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 .
Status of Claims
This action is in reply to the response filed on June 17, 2026.
Claims 1 and 11 were amended.
Claim(s) 1-20 are currently pending and have been examined.
This action is made Final.
Response to Arguments
Applicant argued that Examiner’s 101 rejection was improper because the amended claims parallel Desjardins by specifying how the machine learning model operates and not merely that machine learning is used. Examiner disagrees. Applicant’s claimed invention discloses a series of tasks performed. In relation to the machine learning algorithm, Applicant’s claimed invention also discloses the type of data used to train the machine learning algorithm as well as custom scripts that automatically extract metadata. Unlike Desjardins, Applicant’s claimed invention does not disclose any details related to the functional operation of the machine learning algorithm. Disclosing what the machine learning model does is different from disclosing how it is done. Therefore, Examiner finds Applicant’s argument non-persuasive.
Applicant argued that Examiner’s 101 rejection was improper because the amended claims recited “a particular way to achieve a desired outcome” rather than merely the idea of a result, satisfying the August 4, 2025 memorandum’s framework for distinguishing eligible claims from those that merely “apply” an abstract idea. Examiner disagrees. Discerning whether the claims recited “a particular way to achieve a desired outcome” rather than merely the idea of a result is a consideration when determining patent eligibility. However, Applicant’s amended claims are not deemed patent eligible simply be reciting a particular way to achieve a desired outcome. Other factors must also be considered. Applicant’s claimed invention includes an abstract idea accompanied by additional elements that are merely used as tools to implement the identified abstract idea. The “particular way to achieve a desired outcome” is found in the abstract idea. Without additional elements to integrate the abstract idea into a practical application or provide significantly more than the abstract idea itself, the abstract idea, and its particular way to achieve a desired outcome, remains abstract. Therefore, Examiner finds Applicant’s argument non-persuasive.
Applicant argued that Examiner’s 101 rejection was improper because the clustering-based machine learning training and the weighted-sum scoring formula are not generic computer functions. Applicant asserted they are specific technical mechanisms that improve how the system quantifies participant engagement and identifies qualifying research projects. Examiner disagrees. The weighted-sum scoring formula and the raw activity data clustered by the machine learning model were a part of the abstract idea identified by Examiner during the patent eligibility analysis. They were not identified as generic computer functions. Therefore, Examiner finds Applicant’s argument non-persuasive.
In response to Applicant’s amendments and arguments in favor of patentability over the prior art of record, Examiner finds Applicant’s arguments persuasive and withdraws the rejection of claims 1-20 under 35 USC 103.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1 and 11 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 1 and 11 recite a “machine learning algorithm”, which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. For purposes of Examination, Examiner interpreted the machine learning algorithm as a component of the Business Components Recommendation Engine disclosed in the Specification at paragraph 29.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim(s) 1-20 are directed to a system and a method, which are one of the statutory categories of invention. (Step 1: YES).
The Examiner has identified independent system claim 1 as the claim that represents the claimed invention for analysis and is similar to independent method Claim 11. Claim 1 recites the following limitations:
[an interface that is configured to access a plurality of data sources; a memory component that stores and manages data relating to research and development assessment;] and
[a computer processor coupled to the interface and the memory component, the computer processor further configured to perform the steps of:]
extracting, [via the interface,] raw activity data and metadata corresponding to the raw activity data from the plurality of data sources, wherein the raw activity data comprises project data, human resource data and vendor data;
identifying, from the metadata, one or more relevant participants, an activity level for each relevant participant, and an estimated investment time for each participant by performing a statistical analysis of the metadata, the metadata comprising one or more of lines of code and a determination of activities performed, wherein the statistical analysis comprises analyzing commit frequency, file modification counts, and code contribution patterns from version control repositories to quantify individual participant engagement levels, wherein quantifying the individual participant engagement levels comprises computing a weighted sum of the commit frequency, the file modification counts, and the code contribution patterns combined with scores derived from job title and length of employment data;
transforming, [via the computer processor,] the raw activity data to generate an activity nexus matrix and a subject matter expert identification component based on the analyzed metadata, the transforming comprising one or more custom scripts to join and normalize data and to provide a visualization of relative activity of each of the one or more relevant participants by project, a person-to-project nexus for each of the one or more relevant participants, and an activity level by each of the one or more relevant participants;
identifying, [via a recommendation engine implementing machine learning], at least one qualifying research and development project from the extracted raw activity data and metadata;
outputting the activity nexus matrix, the subject matter expert identification component, and the identified at least one qualifying research and development project in a standardized output format;
based on the standardized output format, generating, [via a recommendation engine], (i) a list of the at least one qualifying research project, (ii) one or more interview preparation packages comprising summarized documentation [created by the machine learning algorithm] based on the extracted raw activity data, the one or more interview preparation packages prioritized based on an intensity and a nature of activity on the identified at least one qualifying research project, (iii) a file of sample contemporaneous documentation for the at least one qualifying research project, and (iv) pre-qualified time survey data for the at least one qualifying research project;
based on the one or more interview preparation packages and pre-qualified time survey data, selectively initiating a validation session with one or more subject matter experts; and
generating a credit calculation with contemporaneous technical documentation supporting a research and development credit for the project;
wherein [the machine learning algorithm] is trained on a dataset comprising prior audit defense data to identify qualifying research projects based on pattern recognition of technical activities, [the machine learning algorithm] being configured to cluster the extracted raw activity data into business component groupings based on similarity to activity data and business components that were upheld during the prior audit defense, and
wherein the custom scripts automatically extract metadata comprising lines of code, commit history, and issue tracking data to establish a quantitative nexus between participants and projects.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity because the limitations recite a commercial or legal interaction. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a commercial or legal interaction, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. The interface, computer processor, recommendation engine, machine learning algorithm, and memory in Claim 1 are just applying generic computer components to the recited abstract limitations. The recitation of generic computer components in a claim does not necessarily preclude that claim from reciting an abstract idea. Claim(s) 11 is also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims recite an abstract idea)
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of an interface, computer processor, machine learning algorithm, recommendation engine, and memory. The computer hardware/software is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, claim(s) 1 and 11 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, these additional elements do not change the outcome of the analysis when considered separately and as an ordered combination. Thus, claim(s) 1 and 11 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 5 and 15 include the additional limitation of a business components recommendation engine. However, the additional limitation is not sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, it does not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, the additional element does not change the outcome of the analysis when considered separately and as an ordered combination. Thus, claim(s) 5 and 15 are not patent eligible.
Dependent claim 7 includes the additional limitation of a recommendation engine. However, the additional limitation is not sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, it does not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, the additional element does not change the outcome of the analysis when considered separately and as an ordered combination. Thus, claim(s) 7 is not patent eligible.
Dependent claim 12 includes the additional limitations of project management systems, versioning control software repositories, employee rosters and payroll systems. However, the additional limitations are not sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, the additional elements do not change the outcome of the analysis when considered separately and as an ordered combination. Thus, claim(s) 12 is not patent eligible.
Dependent claims 2-4, 6, 8-10 and 13, 14, and 16-20 further define the abstract idea that is present in their respective independent claim(s) 1 and 11 and thus correspond to certain methods of organizing human activity and hence are abstract for the reasons presented above. Dependent claims 2-4, 6, 8-10 and 13, 14, and 16-20 do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea. Thus, claim(s) 1-20 are not patent-eligible.
Examiner’s Statement of Reason for Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter. In light of Applicant's remarks, Examiner agrees that the cited reference(s) of Hahn (US 8,544,726), Dankowych (CA 2491381), and Humphrey (US 2007/0156564) do not disclose, teach, or suggest the claimed invention. Hahn teaches a method and system for providing an automated and integrated R&D tax credit tool. Dankowych teaches a computer-based system and method for gathering and processing scientific project data. Humphrey teaches a tax reporting system and method. However, the prior art of record fails to anticipate or render obvious the claimed invention. Specifically, the prior art of record fails to anticipate or render obvious limitations of “identifying, from the metadata, one or more relevant participants, an activity level for each relevant participant, and an estimated investment time for each participant by performing a statistical analysis of the metadata, the metadata comprising one or more of lines of code and a determination of activities performed, wherein the statistical analysis comprises analyzing commit frequency, file modification counts, and code contribution patterns from version control repositories to quantify individual participant engagement levels; transforming, via the computer processor, the raw activity data to generate an activity nexus matrix and a subject matter expert identification component based on the analyzed metadata, the transforming comprising one or more custom scripts to join and normalize data and to provide a visualization of a relative activity of each of the one or more relevant participants by project, a person-to-project nexus for each of the one or more relevant participants, and an activity level by each of the one or more relevant participants,” as described by the allowed claims.
Conclusion
Pertinent Art
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure. Delapass et al (US 2003/0101114) discloses a system and method for collecting and analyzing tax reporting surveys.
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 of 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 extension fee 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 JOHN O PRESTON whose telephone number is (571)270-3918. The examiner can normally be reached 12:00 pm - 8:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael W Anderson can be reached on 571-270-0508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN O PRESTON/Examiner, Art Unit 3693
August 19, 2026
/BRUCE I EBERSMAN/Primary Examiner, Art Unit 3693
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