DETAILED ACTION
Case Status
This office action is in response to remarks and amendments of 15 June 2026. Claims 1-3, 5-7, and 21-32 have been examined.
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 1-3, 5-7, and 21-32 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, 24 and 29 include “wherein the code intelligence tool update and the test results remain unmodified during tuning of the automated evaluator.” The specification describes tuning an evaluator and the evaluator operating on the test results, but this is not the same as saying the code intelligence tool update and the test results remaining unmodified during tuning of the automated evaluator. All respective dependent claims are likewise rejected.
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 24-32 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because “A computer program product” is not claimed or disclosed as comprising hardware elements, such as the recited processor. Accordingly, “A computer program product” can be interpreted as being software per se which is non-statutory subject matter. Respective dependent claims are likewise rejected.
Claim Rejections - 35 USC § 103
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.
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.
The factual inquiries 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 5-7, and 21-32 are rejected under 35 U.S.C. 103 as being unpatentable over Dibia et al., Pub. No.: US 20240045660 A1, hereinafter Dibia in view of Khetan et al., Pub. No.: US 20230252287 A1, hereinafter Khetan.
As per claim 1, Dibia discloses A system comprising:
a processor; and a computer-readable medium storing instructions that are operative upon execution to cause the system (pars. 88-95) to:
receive, by a quality gating function, a code intelligence tool update, wherein the code intelligence tool update comprises an update to software configured to analyze programming code (pars. 23-24, 27, disclose that code generators that operate on programming code are evaluated and a top performing code generator is identified and used);
generate test results by executing the code intelligence tool update operating on code intelligence tool test data that represents one or more programming tasks (pars. 20, 26-27, 35-39, 51-53 disclose programming tasks/constituent blocks, generation of equivalent code blocks, replacement of blcoks in source code, test execution on the modified code and generating test results);
generate, via an automated evaluator, first evaluation results based on the test results, the automated evaluator including a machine learning (ML) model trained to assess performance of the code intelligence tool update (pars. 20-21, 27-29, 40, 47, 52-55 disclose automated CAT evaluation that determines coding scores based on test results and semantic similarity, including a learned neural similarity, and aggregates the coding scores to evaluate code generator performance);
determine a first quality score for the first evaluation results using a hierarchical quality criteria data structure (par. 21, 28-29, 33-34, 47-48, 55 disclose multiple weighted criteria (for coding score computation), aggregation of the weighted coding scores into a first aggregate score, and further aggregation with acceptance scores into a second aggregate score, that form “a hierarchical quality criteria structure”);
Dibia does not expressly disclose however Khetan discloses assess reliability of the automated evaluator by comparing the first quality score with a desired quality score; in response to determining that the first quality score is lower than the desired quality score, iteratively tune the automated evaluator by adjusting weights of the ML model until reliability of the automated evaluator satisfies a criteria, wherein the code intelligence tool update and the test results remain unmodified during tuning of the automated evaluator (Khetan, pars. 39-40, 46-47, 51-52, 62-63 disclose ML model reliability evaluation by comparing a performance score to a threshold, and if below the threshold, iteratively retraining and re-evaluating the model until an acceptable performance is reached, and that model weights are adjusted during fine-tuning using an updated learning rate (Khetan par. 47). Regarding “wherein the code intelligence tool update and the test results remain unmodified during tuning of the automated evaluator” note that Khetans model tuning technique is applied to Dibia’s already generated test results; previously generated code generator test results that serve as the evaluation input are not themselves being modified (pars. Khetan 47 ,52, 63)); and
update an operational code intelligence tool with the code intelligence tool update (see above cited pars. of Dibia including pars 23-24, 56-59 which disclose selecting the best performing code generator after ranking (ranking based on aggregate scores, selecting second plurality of code generators based on ranking and top performing code generators used to generate output passage of software code and further training selected code generators based on evaluation framework)).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because Khetans teaching would have allowed Dibia to evaluate the reliability of the ML based evaluator and iteratively tune its model weights when its quality score fails to satisfy a threshold in order to improve the reliability of the evaluation used to identify and deploy a top performing code intelligence tool.
As per claim 2, Dibia as modified discloses The system of claim 1, wherein the instructions are further operative to: employ the code intelligence tool update for code completion or a chat service regarding software code (see rejection of claim 1 and at least pars. 2, 24 write multiple lines of code, generate output passages of code, etc).
As per claim 3, Dibia as modified discloses The system of claim 1, wherein the automated evaluator comprises an evaluation model and an evaluation prompt, and wherein tuning the automated evaluator comprises tuning the evaluation model and/or tuning the evaluation prompt (see Khetan as cited above, and see Dibia, par. 40 discloses semantic similarity engine 401 compares constituent block 202 and equivalent block and par. 31 discloses code generators in pool of code generators 130 are trained by a machine learning (ML) trainer; this corresponds to evaluation models and tuning through training).
As per claim 5, Dibia as modified discloses the system of claim 1, wherein the instructions are further operative to: iterate generating evaluation results and tuning the automated evaluator until the desired quality score is achieved, wherein determining whether to update the operational code intelligence tool with the code intelligence tool update is based on at least achieving the desired quality score (see Khetan as cited above, and see Dibia, par. 51 includes “Operations 710 and 712 are performed for each constituent block, for which an equivalent block is generated, for each code generator” and pars. 54-56, 75-79 disclose additional coding scores are determined).
As per claim 6, Dibia as modified discloses The system of claim 1, wherein the hierarchical quality criteria data structure comprises relevance criteria, truth criteria, and completeness criteria, and wherein determining the first quality score comprises: rating the first evaluation results separately for each of the relevance criteria, the truth criteria, and the completeness criteria (Dibia pars. 18, 34, 40, 47, 53 disclose scoring using multiple quality metrics such as similarity metrics, test results, code utility metrics (complexity, readability, bug probability)).
As per claim 7, Dibia as modified discloses The system of claim 1, wherein determining the first quality score comprises: receiving annotations, or receiving telemetry data, or comparing the first evaluation results with ground truth data (see rejection of claim 1 and at least Dibia pars. 29, 41, 42, 57).
As per claim 21, Dibia as modified discloses The system of claim 1, wherein the reliability of the automated evaluator is assessed independently from the performance of the code intelligence tool update (Khetan, pars. 46-47 disclose separately evaluating a models reliability using a performance score and threshold).
As per claim 22, Dibia as modified discloses The system of claim 1, wherein the desired quality score is derived from annotations and telemetry corresponding to programming assessments (Dibia, pars. 31, 41-42, 51 disclose feedback and user acceptance “telemetry” used for tuning and Khetan, pars. 52, 58 disclose setting the desired quality threshold).
As per claim 23, Dibia as modified discloses The system of claim 1, wherein tuning the automated evaluator further includes: updating, after each tuning iteration, the first quality score to reflect the reliability of the automated evaluator, wherein the reliability of the automated evaluator satisfies the criteria when the updated first quality score is greater than or equal to the desired quality score (Khetan pars. 47, 52, 59, 63 disclose reevaluating after each fine tuning cycle and continuing until desired accuracy or threshold is reached).
As per claims 24-32, they are analogous to claims above and therefore likewise rejected.
Response to Arguments
Applicant's arguments filed 15 June 2026 have been considered.
The previous 35 USC 101 rejection is withdrawn in view of amendments directed to reliability assessment of the evaluator and iterative tuning thereof, in combination with all other limitations.
With respect to the prior art rejection, Khetan et al., Pub. No.: US 20230252287 A1, has been applied in response to claim amendments.
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 SYED HASAN whose telephone number is (571)270-5008. The examiner can normally be reached M-F 8am - 5 pm.
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, Boris Gorney can be reached at (571)270-5626. 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.
/SYED H HASAN/ Primary Examiner, Art Unit 2154
Prosecution Insights