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 January 21, 2026.
Claims 1 and 11 were amended.
Claim(s) 1-20 are currently pending and have been examined.
This action is made Non-Final.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on January 21, 2026 has been entered.
Response to Arguments
Applicant argued that Examiner’s 101 rejection was improper because the claims do not recite any of the enumerated exceptions. Examiner disagrees. Examiner has identified the specific limitations in the claimed invention that recite an abstract idea and determined that the identified limitations fall within the subcategory of “certain methods of organizing human activity”. In addition to making the determination, Examiner explained why the determination was made. Examiner’s analysis does not amount to a conclusory statement, as Applicant suggested. Applicant seems to suggest that Examiner is required to provide a separate explanation for each individual limitation that constitutes the identified abstract idea. However, such is not the case. The identified abstract idea is made up of several limitations in the claimed invention, and a separate explanation for each individual limitation is not required. Applicant further alleged that Examiner presented the entire claim as the abstract idea, which is not true. Examiner did not include the additional limitations of the interface, computer processor, and memory as part of the abstract idea. The share of the claimed invention that represents the identified abstract idea is not indicative of whether Examiner’s analysis is proper. In the instant case, the bulk of the claimed invention represents the abstract idea because the claimed invention lacks the additional limitations required to integrate the abstract idea into a practical application or provide significantly more than the abstract idea itself. Applicant further argued that the claimed invention is not directed towards a commercial or legal interaction and Examiner failed to explain how or why the claimed invention is directed towards a commercial or legal interaction. Examiner finds that the claimed invention is actually directed towards both a commercial interaction and a legal interaction. Examiner also provided an explanation for the finding. Specifically, the claimed invention is directed towards implementing a research and development tax credit assessment tool. Assessing research and development tax credits in the course of tax reporting is a legal interaction because tax reporting is required by law (i.e. a legal obligation). It is also a commercial interaction because the research and development expenses are a factor in producing goods and services for commerce, similar to marketing and advertising (i.e. sales activities). Applicant argued that the claimed embodiment does not relate to a legal obligation as it does not resolve or pertain to resolution of any legal right. However, a tax credit is a legal right, and assessing the value of a tax credit directly speaks to a taxpayer’s ability to take advantage of the tax credit. Applicant’s claimed invention does not recite tax reporting, it is still a fact that tax reporting is a legal obligation to which the assessment of a tax credit is included. Tax credit assessments essentially have no meaning outside of the context of the legal obligation of tax reporting, and a tax credit is a legal right, as previously stated. The scope of legal obligations is also not limited to the enumerated examples listed in the October Update to the 2019 PEG. Applicant conceded that R&D is a part of the cost of bringing a product to market, and accounting for costs associated with bringing a product to market falls under sales activities and certain methods of organizing human behavior more generally. For these reasons, Examiner finds Applicant’s argument non-persuasive.
Applicant argued that Examiner’s 101 rejection was improper because the amendments “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” 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” distinguish the claimed subject matter from generic computer implementations. Examiner disagrees. Declaring that a particular dataset is used to train a machine learning algorithm for the task of identifying qualifying research projects based on pattern recognition of technical activities is not a representation of a concrete technical solution. It represents the use of the machine learning algorithm as a tool to implement the abstract idea of identifying qualifying research projects based on pattern recognition of technical activities, which is not indicative of patent eligible subject matter. Using custom scripts to extract metadata also is not indicative of patent eligible subject matter because it is not a technical improvement, as Applicant suggests. It is not a technical improvement because it does not represent new functionality that did not previously exist in computer technology. Therefore, Examiner finds Applicant’s argument non-persuasive.
Applicant argued that Examiner’s 101 rejection was improper because the amended claims represent an improvement to computers. Applicant further asserted the improvement is based on machine learning that is at the root of the claimed method/system. Examiner disagrees. The machine learning recited in Applicant’s claimed invention does not represent an improvement to computers because it is recited at a high level of generality. The outputs generated by the claimed invention have been mischaracterized by Applicant as physical outputs. The lists, interview preparation packages, and files of contemporaneous documentation are the abstract results of an abstract idea. They are not physical outputs. Therefore, Examiner finds Applicant’s argument non-persuasive.
Applicant argued that the amended claims demonstrate integration into a practical application by solving the specific technical problem of automatically processing disparate technical data sources to identify qualifying R&D activities. Examiner disagrees. Automatically processing disparate technical data sources to identify qualifying R&D activities is part of the abstract idea implemented by applying the additional limitations of the claimed invention. There was not a specific technical problem solved by the claimed invention because it merely applied the additional limitations in a generic manner to implement the abstract idea of automatically processing disparate technical data sources to identify qualifying R&D activities. Therefore, Examiner finds Applicant’s argument non-persuasive.
Applicant argued that Examiner’s 101 rejection was improper because the machine learning training methodology and automated metadata extraction elements provide an inventive concept that transforms the abstract idea. Applicant further asserted that the combination of training on historical audit defense data with pattern recognition of technical activities creates a specialized technical tool that improves upon conventional R&D assessment methods. Examiner disagrees. The combination of training on historical audit defense data with pattern recognition of technical activities does not create a specialized technical tool. It represents a generic machine learning algorithm used as a tool to perform a specific task. The use of the machine learning algorithm as a tool may demonstrate an improvement upon conventional R&D assessment methods, but it is not a technical improvement upon any technology because it does not solve any technological problems associated with machine learning algorithms. There is also not an inventive concept present in the training methodology and automated metadata extraction elements because no technological limitations were overcome to train the machine learning algorithm or automate the metadata extraction. Therefore, Examiner finds Applicant’s argument non-persuasive.
Applicant argued that Examiner’s 101 rejection was improper because the automatic extraction of lines code, commit history, and issue tracking data to establish quantitative relationships represents a technical advancement in data processing that goes beyond applying a generic computer to an abstract idea. Examiner disagrees. The automatic extraction of lines code, commit history, and issue tracking data to establish quantitative relationships does not represent a technical advancement in data processing because it did not involve overcoming a technical limitation or achieving new functionality that did not previously exist. Therefore, Examiner finds Applicant’s argument non-persuasive.
Applicant argued that the prior art did not teach or suggest the feature of automation recited in the amended claims. Examiner disagrees. The Hahn reference teaches automated features in its system and method for obtaining data related to R&D tax credits and calculating the R&D tax credit (see Hahn: col 3, lines 15-25). Therefore, Examiner finds Applicant’s argument non-persuasive.
Applicant argued that the prior art did not teach or suggest machine learning training on audit defense data. Examiner disagrees. The Humphrey reference teaches machine learning training on audit defense data (see Humphrey: pghs 19 and 54). Therefore, Examiner finds Applicant’s argument non-persuasive.
Applicant argued that the combination of references lacks a motivation to modify the cited references to arrive at the claimed automated analysis of version control metadata. Examiner disagrees. There was a motivation to combine Dankowych with Hahn because there exists a need to gather information about a particular project and compile it in a proscribed and meaningful format to the relevant tax authority. There was a motivation to combine Humphrey with Hahn/Dankowych in order to accurately capture tax benefit that would otherwise be lost. For these reasons, Examiner finds Applicant’s argument non-persuasive.
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;
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, 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 2-10 and 12-20 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 2-10 and 12-20 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.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hahn (US 8,544,726) in view of Dankowych (CA 2491381) and in view of Humphrey (US 2007/0156564).
Regarding claim(s) 1 and 11:
Hahn teaches:
an interface that is configured to access a plurality of data sources; (see Hahn: col 6, line 65 – col 7, line 5, “…the R&D project/activity survey tools are provided to employees of the client business through one or more database interface systems…”
a memory component that stores and manages data relating to research and development assessment; (Hahn: col 13, lines 55-60, “…the term ‘computing system’, includes, but is not limited to: a desktop computer…”)
a computer processor coupled to the interface and the memory component, (Hahn: col 7, lines 10-15, “…the one or more database interface systems interface with databases implemented via one or more computing systems…”)
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; (Hahn: col 12, lines 25-40, “…any other evidentiary data supporting the calculated, and claimed, R&D tax credit, is automatically correlated and securely stored…”; col 34, lines 30-40, “…automatically, and securely, stores, and limits access to, necessary audit source documentation…”; Hahn: col 2, lines 7-15, “…evidence related to the qualification of R&D projects/activities, and time spent by the employee performing qualified R&D activities, should be in the form of primary source documents. That is, each employee engaged in R&D activities should submit contemporaneous documentation about the type of activity they performed, as well as the amount of time spent on qualified R&D projects and activities.”)
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 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; (Hahn: col 8, lines 49-55, “…the employee R&D activity data is automatically analyzed, and classified, using the provided coding system.”; col 10, lines 10-25, “In one embodiment, the cost to the client business of each employee’s time devoted to qualified R&D is aggregated…In various embodiments, data indicating the cost to the client business of each employee’s time devoted to qualified R&D, and/or data indicating a total cost to the client business of all employee time devoted to qualified R&D, is then stored…the cost to the client business of each employee’s time devoted to qualified R&D, and/or the aggregated total cost to the client business of all employee time devoted to qualified R&D, is calculated based on various formulas and/or statutory rules for determining the R&D tax credit.”)
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; (Hahn: col 4, lines 30-40, “…all, or part of: the collected employee and contractor R&D activity data for each employee and contractor; the qualified R&D cost data for the client business…is automatically correlated…”; col 35, line 50 – col 36, line 5, “…the system and/or functionality of the invention may be implemented via various combinations of software and hardware…”; col 15, lines 35-40, “…database interface system is associated with one or more client computing systems and provides access to one or more database systems, that provide employee R&D activity data entry/display, such as employee R&D activity data…”)
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; (Hahn: col 20, lines 5-10, “In one embodiment, the accounting professional, and/or a client business of the accounting professional, then select the appropriate pre-created standardized form depending upon the business type, industries, and/or market of client business.)
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. (Hahn: col 3, lines 55-60, “In one embodiment, each employee’s and each contractor’s percentage of time spent performing qualified R&D activity is linked automatically to each employee’s payroll data…”; col 9, lines 60-65, “In one embodiment, payroll data for each employee is automatically obtained from the one or more data management systems…”)
Hahn does not teach, however, Dankowych teaches:
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; (Dankowych: pghs 3-4, “…the applicant needs to undertake considerable effort to gather information about a particular project and compile it in a proscribed and meaningful format…The questionnaire is for collecting data used in the documenting and calculating R&D tax credit.)
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; (Dankowych: pgh 4, “At least some of the information collected from the more than one of the plurality of users is automatically verified while the information is being input…”)
generating a credit calculation with contemporaneous technical documentation supporting a research and development credit for the project; (Dankowych: pgh 4, “Tax credit information is calculated based upon the allocation of financial resources regarding the one or more projects.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Hahn to include the teachings of Dankowych because “In order to claim tax credits…the applicant needs to undertake considerable effort to gather information about a particular project and compile it in a proscribed and meaningful format to the relevant tax authority” (Dankowych: pgh 3). Hahn/Dankowych does not teach the remaining limitation. However, Humphrey teaches:
identifying, via a recommendation engine implementing machine learning, at least one qualifying research and development project from the extracted raw activity data and metadata; (Humphrey: pgh 54, “The answers to the database can then be examined, either by a person, or using machine learning algorithms known in the art, and questions not on the current question list can then be created using a new question transformer.”)
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, and (Humphrey: pgh 19, “In at least some embodiments, the system is interactive, and learns from the data it receives. For example, different qualifying activities have the potential for having different qualified costs and different reporting requirements. The system is configured to align with what specific people are doing within various activities.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Hahn/Dankowych to include the teachings of Humphrey in order to accurately capture tax benefit that would otherwise be lost (Humphrey: pgh 8).
Regarding claim(s) 2 and 12:
The combination of Hahn/Dankowych/Humphrey, as shown in the rejection above, discloses the limitations of claims 1 and 11, respectively. Hahn further teaches:
wherein the plurality of data sources comprise project management systems, versioning control software repositories, employee rosters and payroll systems. (Hahn: col 3, lines 35-45, “…a computing system implemented…business financial management system…a computing system implemented…payroll management system…”)
Regarding claim(s) 3 and 13:
The combination of Hahn/Dankowych/Humphrey, as shown in the rejection above, discloses the limitations of claims 1 and 11, respectively. Hahn further teaches:
wherein the subject matter expert identification component comprises identifying one or more proper contacts associated with one or more components of the project. (Hahn: col 1, lines 40-45, “…the amount of time spent by each of the business’ employees and contractors performing qualified R&D activities must be quantified.”)
Regarding claim(s) 4 and 14:
The combination of Hahn/Dankowych/Humphrey, as shown in the rejection above, discloses the limitations of claims 1 and 11, respectively. Hahn further teaches:
wherein the business identification component comprises identifying potentially qualifying research and development projects. (Hahn: col 1, lines 40-45, “To quantify the qualifying R&D costs, each employee’s and each contractor’s percentage of time spent performing qualified R&D activities is applied to his/her compensation.”)
Regarding claim(s) 5 and 15:
The combination of Hahn/Dankowych/Humphrey, as shown in the rejection above, discloses the limitations of claims 4 and 14, respectively. Hahn further teaches:
wherein the business identification is performed via a business components recommendation engine that compares development activity data against a training data set containing activity data and business components that were upheld during a prior audit defense. (Hahn: col 8, lines 50-57, “…the accounting professional is alerted to any employee R&D activity data that appears to be in error, incomplete, inaccurate, or extraordinary, based on historical input and analysis regarding the client business itself, and/or data from similarly situated companies, and/or client businesses.”)
Regarding claim(s) 6 and 16:
The combination of Hahn/Dankowych/Humphrey, as shown in the rejection above, discloses the limitations of claims 1 and 11, respectively. Dankowych further teaches:
wherein the one or more interview preparation packages are used with one or more subject matter experts to determine whether research and development credits qualify for the project. (Dankowych: pgh 41, “…each client device is accessible by various users who have information of a particular project for which SR&ED tax credit eligibility is to be assessed, and where such information can by used to complete questionnaire…”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Hahn to include the teachings of Dankowych because “In order to claim tax credits…the applicant needs to undertake considerable effort to gather information about a particular project and compile it in a proscribed and meaningful format to the relevant tax authority” (Dankowych: pgh 3).
Regarding claim(s) 7 and 17:
The combination of Hahn/Dankowych/Humphrey, as shown in the rejection above, discloses the limitations of claims 6 and 16, respectively. Hahn further teaches:
wherein the interview preparation packages are generated by the recommendation engine that provides an activity overview that is leveraged during one or more technical interviews. (Hahn: col 5, lines 60-65, “…the employee R&D project/activity survey forms are standardized forms generated for defined business types, industries, and/or markets.”; col 4, lines 20-35, “…all or part of: the collected employee and contractor R&D activity data for each employee and contractor…is automatically correlated and securely stored…”)
Regarding claim(s) 8 and 18:
The combination of Hahn/Dankowych/Humphrey, as shown in the rejection above, discloses the limitations of claims 1 and 11, respectively. Hahn further teaches:
wherein the pre-qualified time survey data comprises time spent details to determine activity credit and value for the project. (Hahn: col 2, lines 5-10, “…evidence related to the qualification of R&D projects/activities, and time spent…should be in the form of primary source documents.”)
Regarding claim(s) 9 and 19:
The combination of Hahn/Dankowych/Humphrey, as shown in the rejection above, discloses the limitations of claims 1 and 11, respectively. Hahn further teaches:
wherein the activity nexus matrix is by employee and by project. (Hahn: col 3, lines 55-60, “…each employee’s and each contractor’s percentage of time spend performing qualified R&D activity is linked automatically to each employee’s payroll data…”)
Regarding claim(s) 10 and 20:
The combination of Hahn/Dankowych/Humphrey, as shown in the rejection above, discloses the limitations of claims 1 and 11, respectively. Hahn further teaches:
wherein the project data comprises JIRA data and GIT data. (Hahn: col 6, 55-60, “...in various embodiments…provides the employees with a meaningful, convenient, and intuitive method for providing contemporaneous employee R&D project/activity data…”)
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.
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/JOHN O PRESTON/Examiner, Art Unit 3693
March 12, 2026
/ELIZABETH H ROSEN/Primary Examiner, Art Unit 3693