DETAILED ACTION
This Office Action is in response to Application filed on 11 June 2026.
Claims 1-21 are pending. The claims have been considered and examined.
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 .
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.
Claim(s) s 1-5 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mahamuni et al., U.S. Patent App. Pub. 2023/0018199, hereinafter referred to as “Mahamuni” in view of Hickie et al., U.S Patent App. Pub. 2024/0394132, hereinafter referred to as “Hickie”.
Referring to claim 1, Mahamuni discloses a computer system as part of a cloud computing environment (See Mahamuni, paragraphs 0003 and 0064). - A system associated with a cloud computing environment, comprising:
Mahamuni discloses a message module that intercept messages (See Mahamuni, paragraph 0080). - a message interceptor, including:
Mahamuni discloses a computing system with a processor (See Mahamuni, paragraph 0026). - a computer processor, and
Mahamuni discloses the computing system including memory the computing system including the memory module that can intercept messages (See Mahamuni, Fig. 1 and 2; paragraphs 0026 and 0043). - a computer memory storing instructions that when executed by the computer processor cause the interceptor to:
Mahamuni discloses intercepting messages shared between programs executed in the computing environment (See Mahamuni, paragraph 0080). - intercept details of messages associated with a process executed by an enterprise in the cloud computing environment,
Mahamuni discloses matching messages to determining process failures (See Mahamuni, paragraph 0003). - determine an occurrence of a process failure, and
Mahamuni discloses a knowledge base with collection of the unsuccessful batch jobs including the messaging and updating the knowledge base with additional error information (See Mahamuni, paragraphs 0085 and 0114). - responsive to said determination, automatically record details of the intercepted messages that may be relevant to the process failure;
Mahamuni discloses a knowledge base that includes a history of the associated messages of the unsuccessful jobs (See Mahamuni, paragraph 0085). - a collected message data store, coupled to the message interceptor, that contains electronic records representing details of the intercepted messages that may be relevant to the process failure; and
Mahamuni discloses displaying a failure to a system admin (See Mahamuni, paragraph 0086). Mahamuni disclose the display data to a user is done using a graphical user interface (See Mahamuni, paragraph 0030). Mahamuni discloses a distributed network computing environment (See Mahamuni, paragraph 0025). - a message recorder, coupled to the collected message data store, to access information from the collected message data store and arrange for information about the recorded messages to be transmitted, via a distributed communication network, to a support team via a Graphical User Interface ("GUI") display.
Mahamuni does not disclose “the recorded details of the intercepted messages comprising at least one of: an integration scenario identifier, exception trace logs, payloads, memory usage, or computing resource utilization;“.
Hickie discloses intercepting messages, and saving information about the messages to a database (See Hickie, paragraph 0004). Hickie discloses the information about the message includes a payload (See Hickie, paragraph 0005).
It would have been obvious to one of ordinary skill in the art at the time of filing of the invention to combine the computing system with message intercepting of Mahamuni with message intercepting and saving payload information of Hickie. This would have been obvious to do because save the message if there is a failure to prevent message loss (See Hickie, paragraph 0047).
Referring to claim 2, Mahamuni and Hickie disclose all the limitations (See rejection of claim 1) including Mahamuni discloses resource sharing a file system (See Mahamuni, paragraphs 0046 and 0073). - The system of claim 1, wherein the collected message data store is associated with at least one of (i) a distributed event streaming platform, and (ii) a shared file system.
Referring to claim 3, Mahamuni and Hickie disclose all the limitations (See rejection of claim 1) including Mahamuni discloses the system is a business logic for batch applications (See Mahamuni, paragraph 0072). - The system of claim 1, wherein the process is a business application integration scenario and the message interceptor is part of a message processing component of the business application integration scenario.
Referring to claim 4, Mahamuni and Hickie disclose all the limitations (See rejection of claim 1) including Mahamuni discloses the messages including time stamps (See Mahamuni, paragraph 0080). - The system of claim 1, wherein the details of the messages associated with the process include at least one of: (i) an integration scenario identifier, (ii) a message time, (iii) exception trace logs, (iv) payloads, (v) memory usage, (vi) computing resource utilization, and (vii) Input Output ("IO") data.
Referring to claim 5, Mahamuni and Hickie disclose all the limitations (See rejection of claim 1) including Mahamuni discloses the jobs can be serial and collecting the information from the messages of the jobs (See Mahamuni, paragraphs 0076 and 0080). - The system of claim 1, wherein the message recorder is further to de-serialize information from the collected message data store.
Referring to claim 16, Mahamuni discloses a computer program product, that includes a computer-readable storage medium having instructions thereon, as part of a cloud computing environment (See Mahamuni, paragraphs 0003, 0031, and 0064). - One or more non-transitory computer-readable media storing computer- executable instructions that, when executed by a computing system, cause the computing system to perform operations for a cloud computing environment, comprising:
Mahamuni discloses a message module that intercept messages (See Mahamuni, Mahamuni, paragraph 0080). Mahamuni discloses a computing system with a processor (See Mahamuni, Mahamuni, paragraph 0026). Mahamuni discloses intercepting messages shared between programs executed in the computing environment (See Mahamuni, paragraph 0080). - intercepting, by a computer processor of a message interceptor, details of messages associated with a process executed by an enterprise in the cloud computing environment;
Mahamuni discloses matching messages to determining process failures (See Mahamuni, paragraph 0003). - determining, by the computer processor of the message interceptor, an occurrence of a process failure;
Mahamuni discloses a knowledge base with collection of the unsuccessful batch jobs including the messaging and updating the knowledge base with additional error information (See Mahamuni, paragraphs 0085 and 0114). - responsive to said determination, automatically recording details of the intercepted messages that may be relevant to the process failure in a collected message data store that contains electronic records representing details of the intercepted messages that may be relevant to the process failure;
Mahamuni discloses providing the knowledge base to an artificial intelligence (See Mahamuni, Mahamuni, paragraph 0083). - accessing, by a message recorder, information from the collected message data store; and
Mahamuni discloses displaying a failure to a system admin (See Mahamuni, paragraph 0086). Mahamuni disclose the display data to a user is done using a graphical user interface (See Mahamuni, paragraph 0030). Mahamuni discloses a distributed network computing environment (See Mahamuni, paragraph 0025). - arranging, by the message recorder, for information about the recorded messages to be transmitted, via a distributed communication network, to a support team via a Graphical User Interface ("GUI") display.
Mahamuni does not disclose “the recorded details of the intercepted messages comprising at least one of: an integration scenario identifier, exception trace logs, payloads, memory usage, or computing resource utilization;“.
Hickie discloses intercepting messages, and saving information about the messages to a database (See Hickie, paragraph 0004). Hickie discloses the information about the message includes a payload (See Hickie, paragraph 0005).
It would have been obvious to one of ordinary skill in the art at the time of filing of the invention to combine the computing system with message intercepting of Mahamuni with message intercepting and saving payload information of Hickie. This would have been obvious to do because save the message if there is a failure to prevent message loss (See Hickie, paragraph 0047).
Claim(s) 6-15 and 17-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mahamuni and Hickie and further in view of Vasseur et al., U.S. Patent App. Pub. 2025/0293957, hereinafter referred to as “Vasseur”.
Referring to claim 6, Mahamuni and Hickie discloses all the limitations (See rejection of claim 1) except for The system of claim 1, wherein the message recorder provides at least some of the information about the recorded messages to a generative Artificial Intelligence ("AI") Large Language Model ("LLM").
However, Mahamuni does disclose the use of an artificial intelligence for analyzing and predicting job failures (See Mahamuni, paragraph 0022).
Vasseur discloses a generative artificial intelligence model (See Vasseur, paragraph 0038). Vasseur disclose a troubleshooting agent that uses a large language model to generate an output (See Vasseur, paragraph 0141). Vasseur discloses the LLM is used to analyze the information (See Vasseur, paragraph 0094).
It would have been obvious to one of ordinary skill in the art at the time of filing of the invention to combine the analyzing of intercepted messages with an artificial intelligence of Mahamuni and Hickie with the LLM of Vasseur. This would have been obvious to do because LLMs can be used to identify, troubleshoot and potentially mitigate issues (See Vasseur, paragraph 0003).
Referring to claim 7, Mahamuni, Hickie, and Vasseur disclose all the limitations (See rejection of claim 6) including Vasseur discloses LLM including a critic engine that assess code, thus code provided in a prompt to the LLM (See Vasseur, Fig. 5, and 7; paragraphs 0065 and 0117). - The system of claim 6, wherein a LLM prompt includes at least some code base of the process executed by the enterprise.
Referring to claim 8, Mahamuni, Hickie, and Vasseur disclose all the limitations (See rejection of claim 7) including Vasseur discloses a troubleshooting agent that uses the LLM to generate code that includes identifying errors of the code (See Vasseur, paragraphs 0117). - The system of claim 7, wherein a response received from the LLM includes possible troubleshooting information to fix the code base.
Referring to claim 9, Mahamuni, Hickie, and Vasseur disclose all the limitations (See rejection of claim 8) including Mahamuni discloses intercepting messages of batch jobs (See paragraph 0080). Mahamuni discloses the jobs are for executed for a business (See Mahamuni, paragraph 0017). - The system of claim 8, wherein at least one of the message interceptor and the message recorder comprise a component that is plugged into cloud applications for design, development, and/or execution of business processes.
Referring to claim 10, Mahamuni, Hickie, and Vasseur disclose all the limitations (See rejection of claim 8) including Vasseur discloses training the learning model (See Vasseur, paragraph 0036). Mahamuni discloses real-time analysis of the messages (See Mahamuni, paragraph 0098). Mahamuni disclose providing useful remediation steps (See Mahamuni, paragraph 0020). - The system of claim 8, wherein at least one of the message interceptor and the message recorder comprise a reuseable component that communicates with pre-trained LLMs to generate a set of solution proposals based on real-time incident data.
Referring to claim 11, Mahamuni discloses a computer-implemented method as part of a cloud computing environment (See Mahamuni, paragraphs 0003 and 0064). - A computer-implemented method associated with a cloud computing environment, comprising:
Mahamuni discloses a message module that intercept messages (See Mahamuni, paragraph 0080). Mahamuni discloses a computing system with a processor (See Mahamuni, paragraph 0026). Mahamuni discloses intercepting messages shared between programs executed in the computing environment (See Mahamuni, paragraph 0080). - intercepting, by a computer processor of a message interceptor, details of messages associated with a process executed by an enterprise in the cloud computing environment;
Mahamuni discloses matching messages to determining process failures (See Mahamuni, paragraph 0003). - determining, by the computer processor of the message interceptor, an occurrence of a process failure;
Mahamuni discloses a knowledge base with collection of the unsuccessful batch jobs including the messaging and updating the knowledge base with additional error information (See Mahamuni, paragraphs 0085 and 0114). Mahamuni discloses a knowledge base that includes a history of the associated messages of the unsuccessful jobs (See Mahamuni, paragraph 0085). - responsive to said determination, automatically recording details of the intercepted messages that may be relevant to the process failure in a collected message data store that contains electronic records representing details of the intercepted messages that may be relevant to the process failure;
Mahamuni discloses providing the knowledge base to an artificial intelligence (See Mahamuni, paragraph 0083). - accessing, by a message recorder, information from the collected message data store; and
Mahamuni does not disclose “the recorded details of the intercepted messages comprising at least one of: an integration scenario identifier, exception trace logs, payloads, memory usage, or computing resource utilization;“ and “providing, by the message recorder, at least some of the information about the recorded messages to a generative Artificial Intelligence ("AI") Large Language Model ("LLM"), wherein a LLM prompt includes at least some code base of the process executed by the enterprise”.
However, Mahamuni does disclose the use of an artificial intelligence for analyzing and predicting job failures (See Mahamuni, paragraph 0022).
Hickie discloses intercepting messages, and saving information about the messages to a database (See Hickie, paragraph 0004). Hickie discloses the information about the message includes a payload (See Hickie, paragraph 0005).
It would have been obvious to one of ordinary skill in the art at the time of filing of the invention to combine the computing system with message intercepting of Mahamuni with message intercepting and saving payload information of Hickie. This would have been obvious to do because save the message if there is a failure to prevent message loss (See Hickie, paragraph 0047).
Vasseur discloses a generative artificial intelligence model (See Vasseur, paragraph 0038). Vasseur disclose a troubleshooting agent that uses a large language model to generate an output (See Vasseur, paragraph 0141). Vasseur discloses the LLM is used to analyze the information (See Vasseur, paragraph 0094). Vasseur discloses LLM including a critic engine that assess code, thus code provided in a prompt to the LLM (See Vasseur, Fig. 5, and 7; paragraphs 0065 and 0117).
It would have been obvious to one of ordinary skill in the art at the time of filing of the invention to combine the analyzing of intercepted messages with an artificial intelligence of Mahamuni with the LLM of Vasseur. This would have been obvious to do because LLMs can be used to identify, troubleshoot and potentially mitigate issues (See Vasseur, paragraph 0003).
Referring to claim 12, Mahamuni, Hickie, and Vasseur disclose all the limitations (See rejection of claim 11) including Mahamuni discloses resource sharing a file system (See paragraphs 0046 and 0073). - The method of claim 11, wherein the collected message data store is associated with at least one of: (i) a distributed event streaming platform, and (ii) a shared file system.
Referring to claim 13, Mahamuni, Hickie, and Vasseur disclose all the limitations (See rejection of claim 11) including Mahamuni discloses the system is a business logic for batch applications (See paragraph 0072). - The method of claim 11, wherein the process is a business application integration scenario and the message interceptor is part of a message processing component of the business application integration scenario.
Referring to claim 14, Mahamuni, Hickie, and Vasseur disclose all the limitations (See rejection of claim 11) including Mahamuni discloses the messages including time stamps (See paragraph 0080). - The method of claim 11, wherein the details of the messages associated with the process include at least one of: (i) an integration scenario identifier, (ii) a message time, (iii) exception trace logs, (iv) payloads, (v) memory usage, (vi) computing resource utilization, and (vii) Input Output ("IO") data.
Referring to claim 15, Mahamuni, Hickie, and Vasseur disclose all the limitations (See rejection of claim 11) including Mahamuni discloses the jobs can be serial and collecting the information from the messages of the jobs (See paragraphs 0076 and 0080). - The method of claim 11, wherein the message recorder is further to de-serialize information from the collected message data store.
Referring to claim 17, Mahamuni and Hickie discloses all the limitations (See rejection of claim 16) except for The media of claim 16, wherein the message recorder provides at least some of the information about the recorded messages to a generative Artificial Intelligence ("AI") Large Language Model ("LLM").
However, Mahamuni does disclose the use of an artificial intelligence for analyzing and predicting job failures (See Mahamuni, paragraph 0022).
Vasseur discloses a generative artificial intelligence model (See Vasseur, paragraph 0038). Vasseur disclose a troubleshooting agent that uses a large language model to generate an output (See Vasseur, paragraph 0141). Vasseur discloses the LLM is used to analyze the information (See Vasseur, paragraph 0094).
It would have been obvious to one of ordinary skill in the art at the time of filing of the invention to combine the analyzing of intercepted messages with an artificial intelligence of Mahamuni and Hickie with the LLM of Vasseur. This would have been obvious to do because LLMs can be used to identify, troubleshoot and potentially mitigate issues (See Vasseur, paragraph 0003).
Referring to claim 18, Mahamuni, Hickie, and Vasseur disclose all the limitations (See rejection of claim 17) including Vasseur discloses LLM including a critic engine that assess code, thus code provided in a prompt to the LLM (See Vasseur, Fig. 5, and 7; paragraphs 0065 and 0117). -The media of claim 17, wherein a LLM prompt includes at least some code base of the process executed by the enterprise.
Referring to claim 19, Mahamuni, Hickie, and Vasseur disclose all the limitations (See rejection of claim 18) including Vasseur discloses a troubleshooting agent that uses the LLM to generate code that includes identifying errors of the code (See Vasseur, paragraphs 0117). - The media of claim 18, wherein a response received from the LLM includes possible troubleshooting information to fix the code base.
Referring to claim 20, Mahamuni, Hickie, and Vasseur disclose all the limitations (See rejection of claim 19) including Mahamuni discloses intercepting messages of batch jobs (See paragraph 0080). Mahamuni discloses the jobs are for executed for a business (See Mahamuni, paragraph 0017). - The media of claim 19, wherein at least one of the message interceptor and the message recorder comprise a component that is plugged into cloud applications for design, development, and/or execution of business processes.
Referring to claim 21, Mahamuni, Hickie, and Vasseur disclose all the limitations (See rejection of claim 20) including Vasseur discloses training the learning model (See Vasseur, paragraph 0036). Mahamuni discloses real-time analysis of the messages (See Mahamuni, paragraph 0098). Mahamuni disclose providing useful remediation steps (See Mahamuni, paragraph 0020). - The media of claim 20, wherein at least one of the message interceptor and the message recorder comprise a reuseable component that communicates with pre-trained LLMs to generate a set of solution proposals based on real-time incident data.
Response to Arguments
Applicant’s arguments, see pages 7-10 of remarks, filed 11 June 2026, with respect to the rejection(s) of claim(s) 1-21 under 35 USC 102 and 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of new found prior art, see above rejections.
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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/JOSEPH D MANOSKEY/Primary Examiner, Art Unit 2113 August 27, 2026
