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 in regards to claims 1-4, 6-8, 10-14, 16-19 and 22- 25 have been considered, however are not found to be persuasive due to the following reasons. Examiner respectfully disagrees with Applicant’s arguments because the amended limitations still primarily retie collecting, organizing, analyzing, and evaluating information to make predictions and decisions, which constitute abstract mental processes and mathematical concepts. The structured knowledge repository organizes relationships among failures, root causes, and remediation actions; the augmented questions modify user intent by identifying relationships in stored information; the inference models analyze that information to predict a root cause and select a remediation action; and the confidence score is determined based on the frequency with which a prediction occurs. Merely characterizing these information-processing operations as being performed by “inference models” or describing from “hidden knowledge,” does not itself establish an improvement to computer functionality. Unlike Enfish, the claim does not recite a new computer architecture, data structure that changes how the computer stores or retrieves data, or other specific improvement to the computer itself. Applicant’s reliance on Desjardins likewise does not establish eligibility merely because machine-learning technology is involved; the relevant inquiry remains whether the claim as a whole reflects the asserted technological improvement, rather than merely using computer technology to perform an abstract analysis.
Applicant’s reliance on the specification’s statements concerning improved prediction accuracy, increased computing-resource availability, and reduced resource expenditures also does not demonstrate that the judicial exception is integrated into a practical application. Although the claim ultimately requires performing a remediation action that reduces the impact of a failure, it does not specify what particular technical remediation is performed or how the computer system is technically modified; instead, the result is broadly defined as obtaining an updated system that reduces the failure’s impact. Likewise, generating augmented questions and using them with logs and a knowledge repository descries how information is prepared for the inference model, rather than a particular improvement in the operation of the underlying computer system. Thus, the claim uses computer components and machine-learning models as tools for carrying our the abstract processes of information analysis, prediction, scoring, and decision-making followed by a broadly recited application of the resulting decision. Accordingly, Applicant has not shown that the amended limitations integrate the abstract idea into a practical application under Step 2A, prong two, and the arguments do not provide a persuasive basis for withdrawing the 101 rejection.
Claims 5, 9, 15 and 20-21 have been cancelled. Claims 22-25 are new.
Applicant's arguments and amendments with respect to 35 U.S.C. 103 rejection of claims 1, 11 and 16 have been considered and found persuasive, and the rejection has been withdrawn. See detailed reason for allowance 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 1-4, 6-8, 10-14, 16-19 and 22-25 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.
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 22: no specific improvement to computer functionality.
Claim 23: abstract data comparison, analysis and evaluation.
Claim 24: mathematical and information analysis process using generic computing technology.
Claim 25: no particular technological improvement of the computer itself.
Allowable Subject Matter
Claims 1-4, 6-8, 10-14, 16-19 and 22-25 would be allowable if the Applicant can overcome the 101 Abstract Idea set forth.
The following is a statement of reasons for the indication of allowable subject matter: Mahamuni et al. (US 2023/0018199) in view of Beller et al. (US 11,227,113): for claims 1, 11 and 16:
Mahamuni teaches managing a computing system by using system logs, a structured knowledge base, machine-learning models, root cause analysis, and remediation actions to predict and reduce system failures. Mahamuni explains that knowledge base 243 stores historical successful and failed jobs, logs, messages, process information, performance metrics, and feedback and may be “structured as a database” with an ontological organization. The logs record events, processes, messages and communications involving system applications and the operating system, while AI engine 223 uses an RNN/LSTM model and historical time-series data to predict process invocations and potential batch-job failures. Mahamuni further teaches using the knowledge base and process mappings to compare a current failure with historical failures, identify an underlying root cause, and recommend remediation actions that previously corrected similar failures. The recommended remediation can then be automatically or manually implemented, and the system determines whether is successfully alleviates the failure.
Beller teaches a question-answering system that generates additional or augmented questions from an original user question to better determine the user’s intent and then uses the resulting answers and their frequency to select a higher-confidence response. In particular, Better teaches that a question generator produces one or more additional questions based on the first question by using a lexical database containing sematic relationships between terms, and resulting batch of questions provides context that better approximates the user’s information need. The QA system processes each question to generate candidate answer and confidence scores, and Beller further teaches that answers occurring in multiple answer sets may receive a higher ranking because the ranking considers the number of times a candidate answer is returned.
The difference between the prior art and the claimed invention is that Mahamuni nor Beller explicitly teach the one or more augmented questions modify the user intent based on the structured knowledge repository by relating two aspects of the data processing system that are not directly related with each other in a given entry of the log information.
Therefore, it would not 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 and Beller to include the one or more augmented questions modify the user intent based on the structured knowledge repository by relating two aspects of the data processing system that are not directly related with each other in a given entry of the log information.
Conclusion
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SHREYANS A. PATEL
Primary Examiner
Art Unit 2653
/SHREYANS A PATEL/ Examiner, Art Unit 2659
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