reports, regulatory agency warning letters (e.g. FDA warning letters), internal and vendor company audit results, fines and settlement information, country business risks, regulatory agency product recalls, etc.) from various different data sources. The collected information is analyzed using machine learning techniques to determine a risk compliance level or score for one or more of an entity's companies, functions, control types, and locations arising from regulatory audit non-conformances. The risk compliance scores can be used to generate a risk prediction and identify one or more actions to be taken by the entity to improve or increase an associated compliance level.” (Abstract.)
U.S. Pat. App. Pub. No. 2023/0214754 A1 to Eidelman et al. discloses, “A method for identifying stakeholders relative to an issue is disclosed. In one embodiment, the method may include accessing first data associated with a plurality of individuals associated with an organization; generating first nodes representing the plurality of individuals within an issue graph model; accessing second data associated with one or more policies; generating second nodes representing the one or more policies within the issue graph model based on the second data; receiving an indication of a selected agenda issue; generating links within the issue graph model representing relationships between the first nodes and the second nodes; determining importance scores for the first nodes in the issue graph; identifying a node of the plurality of first nodes associated with the at least one selected agenda issue based on the importance scores; and outputting node properties associated with the identified node.” (Abstract.)
Kapoor, Dhruv. “Machine Learning 101 - The 7 Steps of a Machine Learning Process.” Medium, 14 April 2020 (last accessed on 09 March 2026 at https://medium.com/analytics-vidhya/machine-learning-101-the-7-steps-of-a-machine-learning-process-9439f3ef97eb).
Awan, Abid Ali. “The Machine Learning Life Cycle Explained.” datacamp, 03 October 2022 (last accessed on 09 March 2026 at https://www.datacamp.com/blog/machine-learning-lifecycle-explained).
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 THOMAS Y. HO, whose telephone number is (571)270-7918. The examiner can normally be reached Monday through Friday, 9:30 AM to 5:30 PM Eastern.
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, Jerry O'Connor, can be reached at 571-272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/THOMAS YIH HO/Primary Examiner, Art Unit 3624