DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This is in response to Application 19/072274 filed on March 6, 2025 in which Claims 1-20 are presented for examination.
Status of Claims
Claims 1, 4 and 6-20 have been amended. Claims 1-20 are pending, of which Claims 1-20 are rejected under 103.
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 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.
Claim(s) 1, 6, 9, 14 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kulkarni (US Patent Application 2023/0031997) in view of Sarkar (US Patent Application 2025/0147838) and further in view of Nychis (US Patent Application 2016/0259717).
Claim 1, Kulkarni teaches a method comprising: sending, from a developer portal system and to an issue repository, a request for an issue stack (View Kulkarni ¶ 67; developer requests log data), wherein the issue stack is a report generated in response to a user experiencing a software issue (View Kulkarni ¶ 67; log data includes overall state of application, performance results, failure information, processing error/issues, testing data and the like); receiving, from the issue repository and at the developer portal system, the issue stack (View Kulkarni ¶ 67; obtain log data based on tags); sending, by the developer portal system, the issue stack and an issue ID to an analysis engine to be analyzed (View Kulkarni ¶ 67; analyze log data); and sending the analysis result to the developer portal system for presentation at a user interface associated with the developer portal system (View Kulkarni ¶ 67; user interface).
Kulkarni does not explicitly teach identifying, by the analysis engine, a bot to analyze the issue stack, wherein the identified bot comprises a first machine learning algorithm, and wherein the identified bot corresponds to an operating system indicated in the issue stack; generating, by the bot, a prompt for a generative Al model, the prompt comprising the issue stack; sending, by the analysis engine, the prompt to the generative Al model; receiving a response from the generative Al model; generating, by the bot, an analysis result based on the response from the generative Al model.
However, Sarkar teaches identifying, by the analysis engine, a bot to analyze the issue stack, wherein the identified bot comprises a first machine learning algorithm (View Sarkar Abstract ¶ 5, 20, 43; conversational AI agent; chatbot); generating, by the bot, a prompt for a generative Al model, the prompt comprising the issue stack (View Sarkar ¶ 41, 43; chatbot; diagnostic report); sending, by the analysis engine, the prompt to the generative Al model (View Sarkar ¶ 48; embedded chatbot provides diagnostic report as input to first machine learning model); receiving a response from the generative Al model (View Sarkar ¶ 41, 43; receive prediction output from first machine learning model); generating, by the bot, an analysis result based on the response from the generative Al model (View Sarkar ¶ 48, 49; periodical report output).
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Kulkarni with identifying, by the analysis engine, a bot to analyze the issue stack, wherein the identified bot comprises a first machine learning algorithm; generating, by the bot, a prompt for a generative Al model, the prompt comprising the issue stack; sending, by the analysis engine, the prompt to the generative Al model; receiving a response from the generative Al model; generating, by the bot, an analysis result based on the response from the generative Al model since it is known the art that a failure can be detected in a communication lane (View Sarkar ¶ 41, 43, 48). Such modification would have allowed a sideband port to be reconfigured.
Kulkarni and Sarkar do not explicitly teach the identified bot corresponds to an operating system indicated in the issue stack.
However, Nychis teaches the identified bot corresponds to an operating system indicated in the issue stack (View Nychis ¶ 139; a software robot may be configured to control the operating system and/or one or more application programs to perform any suitable task including, but not limited to, automatically generating a presentation (e.g., in MICROSOFT POWERPOINT) and/or a report (e.g., in MICROSOFT EXCEL) with information gathered from multiple sources).
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the combination of teachings with the identified bot corresponds to an operating system indicated in the issue stack since it is known the art that a software bot can be associated with an operating system (View Nychis ¶ 139). Such modification would have allowed a failure report to be generated for an operating system.
Claim 9 is the media corresponding to the method of Claim 1 and is therefore rejected under the same reasons set forth in the rejection Claim 1.
Claim 17 is the system corresponding to the method of Claim 1 and is therefore rejected under the same reasons set forth in the rejection Claim 1.
Claim 6, most of the limitations of this claim has been noted in the rejection of Claim 1. Nychis further teaches the operating system indicated in the issue stack is one of Windows, MacOS, iOS, Linux, UNIX or Android (View Nychis ¶ 141; operating systems that may be controlled by a software robot include, but are not limited to, the ANDROID operating system, the BSD operating system, the CHROME operating system, the IPhone operating system (IOS), the LINUX operating system, the Mac OS X operating system, the SOLARIS operating system, IBM AIX, and MICROSOFT WINDOWS).
Claim 14 is the media corresponding to the method of Claim 6 and is therefore rejected under the same reasons set forth in the rejection Claim 6.
Claim(s) 2, 3, 10, 11, 18 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kulkarni (US Patent Application 2023/0031997) in view of Sarkar (US Patent Application 2025/0147838) in view of Nychis (US Patent Application 2016/0259717) in view of Flohr (US Patent Application 2019/0235842) and further in view of Ogawa (US Patent Application 2020/0356608).
Claim 2, most of the limitations of this claim has been noted in the rejection of Claim 1. The combination of teachings does not explicitly teach generating the analysis result comprises: performing a search of a knowledge base for information associated with the issue stack; and performing, by the bot, an internet search.
However, Flohr teaches generating the analysis result comprises: performing a search of a knowledge base for information associated with the issue stack (View Flohr ¶ 8; fixed error database queried).
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the combination of teachings with generating the analysis result comprises: performing a search of a knowledge base for information associated with the issue stack since it is known the art that a knowledge base can be queried (View Flohr ¶ 8). Such modification would have allowed an error to be searched for in a database.
The combination of teachings does not explicitly teach performing, by the bot, an internet search.
However, Ogawa teaches performing, by the bot, an internet search (View Ogawa ¶ 158, 208; search bot).
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the combination of teachings with performing, by the bot, an internet search since it is known the art that an internet search can be performed (View Ogawa ¶ 158, 208). Such modification would have allowed an internet search can be performed by a bot.
Claim 10 is the media corresponding to the method of Claim 2 and is therefore rejected under the same reasons set forth in the rejection Claim 2.
Claim 18 is the system corresponding to the method of Claim 2 and is therefore rejected under the same reasons set forth in the rejection Claim 2.
Claim 3, most of the limitations of this claim has been noted in the rejection of Claim 2. Flohr further teaches the knowledge base comprises previously analyzed issue stacks and corresponding previous analysis results (View Flohr ¶ 8, 9; fixed error database; unique error report identifier).
Claim 11 is the media corresponding to the method of Claim 3 and is therefore rejected under the same reasons set forth in the rejection Claim 3.
Claim 19 is the system corresponding to the method of Claim 3 and is therefore rejected under the same reasons set forth in the rejection Claim 3.
Claim(s) 4, 12 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kulkarni (US Patent Application 2023/0031997) in view of Sarkar (US Patent Application 2025/0147838) in view of Nychis (US Patent Application 2016/0259717) in view of Flohr (US Patent Application 2019/0235842) in view of Ogawa (US Patent Application 2020/0356608) and further in view of Reid (US Patent Application 2016/0041894).
Claim 4, most of the limitations of this claim has been noted in the rejection of Claim 3. The combination of teachings does not explicitly teach the first machine learning algorithm is a neural network trained based on the corresponding previous analysis results, previously analyzed issue stacks, and manually analyzed issues.
However, Reid teaches the first machine learning algorithm is a neural network trained based on the corresponding previous analysis results, previously analyzed issue stacks, and manually analyzed issues (View Reid ¶ 45, 130; machine learning, trace event information, customer report of problems, telemetry data).
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the combination of teachings with the first machine learning algorithm is a neural network trained based on the corresponding previous analysis results, previously analyzed issue stacks, and manually analyzed issues since it is known the art that a machine learning can receive problem data (View Reid ¶ 45, 130). Such modification would have allowed results to be analyzed.
Claim 12 is the media corresponding to the method of Claim 4 and is therefore rejected under the same reasons set forth in the rejection Claim 4.
Claim 20 is the system corresponding to the method of Claim 4 and is therefore rejected under the same reasons set forth in the rejection Claim 4.
Claim(s) 5 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kulkarni (US Patent Application 2023/0031997) in view of Sarkar (US Patent Application 2025/0147838) in view of Nychis (US Patent Application 2016/0259717) and further in view of Kalamkar (US Patent Application 2025/0335774).
Claim 5, most of the limitations of this claim has been noted in the rejection of Claim 1. The combination of teachings does not explicitly teach generating the analysis result comprises generating a text summary explaining a cause of the software issue.
However, Kalamkar teaches generating the analysis result comprises generating a text summary explaining a cause of the software issue (View Kalamkar ¶ 5; output a natural language summary of an operational event and potential root cause).
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the combination of teachings with generating the analysis result comprises generating a text summary explaining a cause of the software issue since it is known the art that a text summary of problem data can be generated (View Kalamkar ¶ 5). Such modification would have allowed results to be in a text summary.
Claim 13 is the media corresponding to the method of Claim 5 and is therefore rejected under the same reasons set forth in the rejection Claim 5.
Claim(s) 7 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kulkarni (US Patent Application 2023/0031997) in view of Sarkar (US Patent Application 2025/0147838) in view of Nychis (US Patent Application 2016/0259717) and further in view of Sloane (US Patent Application 2022/0066860).
Claim 7, most of the limitations of this claim has been noted in the rejection of Claim 1. The combination of teachings does not explicitly teach sending the request for the issue stack is triggered in response to a selection of the software issue in the user interface associated with the developer portal system.
However, Sloane teaches sending the request for the issue stack is triggered in response to a selection of the software issue in the user interface associated with the developer portal system (View Sloane ¶ 42; user selects error message returned by non-functioning application, user screen).
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the combination of teachings with sending the request for the issue stack is triggered in response to a selection of the software issue in the user interface associated with the developer portal system since it is known the art that a user can select an error message (View Sloane ¶ 42). Such modification would have allowed an error message to be selected by a user.
Claim 15 is the media corresponding to the method of Claim 7 and is therefore rejected under the same reasons set forth in the rejection Claim 7.
Claim(s) 8 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kulkarni (US Patent Application 2023/0031997) in view of Sarkar (US Patent Application 2025/0147838) in view of Nychis (US Patent Application 2016/0259717) and further in view of Kumar (US Patent Application 2019/0303258).
Claim 8, most of the limitations of this claim has been noted in the rejection of Claim 1. Kulkarni further teaches sending the updated analysis result to the developer portal system for presentation (View Kulkarni ¶ 67; user interface).
The combination of teachings does not explicitly teach receiving a regeneration command at the analysis engine and in response: re-sending the prompt to the generative AI model; generating an updated analysis result.
However, Kumar teaches receiving a regeneration command at the analysis engine (View Kumar ¶ 60; dynamic feedback loop) and in response: re-sending the prompt to the generative AI model (View Kumar ¶ 91; provide potential problem back to the problem analyzer); generating an updated analysis result (View Kumar ¶ 91; update machine learning algorithm based on reported result).
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the combination of teachings with receiving a regeneration command at the analysis engine and in response: re-sending the prompt to the generative AI model; generating an updated analysis result since it is known the art that updated results can be generated (View Kumar ¶ 91). Such modification would have allowed results to be updated by regenerating an analysis command.
Claim 16 is the media corresponding to the method of Claim 8 and is therefore rejected under the same reasons set forth in the rejection Claim 8.
Response to Arguments
Applicant's arguments filed July 13, 2026 have been fully considered but they are not persuasive.
On pages 8-9, Applicant argues that Sarkar does not teach “identifying, by the analysis engine, a bot to analyze the issue stack, wherein the identified bot comprises a first machine learning algorithm”, in Claims 1, 9 and 17.
Examiner respectfully disagrees with Applicant. Sarkar teaches obtaining a diagnostic report for the electronic device, establishing a communication channel with a remote chatbot, sending, to the remote chatbot (identifying a bot) over the communication channel, the diagnostic report and receiving, from the remote chatbot over the communication channel, a set of instructions for resolving at least one potential fault of the electronic device (analyze issue stack) in Paragraph 5. Sarkar also teaches a periodical report indicative of an overall health of the electronic device is received from the remote chatbot over the communication channel. The overall health is predicted by a machine learning model utilized by the remote chatbot, in Paragraph 20.
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
Jang et al. (U.S. Patent Application 2011/0153073); teaches assigning robot software components having a same priority and cycle to a component executor; executing, on the component executor, the robot software components by using a thread assigned to the components from an operating system; notifying an executor monitor of the execution result; and determining, by the executor monitor, whether or not a failure has occurred during the execution of the robot software components, and generating a new component executor by an executor manager if it is determined that there is a failure during the execution of robot software components.
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.
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/SARAI E BUTLER/Primary Examiner, Art Unit 2114
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