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 .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 120 as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 18/376,251, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application.
Claims 7 and 14 each recite “wherein the operations further comprise using a second machine learning model trained by a second machine learning algorithm to determine the external Al tool from a plurality of machine learning models.” There is no support in the prior filed application for such a recitation. The disclosure regarding a “second machine learning model trained by a second machine learning algorithm” is found in specification paragraphs 0028-0036, with regard to the model determination module. The specification discloses only that the second machine learning model is trained and used to determine whether an internal AI tool or an external AI tool should be used. The specification is silent regarding the use of a second machine learning model for selection of an external AI tool from a plurality of machine learning models.
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 7 and 14 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 7 and 14 each recite “wherein the operations further comprise using a second machine learning model trained by a second machine learning algorithm to determine the external Al tool from a plurality of machine learning models.” There is no support in the specification for such a recitation. The disclosure regarding a “second machine learning model trained by a second machine learning algorithm” is found in specification paragraphs 0029-0037, with regard to the model determination module. The specification discloses only that the second machine learning model is trained and used to determine whether an internal AI tool or an external AI tool should be used. The specification is silent regarding the use of a second machine learning model for selection of an external AI tool from a plurality of machine learning models.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The following is an analysis of the claims regarding subject matter eligibility in accordance with the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG):
Claim Interpretation for claim 1
Under the broadest reasonable interpretation, the terms of the claim are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP 2111.
The claim recites receiving, from a pipeline, an identification of an event that occurred during operation of the pipeline on first software code created by a first user. The claim places no limits on how the identification is received.
The claim recites using an event analysis engine on the identification of the event to identify a root cause of the event. The claim does not place any limitations on how the identifying is accomplished and the plain meaning of identifying encompasses mental observations or evaluations. The claim does not place any limits on the event analysis engine, and the specification makes clear that the engine can be a generic machine learning model trained by any of a wide array of algorithms. The event analysis engine, then, is merely a generic invocation of a generic computer tool.
The claim recites in response to a determination that an external Al tool should be used, passing the root cause to the external Al tool. The claim does not place any limitations on how the determination is made and the plain meaning of determination encompasses mental observations or evaluations. The claim further places no limits on how the root cause is passed to an external AI tool.
The claim recites the external Al tool being a Generative Artificial Intelligence (GAI) model that generates one or more actions. The claim places no limits on the GAI model, and the specification makes clear the GAI model could take the form of numerous different types of models. The external AI tool is recited at a high level of generality.
Finally, the claim recites causing the one or more actions to be displayed to the first user. The claim does not impose any limits on how the actions are displayed and requires nothing more than a generic display to accomplish the displaying step.
The claim recites that each of the steps is performed by a generically recited hardware processor executing instructions stored on a computer-readable medium.
Based on the plain meaning of the words in the claim, the broadest reasonable interpretation of the claim is a generic hardware processor executing instructions that cause the processor to receive an event indication from a pipeline, identify a root cause of the event, make a determination and conditional transmit the root cause to an external tool, and display the output from that tool
Subject Matter Eligibility Analysis
Step 1: Do the Claims Specify a Statutory Category?
Claims 1-7 recite systems, claims 8-14 recite methods, and claims 15-20 recite non-transitory machine-readable media having instructions stored therein, therefore satisfying Step 1 of the analysis.
Step 2 Analysis for Claims 1-7
Step 2A – Prong 1: Is a Judicial Exception Recited?
This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
As discussed in the claim interpretation section, the broadest reasonable interpretation of the steps reciting identifying and making a determination fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and/or opinion. See MPEP 2106.04(a)(2)(III).
As such, the limitations identified above, as currently written, describe a process which, under its broadest reasonable interpretation, covers performance of the limitations in the human mind but for the recitation of generic computer components (i.e., use of a processor and memory). That is, nothing in the claim elements preclude the steps of making an identification and making a determination from practically being performed in the mind (or using pen and paper).
If a claim limitation, under its broadest reasonable interpretation, covers the practical performance of the limitation in the human mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. See the 2019 Revised Patent Subject Matter Eligibility Guidance. Accordingly, the claim recites an abstract idea.
Regarding claim 6:
The claim recites the use of manual input from a user. This is a recitation of a mental judgment, evaluation, observation or opinion.
Claims 2-5 and 7 incorporate the abstract idea of the independent claim by virtue of their dependency. The limitations in these dependent claims are directed to the identified abstract idea and, under their broadest reasonable interpretation, cover performance of the limitations in the human mind but for the recitation of generic computer components, thereby falling within the “Mental Processes” grouping of abstract ideas.
Accordingly claims 1-7 recite abstract ideas.
Step 2A – Prong 2: Is the Judicial Exception Integrated into a Practical Application?
This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the judicial exception or whether the claim is “directed to” the judicial exception. The evaluation is performed by identifying whether there are additional elements in the claim beyond the judicial exception and the evaluating those elements individually and as a whole, in combination with all limitations of the claim, to determine whether the claim integrates the judicial exception into a practical application.
Claim 1 recites additional limitations of “at least one hardware processor,” “a computer-readable medium storing instructions,” “receiving, from a pipeline, an identification of an event that occurred during operation of the pipeline on first software code created by a first user,” “an event analysis engine,” “passing the root cause to the external Al tool,” “the external Al tool being a Generative Artificial Intelligence (GAI) model that generates one or more actions,” and “causing the one or more actions to be displayed to the first user.”
The recitations of “at least one hardware processor,” “a computer-readable medium storing instructions,” “an event analysis engine,” and “the external Al tool being a Generative Artificial Intelligence (GAI) model that generates one or more actions,” are all recited at a high level of generality and represent no more than generic computing components, as explained above in the claim interpretation section. There is no indication that the combination of elements solves a technological problem other than merely taking advantage of the inherent advantages of using existing computer technology in its ordinary, off-the-shelf capacity to apply the judicial exception. Simply implementing the abstract ideas on a general purpose processor or other generic computer component is not a practical application of the abstract ideas. The additional elements are no more than mere instructions to apply the judicial exception on a computer (see MPEP 2106.05(f)). These elements can also be viewed as nothing more than an attempt to generally link the judicial exception to the technological environment of a computer (see MPEP 2106.05(h)).
The recitation of “receiving, from a pipeline, an identification of an event that occurred during operation of the pipeline on first software code created by a first user” and “causing the one or more actions to be displayed to the first user” amount to mere data gathering and/or output that are necessary for all uses of the abstract idea. There is no limit place on the receiving of an identification and the step merely recites a passive action of receiving data. As explained above, the claim does not impose any limits on how the actions are displayed and requires nothing more than a generic display to accomplish the displaying step. These additional elements are insignificant extra-solution activity. See MPEP 2106.05(g).
The recitation of “passing the root cause to the external Al tool” amounts to no more than transmitting data over a network, recited at a high level of generality and is insignificant extra-solution activity. See MPEP 2106.05(d)(II) and MPEP 2106.05(g).
Regarding claim 2:
The claim merely recites additional limitations regarding the particular type of data to be transmitted. These limitations for determining the particular type of data to be transmitted amount to insignificant extra-solution activity as they simply direct one as to which type of data to transmit. MPEP 2106.05(g).
Regarding claim 3:
The claim merely recites additional limitations regarding the particular type of data to be transmitted. These limitations for determining the particular type of data to be transmitted amount to insignificant extra-solution activity as they simply direct one as to which type of data to transmit. MPEP 2106.05(g).
Regarding claim 4:
The claim recites that the event analysis engine is a first machine learning model trained by a first machine learning algorithm. As explained in the claim interpretation section, the specification makes clear that the engine can be a generic machine learning model trained by any of a wide array of algorithms. The event analysis engine, then, is merely a generic invocation of a generic computer tool. The additional elements thus represent no more than mere instructions to apply the judicial exception on a computer. See MPEP 2106.05(f).
Regarding claim 5:
The claim recites types of data on which to train the machine learning model of claim 4. As explained above, the specification makes clear that the engine can be a generic machine learning model trained by any of a wide array of algorithms, thus placing no limits on training of the model and making clear that the model is no more than a generic component claimed at a high level of generality. Recitation of particular types of data to be used in the training provides no more than an indication of particular types of data to be manipulated and is no more than insignificant extra-solution activity.
Claim 6 does not recite additional elements.
Regarding claim 7:
The claim recites a second machine learning model trained by a second machine learning algorithm to determine the external Al tool from a plurality of machine learning models. As with the recitation of the machine learning model above, the specification makes clear that the model is a generic machine learning model trained by any of a wide array of algorithms. The second model, then, then, is merely a generic invocation of a generic computer tool. The additional elements thus represent no more than mere instructions to apply the judicial exception on a computer. See MPEP 2106.05(f).
Accordingly, the identified additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B: Do the Claims Provide an Inventive Concept?
When evaluating whether the claims provide an inventive concept, the presence of any additional elements in the claims need to be considered to determine whether they add “significantly more” than the judicial exception.
As explained with respect to Step 2A, Prong 2, claim 1 recites additional limitations of “at least one hardware processor,” “a computer-readable medium storing instructions,” “receiving, from a pipeline, an identification of an event that occurred during operation of the pipeline on first software code created by a first user,” “an event analysis engine,” “passing the root cause to the external Al tool,” “the external Al tool being a Generative Artificial Intelligence (GAI) model that generates one or more actions,” and “causing the one or more actions to be displayed to the first user.”
The recitations of “at least one hardware processor,” “a computer-readable medium storing instructions,” “an event analysis engine,” and “the external Al tool being a Generative Artificial Intelligence (GAI) model that generates one or more actions,” are all recited at a high level of generality and represent no more than generic computing components, as explained above in the claim interpretation section. There is no indication that the combination of elements solves a technological problem other than merely taking advantage of the inherent advantages of using existing computer technology in its ordinary, off-the-shelf capacity to apply the judicial exception. Simply implementing the abstract ideas on a general purpose processor or other generic computer component is not a practical application of the abstract ideas. The additional elements are no more than mere instructions to apply the judicial exception on a computer (see MPEP 2106.05(f)). These elements can also be viewed as nothing more than an attempt to generally link the judicial exception to the technological environment of a computer (see MPEP 2106.05(h)).
The recitation of “receiving, from a pipeline, an identification of an event that occurred during operation of the pipeline on first software code created by a first user” and “causing the one or more actions to be displayed to the first user” amount to mere data gathering and/or output that are necessary for all uses of the abstract idea. There is no limit place on the receiving of an identification and the step merely recites a passive action of receiving data. As explained above, the claim does not impose any limits on how the actions are displayed and requires nothing more than a generic display to accomplish the displaying step. These additional elements are insignificant extra-solution activity. See MPEP 2106.05(g).
The recitation of “passing the root cause to the external Al tool” amounts to no more than transmitting data over a network, recited at a high level of generality and is insignificant extra-solution activity. Additionally, the courts have recognized that recitations of transmitting data over a network, recited at a high level of generality, is well-understood, routine, conventional activity. See MPEP 2106.05(d)(II) and MPEP 2106.05(g).
Regarding claim 2:
The claim merely recites additional limitations regarding the particular type of data to be transmitted. These limitations for determining the particular type of data to be transmitted amount to insignificant extra-solution activity as they simply direct one as to which type of data to transmit. MPEP 2106.05(g).
Regarding claim 3:
The claim merely recites additional limitations regarding the particular type of data to be transmitted. These limitations for determining the particular type of data to be transmitted amount to insignificant extra-solution activity as they simply direct one as to which type of data to transmit. MPEP 2106.05(g).
Regarding claim 4:
The claim recites that the event analysis engine is a first machine learning model trained by a first machine learning algorithm. As explained in the claim interpretation section, the specification makes clear that the engine can be a generic machine learning model trained by any of a wide array of algorithms. The event analysis engine, then, is merely a generic invocation of a generic computer tool. The additional elements thus represent no more than mere instructions to apply the judicial exception on a computer. See MPEP 2106.05(f).
Regarding claim 5:
The claim recites types of data on which to train the machine learning model of claim 4. As explained above, the specification makes clear that the engine can be a generic machine learning model trained by any of a wide array of algorithms, thus placing no limits on training of the model and making clear that the model is no more than a generic component claimed at a high level of generality. Recitation of particular types of data to be used in the training provides no more than an indication of particular types of data to be manipulated and is no more than insignificant extra-solution activity.
Claim 6 does not recite additional elements.
Regarding claim 7:
The claim recites a second machine learning model trained by a second machine learning algorithm to determine the external Al tool from a plurality of machine learning models. As with the recitation of the machine learning model above, the specification makes clear that the model is a generic machine learning model trained by any of a wide array of algorithms. The second model, then, then, is merely a generic invocation of a generic computer tool. The additional elements thus represent no more than mere instructions to apply the judicial exception on a computer. See MPEP 2106.05(f).
Accordingly, these limitations recite no additional elements that, taken individually or in combination with all elements of the claim as a whole, would amount to significantly more than the abstract idea defined in the claim.
Conclusion: In light of the above, the limitations in claims 1-7 recite and are directed to an abstract idea and recite no additional elements that would amount to significantly more than the identified abstract idea. Claims 1-7 are therefore not patent eligible.
Step 2 Analysis for Claims 8-20
The claims recite limitations which are similar to the limitations in claims 1-7, and is similarly directed to the same abstract idea as identified above. The Step 2 analysis for the limitations in claims 8-20 is similar to the analysis for claims 1-7.
Accordingly, claims 8-20 are not patent eligible under 35 U.S.C. § 101 for the same reasons as claims 1-7.
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.
Claims 1, 2, 4, 8, 9, 11, 15, 16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Teja et al. (2024/0345904) in view of Ni et al. (2024/0220350).
Regarding claims 1 and 15:
Teja teaches:
A system comprising:
at least one hardware processor [par 46-48];
a computer-readable medium storing instructions that [par 46-48], when executed by the at least one hardware processor, cause the at least one hardware processor to perform operations comprising:
receiving, from a pipeline, an identification of an event that occurred during operation of the pipeline on first software code created by a first user [par 19, 22, 69 – getting log records from a failed pipeline job in a software deployment (CI/CD) pipeline]; and
using an event analysis engine on the identification of the event to identify a root cause of the event [par 59-61, 69 – uses CI/CD pipeline engine to parse log records and perform natural language processing to identify the keywords and error info].
Teja does not explicitly teach:
in response to a determination that an external Al tool should be used, passing the root cause to the external Al tool,
the external Al tool being a Generative Artificial Intelligence (GAI) model that generates one or more actions; and
causing the one or more actions to be displayed to the first user.
Teja does, however, teach that if the classified error does not belong to a class that is known and has an associated error resolution script, the error is notified to DevOps and is handled manually.
Ni teaches:
in response to a determination that an external Al tool should be used, passing the root cause to the external Al tool [par 28, 32 – when a fault cannot be solved locally with a local AI model, the error and problem description are passed to an external AI tool],
the external Al tool being a Generative Artificial Intelligence (GAI) model that generates one or more actions [par 24 – deep learning model, for example BERT. The specification does not define the term action in the context of the GAI model, thus the plain meaning of the term is considered to be any output provided by the GAI model]; and
causing the one or more actions to be displayed to the first user [par 24, 28, 32 – responding to user’s problem with a BERT model would necessarily include output to the user].
It would have been obvious to one of ordinary skill in the art prior to the effective filing date to combine the external AI tool in response to in inability to solve a local problem as disclosed by Ni with the error escalation based on error class of Teja.
One of ordinary skill in the art prior to the effective filing date to make the combination because Ni teaches that use of an external, or cloud model, allows for solving more complex problems than can be solved locally while still providing the final use effect of solving a problem without necessitating manual intervention, which can handle problems with fast response, low cost, and improved efficiency [par 3, 18, 19, 24].
Regarding claims 2 and 16:
The combination teaches:
The system of claim 1, wherein the passing further includes contextual information regarding the event to the GAI model [Teja par 59, Ni par 32].
Regarding claims 4 and 18:
The combination teaches:
The system of claim 1, wherein the event analysis engine is a first machine learning model trained by a first machine learning algorithm [Teja par 61].
Regarding claims 8, 9 and 11:
The claims are rejected as the methods of using the system of claims 1, 2 and 4.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-6, 8-13, and 15-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-6, 8-13, and 15-20 of U.S. Patent No. 12321222. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the ‘222 patent teach all recitations of the instant claims and thus anticipate the instant claims.
Claim 1 is exemplary:
Instant claim language
‘222 claim language
Explanation
A system comprising:
A system comprising:
identical
at least one hardware processor;
at least one hardware processor;
identical
a computer-readable medium storing instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform operations comprising:
and a computer-readable medium storing instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform operations comprising:
identical
receiving, from a pipeline, an identification of an event that occurred during operation of the pipeline on first software code created by a first user;
receiving, from a continuous integration/continuous deployment (CI/CD) pipeline, an identification of an error that occurred during operation of the CI/CD pipeline on a first software code created by a first user and an event log containing events occurring during the operation;
Broadened by removal of details such as the type of pipeline, changing the term error the broader event, and eliminating the event log
using an event analysis engine on the identification of the event to identify a root cause of the event;
using an error analysis engine on the identification of the error and the event log to identify a root cause of the error;
Broadened by changing error to event and eliminating the event log
using a model determination component to determine whether to utilize an internal artificial intelligence (AI) tool or an external AI tool, the internal AI tool operated by an entity operating the CI/CD pipeline, the external AI tool operated by an entity other than the entity operating the CI/CD pipeline;
Removed from instant claim
in response to a determination that an external Al tool should be used, passing the root cause to the external Al tool, the external Al tool being a Generative Artificial Intelligence (GAI) model that generates one or more actions;
in response to a determination that the external AI tool should be used, passing the root cause of the error to the external AI tool, the external AI being a Generative Artificial Intelligence (GAI) model that generates one or more solutions to the root cause of the error;
Broadened by changing “one or more solutions to the root cause of the error” in the ’222 claim to “one or more actions”
and causing the one or more actions to be displayed to the first user.
and causing the one or more solutions to be displayed to the first user in a user interface.
Broadened by changing “solutions” to “actions” and eliminating the user interface
The remaining independent claims 8 and 15 contain nearly identical broadening or removal of claim language.
The language of dependent claims 2-6, 9-13, and 16-20 of the instant application is identical to that of the corresponding dependent claims of the ’222 patent.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
‘148 to Acharya et al. discloses using a generative AI to process a user request to look at a software problem, build a context for a portion of code related to the problem including a root cause, and build and submit a prompt for an LLM to analyze and provide solutions to the problem.
‘357 to Brafman et al. discloses using CI/CD data to form root cause hypotheses for system failures, extracts pipeline metadata, builds logs and other data to analyze regarding the failure, generates hypotheses on failure causes, prioritizes them, determines resolutions for the hypotheses and gets user responses to prompt resolution.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARC M DUNCAN whose telephone number is (571)272-3646. The examiner can normally be reached M-F: 730am-9am, 10am-4:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bryce Bonzo can be reached at 571-272-3655. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARC DUNCAN/Primary Examiner, Art Unit 2113
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