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 the Claims
The Amendment filed on 04/28/2026 has been entered. Claims 1-20 are pending in the instant patent application. Claims 1, 8 and 15 are amended. This Final Office Action is in response to the claims filed.
Response to Claim Amendments
Applicant’s amendments to the claims are insufficient to overcome the 35 U.S.C. §101 rejections. The rejections remain pending and are updated and addressed below in light of the amendments and per guidelines for 101 analysis (PEG 2019).
Applicant’s amendments to the claims are sufficient to overcome the 35 U.S.C. §103 rejections. The rejections have been withdrawn with reasoning provided below.
Applicant’s amendments have further necessitated new grounds of rejection under 35 U.S.C. §112.
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, 8 and 15 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, 8 and 15 recite that "in response to the technical debt score exceeding a threshold, generating, via the natural language generation model, replacement code snippets". However, in the specification this is not properly described. Para 0032 and 0071 speak on generating code snippets, though it does not recite generating them based upon a threshold.
Response to 35 U.S.C. §101 Arguments
Applicant’s arguments regarding 35 U.S.C. §101 rejection of the claims have been fully considered, but are not persuasive. It also appears that Applicant’s arguments are made in light of the amended language.
Regarding Applicant’s arguments that the previous Office Action’s interpretation exceeds the broadest reasonable interpretation, Examiner respectfully disagrees and maintains that the 101 analysis was performed according to the PTO’s guidelines for 101 eligibility and the claim language was taken at its broadest reasonable interpretation without oversimplifying. Regarding Mental Processes, Examiner will further note that the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind (see MPEP 2106.04(a)(2)(III)(C)). Examiner maintains that the claims, as amended, still recites elements performing in their generic capacity.
Regarding Applicant’s assertion that the claims are analogous to claims that were found not to recite Mental Processes or Mathematical Concepts, Examiner respectfully disagrees. The claim language is in fact, in line with the ineligibility of Example 47, Claim 2. Like Example 47, Claim 2, the computing elements are recited at a high level of generality, i.e., as a generic computer performing generic computer functions. In addition, the Step 2A- Prong Two and Step 2B analysis of claim 2 of Example 47 states, in part, all uses of the recited judicial exceptions require data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05. The recitation of “using a trained ANN” in limitations (d) and (e) also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a trained ANN” limits the identified judicial exceptions “detecting one or more anomalies in a data set using the trained ANN” and “analyzing the one or more detected anomalies using the trained ANN to generate anomaly data,” this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Examiner finds there are no similar technological improvements here. The machine learning model does not recite an improvement to the functioning of an artificial intelligence technology, computer-related technology or any technological field, thus failing to add an inventive concept to the claims. Applicant has not made any persuasive argument that would alter this analysis. Examiner maintains the claims are directed to an abstract idea.
Regarding Applicant’s assertion that the claims are eligible under Step 2A, Prong Two, Examiner respectfully disagrees. Examiner maintains that any asserted improvements are not reflected in the same language. In addition, the limitations are reciting elements that are merely being used as tools to carry out the abstract idea and further make an attempt to generally link the use of the judicial exception to a particular technological environment. In addition, Examiner has considered each claim and every limitation of which both individually and as a whole as according to the PTO's guidelines for 101 eligibility. Furthermore, Examiner stated the as merely generic computing devices, because that is how they are presented in light of the claim language and with the amended claim language. In addition, Examiner did not state that any element was well understood, routine or conventional in the Step 2A Prong 2 analysis portion. Any assertion of such was made in the Step 2B portion of the analysis.
Regarding DDR, Examiner has already noted that in DDR Holdings, the court found the claims to be patent eligible because the claims recites the solution of a hybrid webpage that co-displays the look and feel of the first website with the desired content from the second website. The court found such solution to be rooted in computer technology because there was no other way to accomplish such solution. The computer was an essential part of performing the solution. Although the current claims recite functions performed by a computer system and a neural network, such functions are not found to be significantly more when recited in their generic manner. Applicant asserts that the claims “define a particular technological workflow for addressing a technology-centric problem in complex software ecosystems”, for which the Examiner respectfully disagrees and maintains that the claims recite generic computing elements implemented and functioning within their generic capacity.
Applicant further asserts that the claims are similar to the reasoning given in Ex Parte Desjardins, for which the Examiner respectfully disagrees. As stated before, the features discussed in the Prong Two analysis do not integrate the abstract idea into a practical application because again, the features are merely being used as tools to carry out the abstract idea and merely tie it to a particular technological field.
Regarding Step 2B, Examiner did evaluate whether the claim recites any additional elements individually and in combination, that amount to significantly more than the abstract idea and found that nothing in the claim language accomplished such. Even in light of the amended language, the elements are recited generically and the claim language further recites computer functions that the courts have recognized as well-understood, routine, and conventional functions when they are claimed in a merely generic manner. The “claimed arrangement” does not recite an inventive concept and do not recite significantly more.
Examiner recommends the Applicant in at least, amend the claim language so that the additional elements alone and in combination, go beyond the very nature of their functioning. Applying generic AI/machine learning tools to a specific field does not confer eligibility.
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.
Regarding Claims 1-7, they are directed to a system, however the claims are directed to a judicial exception without significantly more. Claims 1-7 are directed to the abstract idea of evaluating and managing technical debt.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 1, claim 1 recites collect, via the one or more processors, data from a technology ecosystem, the data including software and hardware platforms, code repositories, architecture diagrams, tools, and processes; clean, normalize, and categorize the collected data from the technology ecosystem to identify and retain only data points relevant to technical debt assessment; determine, coding signals from the collected data, wherein the coding signals are based on code quality metrics and business capabilities; receive, inputs, to inform a technology ecosystem assessment; assign, weighted scores to the coding signals based on their impact on technical debt accumulation, wherein the weighted scores are determined based at least in part on expert knowledge and industry best practices; classify, the weighted coding signals and their weighted scores into score ranges to produce classified coding signals, wherein the scoring ranges are indicative of code quality, complexity, and adherence to best practices; determine, non-linear relationships between the classified coding signals and the business capabilities; provide structured instructions and prompts to a model fine- tuned for the technical debt assessment, the structured instructions and prompts that facilitate the model in generating relevant analyses and responses; generate and based on the classified coding signals, a natural language report, wherein the natural language report includes reasoning for the assigned score ranges, identification of coding patterns contributing to technical debt accumulation, and recommended code modifications for technical debt reduction; evaluate, the collected data, the coding signals, the inputs, and the business capabilities; generate a technical debt score based on the evaluation, wherein the technical debt score represents the technical debt of the technology ecosystem, predict, future states of technical debt based on the technical debt score and the classified coding signals; update the historical data with the classified coding signals, the natural language report, and the technical debt score, and adjust the weighted scores based on the natural language report; and in response to the technical debt score exceeding a threshold, generate, via the model, replacement code snippets.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be performed in the human mind and/or with pen and paper (including observations, evaluations, judgements, opinions). Examiner respectfully reminds Applicant, regardless of the complexity and/or granularity of the type of data, computational data analysis without meaningful limitations within the claims that amount to significantly more is a judicial exception (i.e. abstract idea). Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind (see MPEP 2106.04(a)(2)(III)(C)).
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of one or more processors, one or more memories, train via the one or more processors a neural network model using historical data wherein the historical data includes coding practices and outcomes from previous technical debt assessments, a technology ecosystem, a technical debt assessment system, a natural language generation model, scoring system, neural network and organizational redesign system. The one or more processors, one or more memories, train via the one or more processors a neural network model using historical data wherein the historical data includes coding practices and outcomes from previous technical debt assessments, technology ecosystem, scoring system, a natural language generation model, a technical debt assessment system and organizational redesign system are merely generic computing devices and do not integrate the judicial exception into a practical application. In addition, Claim 1’s use of a neural network and natural language generation model and Claim 4’s use of artificial intelligence is merely being used as a tool to carry out the abstract idea.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 1 and 4 include various elements that are not directed to the abstract idea under 2A. These elements include one or more processors, one or more memories, train via the one or more processors a neural network model using historical data wherein the historical data includes coding practices and outcomes from previous technical debt assessments, a technical debt assessment system, scoring system, organizational redesign system, a natural language generation model, neural network, artificial intelligence and the generic computing elements described in the Applicant's specification in at least Para 0024-0026. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions. In addition Claim 1 recites computer functions (in at least the “collect” and “receive” limitations) that the courts have recognized as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (See MPEP 2106.05(d)(ii)...at least, Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Therefore, Claims 1 and 4, alone or in combination, are not drawn to eligible subject matter as they are directed to abstract ideas without significantly more.
Regarding Claims 8-14, they are directed to a method, however the claims are directed to a judicial exception without significantly more. Claims 8-14 are directed to the abstract idea of evaluating and managing technical debt.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 8, claim 8 recites collecting, by the one or more processors, data from a technology ecosystem, the data including software and hardware platforms, code repositories, architecture diagrams, tools, and processes; cleaning, normalizing, and categorizing the collected data from the technology ecosystem to identify and retain only data points relevant to technical debt assessment; determining, coding signals from the collected data, wherein the coding signals are based on code quality metrics and business capabilities; receiving, inputs, to inform a technology ecosystem assessment; assigning, weighted scores to the coding signals based on their impact on technical debt accumulation, wherein the weighted scores are determined based at least in part on expert knowledge and industry best practices; classifying, the weighted coding signals and their weighted scores into score ranges to produce classified coding signals, wherein the scoring ranges are indicative of code quality, complexity, and adherence to best practices; determining, non-linear relationships between the classified coding signals and the business capabilities; providing structured instructions and prompts to a model fine-tuned for technical debt assessment, structured instructions and prompts that facilitate a model in generating relevant analyses and responses; generating, by the model and based on the classified coding signals, a natural language report, wherein the natural language report includes reasoning for the assigned score ranges, identification of coding patterns contributing to technical debt accumulation, and recommended code modifications for technical debt reduction; evaluating, the collected data, the coding signals, the inputs, and the business capabilities; generating, a technical debt score based on the evaluation, wherein the technical debt score represents the technical debt of the technology ecosystem; predicting, future states of technical debt based on the technical debt score and the classified coding signals; updating the historical data with the classified coding signals, the natural language report, and the technical debt score, and adjusting the weighted scores based on the natural language report; and in response to the technical debt score exceeding a threshold, generating, via the model, replacement code snippets.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be performed in the human mind and/or with pen and paper (including observations, evaluations, judgements, opinions). Examiner respectfully reminds Applicant, regardless of the complexity and/or granularity of the type of data, computational data analysis without meaningful limitations within the claims that amount to significantly more is a judicial exception (i.e. abstract idea). Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind (see MPEP 2106.04(a)(2)(III)(C)).
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of training via the one or more processors a neural network model using historical data wherein the historical data includes coding practices and outcomes from previous technical debt assessments, one or more processors, scoring system, natural language generation model, a neural network model using historical data comprising best practices and outcomes from previous technical debt assessments, one or more processors, a technology ecosystem, a technical debt assessment system and organizational redesign system. The training via the one or more processors a neural network model using historical data wherein the historical data includes coding practices and outcomes from previous technical debt assessments, one or more processors, scoring system, natural language generation model, a neural network model using historical data comprising best practices and outcomes from previous technical debt assessments, one or more processors, a technology ecosystem, a technical debt assessment system and organizational redesign system are merely generic computing devices and do not integrate the judicial exception into a practical application. In addition, Claim 8’s use of a neural network model, natural language generation model and a neural network, and Claims 11’s use of artificial intelligence is merely being used as a tool to carry out the abstract idea.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 8 and 11 include various elements that are not directed to the abstract idea under 2A. These elements include one or more processors, training via the one or more processors a neural network model using historical data wherein the historical data includes coding practices and outcomes from previous technical debt assessments, a technology ecosystem, a technical debt assessment system, organizational redesign system, neural network model, neural network, scoring system, artificial intelligence and the generic computing elements described in the Applicant's specification in at least Para 0024-0026. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions. In addition Claim 8 (in at least the “collecting” and “receiving” limitations) recites computer functions that the courts have recognized as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (See MPEP 2106.05(d)(ii)...at least, Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Therefore, Claims 8 and 11, alone or in combination, are not drawn to eligible subject matter as they are directed to abstract ideas without significantly more.
Regarding Claims 15-20, they are directed to a method, however the claims are directed to a judicial exception without significantly more. Claims 15-20 are directed to the abstract idea of evaluating and managing technical debt.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 15, claim 15 recites collecting data from a technology ecosystem, the data including software and hardware platforms, code repositories, architecture diagrams, tools, and processes; cleaning, normalizing, and categorizing the collected data from the technology ecosystem to identify and retain only data points relevant to technical debt assessment; determining coding signals from the collected data, wherein the coding signals are based on code quality metrics and business capabilities; receiving inputs to inform a technology ecosystem assessment; assigning weighted scores to the coding signals based on their impact on technical debt accumulation, wherein the weighted scores are determined based at least in part on expert knowledge and industry best practices; classifying, the weighted coding signals and their weighted scores into score ranges to produce classified coding signals, wherein the scoring ranges are indicative of code quality, complexity, and adherence to best practices; determining, non-linear relationships between the classified coding signals and the business capabilities; providing structured instructions and prompts to a model fine-tuned for technical debt assessment, structured instructions and prompts that facilitate the model in generating relevant analyses and responses; generating, by the model and based on the classified coding signals, a natural language report, wherein the natural language report includes reasoning for the assigned score ranges, identification of coding patterns contributing to technical debt accumulation, and recommended code modifications for technical debt reduction; evaluating the collected data, the coding signals, the inputs, and the business capabilities; generating a technical debt score based on the evaluation, wherein the technical debt score represents the technical debt of the technology ecosystems predicting, future states of technical debt based on the technical debt score and the classified coding signals; updating the historical data with the classified coding signals, the natural language report, and the technical debt score, and adjusting the weighted scores based on the natural language report; and in response to the technical debt score exceeding a threshold, generating, via the model, replacement code snippets.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be performed in the human mind and/or with pen and paper (including observations, evaluations, judgements, opinions). Examiner respectfully reminds Applicant, regardless of the complexity and/or granularity of the type of data, computational data analysis without meaningful limitations within the claims that amount to significantly more is a judicial exception (i.e. abstract idea). Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind (see MPEP 2106.04(a)(2)(III)(C)).
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of training via the one or more processors a neural network model using historical data wherein the historical data includes coding practices and outcomes from previous technical debt assessments, one or more processors, scoring system, natural language generation model, a neural network model using historical data comprising best practices and outcomes from previous technical debt assessments, one or more processors, a technology ecosystem, a technical debt assessment system and organizational redesign system. The training via the one or more processors a neural network model using historical data wherein the historical data includes coding practices and outcomes from previous technical debt assessments, one or more processors, scoring system, natural language generation model, a neural network model using historical data comprising best practices and outcomes from previous technical debt assessments, one or more processors, a technology ecosystem, a technical debt assessment system and organizational redesign system are merely generic computing devices and do not integrate the judicial exception into a practical application. In addition, Claim 15’s use of a neural network model, natural language generation model and a neural network, and Claims 18’s use of artificial intelligence is merely being used as a tool to carry out the abstract idea.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 15 and 18 include various elements that are not directed to the abstract idea under 2A. These elements include one or more processors, training a neural network model using historical data wherein the historical data includes coding practices and outcomes from previous technical debt assessments, a computing system, a neural network, a technology ecosystem, a scoring system, a technical debt assessment system, organizational redesign system, artificial intelligence and the generic computing elements described in the Applicant's specification in at least Para 0024-0026. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions. In addition Claim 15 (in at least the “collecting” and “receiving” limitations) recites computer functions that the courts have recognized as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (See MPEP 2106.05(d)(ii)...at least, Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Therefore, Claims 15 and 18, alone or in combination, are not drawn to eligible subject matter as they are directed to abstract ideas without significantly more.
Distinguishable Over the Prior Art
Examiner analyzed newly amended claims 1, 8 and 15 in view of the prior art on record and finds not all claim limitations are explicitly taught nor would one of ordinary skill in the art find it obvious to combine these references, or more, with a reasonable explanation of success as discussed below.
While Chen et al. (US 2024/0402999 A1) teaches of perform a range of coding tasks based on natural language processing. Example tasks include code completion (e.g., suggesting code completions for developers as they write code; e.g., if a developer starts typing a line of code and then pauses, code snippets may be suggested to complete the task, which may save time and improve the accuracy of the code being written), automated testing (e.g., generating test cases and test code, which may help developers ensure that their code is functioning correctly and catch bugs from the outset), code refactoring (e.g., suggesting changes to existing code that can improve its efficiency, readability, and maintainability, which may help developers optimize their code and reduce technical debt), natural language processing (e.g., processing natural language queries and generating code based on those queries, which may be useful for developers who are not familiar with a particular programming language or who need to write code quickly), intelligent coding assistants (e.g., building intelligent coding assistants that can help developers perform complex coding tasks; e.g., a coding assistant may help a developer build a machine learning model or optimize a database query), code generation for low-code platforms (e.g., integrating with platforms designed to make it easier for non-technical users to build applications to help generate the code needed to build the application, which may help reduce the technical knowledge required to build an application and speed up the development process), code synthesis for code reviews (e.g., automatically generating code changes based on code review comments, which may save time and improve the efficiency of the code review process), rapid prototyping (e.g., quickly prototyping new ideas and testing out different approaches to coding problems, which may help developers iterate on their ideas more quickly and efficiently, code analysis and optimization (e.g., analyzing existing code and suggesting ways to improve it; e.g., suggesting ways to reduce the complexity of code, improve its performance, or reduce its memory footprint), and game development (e.g., generating code for game development, including game engines, physics simulations, and artificial intelligence algorithms, which may game developers create more complex and realistic games more efficiently.
And while Balasubramanian et al. (US 2022/0269795 A1) teaches of a quality summary service uses machine learning and natural language generation techniques to generate the quality summary insights. The quality summary service looks up the quality information that include details, such as the number of bugs and number of issues highlighted in its code quality scan against quality best practices. Based on the quality information, a quality score is determined. The quality summary service compares the quality score with other similar software components and leverages natural language generation to describe the quality summary insights to the user. The input software component is identified, and details are gathered. In step 502, software component's quality information and its information sources are identified. The quality scores for software components are passed on to the next step 503. In step 503, the templates are identified for generating the quality information. The data with details of quality information is collected and sent to the next step 504. The step 504 is the neural network training for generating quality summary wherein the neural network is trained with the details of software components and quality information. In step 505, the neural network model trained in step 504 generates a Natural language summary as quality summary of a software component.
And while Neves et al. (US 2022/0137959 A1) teaches of architecture dashboard shows modules within a factory. The level of technical debt in each module may be indicated by a visual marker such as the background color of the module. For example, red modules have the most technical debt, orange modules have medium technical debt, and green modules have the least technical debt. Users can drill down into the modules, for example performing the disclosed code duplication identification and refactoring techniques to reduce the technical debt. Upon clicking on a module, a graphical user interface such as the one shown in the following figure is displayed. FIG. 10 is a diagram illustrating an example of an architecture dashboard obtained in some embodiments. This graphical user interface shows an example pattern identified using the disclosed techniques. When a pattern is selected, a preview is displayed in the right-hand panel. The panel shows the portion of the logic where the pattern appears. The pattern is linked to a visual modeling environment so a user can be re-directed to the visual modeling environment to modify the flow. For example, the user can use the “extract to action” functionality (which automatically creates a function from the selected portion of code) to refactor the code pattern into a function.
And while Guenther et al. (US 2020/0183818 A1) teaches of a code analysis can use a set of standard code tests, custom code tests, or a combination thereof. Or, a user may be allowed to select or deselect particular tests to run, or to view results of fewer tests than were actually performed. Further, a user may be allowed to set parameters for various tests, or create test variants…a user may be equally interested in all types of code principle violations. In other cases, some code principle violations may be considered more or less problematic than other types of violations. Accordingly, tests can be assigned qualitative rankings (e.g., “high,” “medium,” “low” severity), rather than, or in addition to, providing quantitative results (e.g., a number of issues of each type, where a number of issues can also be correlated with a qualitative ranking, such as <5 being “low,” >5 and <15 being “medium,” and >15 being “high”).
In view of the prior art on record, Examiner finds not all claim limitations of Claims 1, 8 and 15 are explicitly taught nor would one of ordinary skill in the art find it obvious to combine these references, or more, with a reasonable explanation of success to in at least teach the limitations of
“providing structured instructions and prompts to a natural language generation model fine-tuned for technical debt assessment, structured instructions and prompts that facilitate the natural language generation model in generating relevant analyses and responses; generating, by the natural language generation model and based on the classified coding signals, a natural language report, wherein the natural language report includes reasoning for the assigned score ranges, identification of coding patterns contributing to technical debt accumulation, and recommended code modifications for technical debt reduction”.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TYRONE E SINGLETARY whose telephone number is (571)272-1684. The examiner can normally be reached 9 - 5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached at 571-272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/T.E.S./ Examiner, Art Unit 3625
/BETH V BOSWELL/ Supervisory Patent Examiner, Art Unit 3625
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