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 .
Response to Arguments
Applicant's arguments with respect to 35 U.S.C. 101 Abstract Idea in regards to claim 1-20 have been considered but are moot due to new grounds of rejection necessitated by amendments. Examiner respectfully disagrees with Applicant’s arguments. Applicant's arguments filed 3/11/2026 have been fully considered but they are not persuasive. These claims are directed to collecting information, organizing it, asking modified questions, using inference models to analyze the information, producing predictions/confidence scores, and selecting a remediation action. These steps are essentially information processing and decision-making. The claims that recite mental-process-type activities such as observations, evaluations, judgments, and opinions can fall within the abstract-idea category, even when performed on a computer. Merely using a computer to perform data parsing, comparing, or evaluation at a high level of generality can still be abstract.
Claims 9 has been cancelled. Claim 21 is new.
Applicant's arguments with respect to 35 U.S.C. 102 in regards to claims 1-8 and 11-20 have been considered but are moot due to new grounds of rejection necessitated by amendments. See detailed rejection below.
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 1-8 and 10-21 are rejected under 35 U.S.C. 101 Abstract Idea.
Claims 1, 11 and 16 are directed to collecting information, organizing it, asking modified questions, using inference models to analyze the information, producing predictions/confidence scores, and selecting a remediation action. These steps are essentially information processing and decision-making. The claims that recite mental-process-type activities such as observations, evaluations, judgments, and opinions can fall within the abstract-idea category, even when performed on a computer. Merely using a computer to perform data parsing, comparing, or evaluation at a high level of generality can still be abstract.
The claims do mention a “data processing system,” log information, inference models, a structured knowledge repository, root-cause prediction, and remediation. But the claims do not recite a specific technical improvement to computer operation, such as a new logging architecture, a new model-training technique, a new remediation mechanism, a specific system-configuration change, or a concrete way that the computer itself is improved. The claims can be eligible when it integrates the abstract idea into a practical application, such as an improvement to computer functioning or another technical field, but the improvement must be reflected in the claim, not merely stated as a desired result. The final step, “causing the data processing system to perform the at least one remediation action,” helps the applicant’s eligibility position, but it is not enough as written because the remediation action is not concretely defined.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims are (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. There is further no improvement to the computing device.
Dependent claims 2-8, 10, 12-15 and 17-21 further recite an abstract idea performable by a human and do not amount to significantly more than the abstract idea as they do not provide steps other than what is conventionally known.
Claims 2, 12 and 17: it does not claim a specific technical improvement to the LLM or computer system; it only uses an AI model to perform abstract prediction.
Claims 3, 13, and 18: which is still abstract question analysis and prediction-making.
Claims 4, 14 and 19: which is just a result of information analysis, not a concrete technical improvement.
Claims 5, 15 and 20: which is a mathematical/statistical scoring rule and therefore an abstract idea.
Claim 6: question-template generation is still abstract information organization and does not improve computer technology.
Claim 7: which is only organizing information by event type, not improving how the computer itself operates.
Claim 8: which is still abstract question modification and information processing.
Claim 10: which is an abstract evaluation or judgment about information.
Claim 21: which is still abstract correlation of information from logs.
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) 1, 6-8, 10-11 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mahamuni et al. (US 2023/0018199) in view of Beller et al. (US 11,227,113).
Claims 1, 11 and 16,
Mahamuni teaches a method of managing a data processing system of data processing systems, comprising ([0108] one or more embodiments of methods for predicting, preventing and remediating batch job failures, as described in accordance with FIGS. 2-5 above, using one or more computing systems defined generically by computing system 100 of FIG. 1):
obtaining a structured knowledge repository built using a second inference model that correlates an indication of a root cause and a failure ([0088-0090] [0092] a knowledge base and inference/reasoning engine; “the reasoning engine may also include an inference engine which may take existing information stored by the knowledge base 243 and the fact database, then use both sets of information to reach one or more conclusions and/or implement an action; use of AI/ML to arrive at one or more predicted batch job failures, root cause conclusions … and/or recommended actions; that archived failed-job data is mapped to current system logs and that the previous root cause for the similar batch job failure and remediation actions that corrected the historical failure may be recommended);
the input data includes log information related to aspects of the data processing system, each aspect corresponding to an event or a component of the data processing system ([0079-0080] [0083] [0091] that log files automatically produce time-stamped documentation of processes of a system while in an execution and running state and that workflow messages and error messages are stored in the knowledge base; logs, messages, metrics, and process-level information associated with batch jobs, including processes invoked by each batch job and maps invoked processes to messages/logs),
generating a prediction response corresponding to a predicted occurrence of the root cause by using the one or more predictions ([0090] [0112-0113] that the knowledge base/AI engine predicts batch failures, determines root-cause conclusions, and outputs RCA results including a determination of the underlying cause of the job failure),
identifying, using the second inference model, at least one remediation action based on the predicted occurrence of the root cause and the structured knowledge repository ([0088-0090] [0112-0113] using the knowledge base and inference/AI engine to predict failures and remediation actions and that previously successful remediation steps may alleviate the current failure); and
causing the data processing system to perform the at least one remediation action to obtain an updated data processing system ([0114-0115] that the recommended remediation action … is implemented either automatically by the system, approved by the system admin or manually applied and that the knowledge base is updated with feedback after remediation).
The difference between the prior art and the claimed invention is that Mahamuni does not explicitly teach generating one or more augmented questions using input data, a user intent, and the structured knowledge repository, wherein the user intent is related to a first aspect of the data processing system, and the one or more augmented questions modify the user intent based on the structured knowledge repository; providing the user intent, the one or more augmented questions, and the input data into a first inference model as a model input data to obtain one or more predictions; wherein the prediction response comprises a confidence score for each of the one or more predictions.
Beller teaches generating one or more augmented questions using input data, a user intent, and the structured knowledge repository ([col. 2 line 41 to col. 3 line 29] that a batch of questions can be generated to approximate the user’s information need and that intent … may be understood by modifying terms of the question to determine a context; question generator is configured to generate one or more additional questions based, at least in part, on the first question),
the user intent is related to a first aspect of the data processing system ([co. 2 line 41 to col. 3 line 41] a first question from a user and modification of that question to approximate the user’s information need; in the combination, the user’s first question/intent would concern Mahamuni’s system aspect, such as a log event, component, invoked process or failure), and
the one or more augmented questions modify the user intent based on the structured knowledge repository ([col. 5 line 15 to col. 6 line 5] that the question generator can replace one term in the first question … with another term that is more specific or more general and that semantic changes can result in a batch of questions that relate to the same information need but with varying specificity or breadth);
providing the user intent, the one or more augmented questions, and the input data into a first inference model as a model input data to obtain one or more predictions ([col. 3 line 42 to col. 4 line 32] sending the batch of questions to a QA processor: the batch process 132 is configured to send each of the batch of questions to a QA processor 101 and the QA processor processes the question to determine answer (Mahamuni teaches an RNN/LSTM model trained using time-series input to predict process invocations and flag potential failures see [0118-0120]));
wherein the prediction response comprises a confidence score for each of the one or more predictions ([col. 2 lines 20-52] [claims 8-12] that the QA system outputs an answer to the input question along with a confidence measure and that candidate answer are ranked using confidence scores; determining confidence scores for the candidate answers and generating a set of confidence scores).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teachings of Mahamuni with teachings of Beller by modifying the predictive batch job failure detection and remediation as taught by Mahamuni to include generating one or more augmented questions using input data, a user intent, and the structured knowledge repository, wherein the user intent is related to a first aspect of the data processing system, and the one or more augmented questions modify the user intent based on the structured knowledge repository; providing the user intent, the one or more augmented questions, and the input data into a first inference model as a model input data to obtain one or more predictions; wherein the prediction response comprises a confidence score for each of the one or more predictions as taught by Beller for the benefit of providing automated mechanisms for searching through large sets of sources of content, and analyze them with regard to an input question to determine an answer to the question (Beller [col. 1 lines 17-27).
Claim 6,
Beller further teaches the method of claim 1, wherein the one or more augmented questions are generated using an augmented question script comprising a plurality of question templates ([Fig. 2] possible additional question templates 222, 224, 226 and batch of questions 232, 234, 236) or using a second inference model trained using the plurality of question templates and the structured knowledge repository.
Claim 7,
Beller further teaches the method of claim 6, wherein the input data comprises events, and each of the plurality of question templates is associated with at least one event of the events ([Fig. 2] question regarding "who flow the atomic bomb to Japan in 1945?" producing plurality of additional/batch questions based on the intent/event of the question).
Claim 8,
Beller further teaches the method of claim 7, wherein the user intent comprises a question regarding the events ([Fig. 2] "who flow the atomic bomb to Japan in 1945?"), and each of the one or more augmented questions are different from the question included in the user intent ([Fig. 2] possible additional questions/batch of questions).
Claim 10,
Mahamuni further teaches the method of claim 9, further comprising: after providing the prediction response ([0083-0090] the remediation steps occur after the prediction response is provided):
assessing, using the second inference model, a likelihood of the root cause being accurate using the prediction response ([0115] allowing the knowledge base to reinforce the accuracy of the root cause analysis; validation of predicted causes using secondary analysis; the system includes an automation engine component that evaluates accuracy of the predicted event).
Claim(s) 2-5, 12-15 and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mahamuni et al. (US 2023/0018199) in view of Beller et al. (US 11,227,113) and further in view of Wang et al. (“Self-Consistency Improves Chain of Thought Reasoning in Language Models”; Mar. 7, 2023).
Claims 2, 12 and 17,
Mahamuni further teaches used to generate the one or more predictions using the model input data (the AI engine analyzes “metrics, logs, messages, feedback, process-level information etc. and uses them to arrive at one or more predicted batch job failures, root cause conclusions … and/or recommended actions).
The difference between the prior art and the claimed invention is that Mahamuni nor Beller explicitly teach wherein the first inference model is a large language model (LLM) comprising a plurality of logical pathways.
Wang teaches wherein the first inference model is a large language model (LLM) comprising a plurality of logical pathways ([Abstract] [3.1 Language models and prompts] chain-of-thought prompting combined with pre-trained large language models and evaluates four transformer-based language models including GPT-3 with 175-billion parameters and PaLM-540B with 540-billion parameters).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teachings of Mahamuni with teachings of Wang by modifying the predictive batch job failure detection and remediation as taught by Mahamuni to include wherein the first inference model is a large language model (LLM) comprising a plurality of logical pathways as taught by Wang for the benefit shows that self-consistency boosts the performance of chain-of-thought prompting with a striking margin on a range of popular arithmetic and commonsense reasoning benchmarks (Wang [Abstract).
Claims 3, 13 and 18,
Beller further teaches the method of claim 2, wherein the user intent comprises a question related to the one or more predictions (Fig. 2] first question ), the question triggering use of a first logical pathway of the plurality of logical pathways ([Fig. 2] possible additional questions) to obtain a first prediction of the one or more predictions ([Fig. 3] first/second answer sets), and
the one or more augmented questions comprises a first augmented question that is different from the question included in the user intent ([Fig. 2] batch (different; 232, 234, 236) of question based on the intent of the first question), the first augmented question triggering use of a second logical pathway of the plurality of logical pathways to obtain a second prediction of the one or more predictions ([Figs. 2-3] second answer set based on the plurality of possible addition question/batch of questions), the second logical pathway being different from the first logical pathway ([Fig. 3] second answer set using batch of questions).
Claims 4, 14 and 19,
Beller further teaches the method of claim 3, wherein the second prediction is different from the first prediction ([Fig. 3] first/second answer set which are different based on scoring).
Claims 5, 15 and 20,
Beller further teaches the method of claim 3, wherein the confidence score for each of the one or more predictions is based on a frequency of each of the one or more predictions ([Fig. 3] [col. 6 line 59 to col 8 line 54] confidence scores based on weighted combination of the first and second answer sets).
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mahamuni et al. (US 2023/0018199) in view of Beller et al. (US 11,227,113) and further in view of Ruan et al. (US 2016/0124823).
Claim 21,
Beller further teaches the method of claim 1, wherein the one or more augmented questions modify the user intent ([col. 2 lines 41-57] often the intent a question may be understood by modifying terms of the question to determine a context of the input question; a batch of questions can be generated to approximate the user's information need).
The difference between the prior art and the claimed invention is that Mahamuni nor Beller explicitly teach relating two aspects of the data processing system that are not directly related with each other in a given entry of the log information.
relating two aspects of the data processing system that are not directly related with each other in a given entry of the log information ([0071] [0090] [0094] distributed systems/aspects components: a cloud management system includes multiple, distributed components that are responsible for different functions, e.g., compute, storage, network, identity, image and when a problem occurs, the root cause may lie in any of the related components, requires collectively analyzing all the operational data gathered from these components; that the direct relationship may be absent from a long entry/component log because no global identifier is available for tracking inter-component interactions; different components may define different identifiers for only part of the request processing flow; discovering the relationship between these identifiers, and piece together the end-to-end view of request handling, represented in logs).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teachings of Beller with teachings of Ruan by modifying precision batch interaction with a question answering system as taught by Beller to include relating two aspects of the data processing system that are not directly related with each other in a given entry of the log information as taught by Ruan for the benefit of reducing or eliminating the need for human to find clues as to what the root cause of the problem is by determining and highlighting the most relevant log entries that may contain the key information (Ruan [0008]).
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.
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 SHREYANS A PATEL whose telephone number is (571)270-0689. The examiner can normally be reached Monday-Friday 8am-5pm PST.
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, Pierre Desir can be reached at 571-272-7799. 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.
SHREYANS A. PATEL
Primary Examiner
Art Unit 2653
/SHREYANS A PATEL/ Examiner, Art Unit 2659
Prosecution Insights