DETAILED ACTION
This office action is in response to the filed application 19/291,695 on August 6, 2025.
Claims 1-8 are presented for examination.
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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on August 6, 2025 was in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements were considered by the Examiner.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 3-4, 6-8 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Richardson et al. (US 2022/0138556).
In regard to claim 1, Richardson et al. teach a non-transitory computer-readable recording medium storing therein a computer program that causes a computer to execute a process comprising:
acquiring first partial log data extracted from first log data output by an information processing system (receive initial training data, fig. 3, para. 54-55, tokenizer 212 tokenizing the training data and producing a tokenized input for the training engine, para. 57-64)); and
generating, by entering the first partial log data to a trained machine learning model, a search program for searching second log data for second partial log data having a common pattern with the first partial log data by using the machine learning model (trained neural network or multiple trained neural networks to process or parse data logs … may include NLP machine learning model, para. 62, fig. 406).
In regard to claim 3, Richardson et al. teach the non-transitory computer-readable recording medium according to claim 1, wherein the machine learning model is a natural language processing model for generating an output text from an input text (Natural Language Processing (NLP), para. 6-10), and wherein the generating includes entering the first partial log data and an instruction instructing generation of the search program to the machine learning model and causing the machine learning model to output the search program (Natural language Processing (NLP) machine learning model where the first training data includes a first data log generated by a first type of machine and second training data includes a second data log generated by a second type of machine and determine that the neural network has trained on the first training data and the second training data and storing the neural network in computer memory such that the neural network is made available to process additional data logs, para. 9).
In regard to claim 4, Richardson et al. teach the non-transitory computer-readable recording medium according to claim 3, wherein the generating further includes entering, to the machine learning model, sample data in which third partial log data is associated with another search program capable of searching for the third partial log data (each type of machine may be configured to generate a different type of training data which may be in the form of a partial log, para. 49-53).
In regard to claim 6, Richardson et al. teach the non-transitory computer-readable recording medium according to claim 1, wherein the process further includes executing the search program on the second log data and extracting the second partial log data from the second log data (trained neural network which can be stored in memory of the processing system and used by the processor to process/parse data logs, para. 49-53).
In regard to claim 7, Richardson et al. teach a generation method comprising:
acquiring, by a processor, first partial log data extracted from first log data output by an information processing system (receive initial training data, fig. 3, para. 54-55, tokenizer 212 tokenizing the training data and producing a tokenized input for the training engine, para. 57-64)); and
generating, by the processor, by entering the first partial log data to a trained machine learning model, a search program for searching second log data for second partial log data having a common pattern with the first partial log data by using the machine learning model (trained neural network or multiple trained neural networks to process or parse data logs … may include NLP machine learning model, para. 62, fig. 406).
In regard to claim 8, Richardson et al. teach an information processing apparatus comprising:
a memory configured to store first partial log data extracted from first log data output by an information processing system (receive initial training data, fig. 3, para. 54-55, tokenizer 212 tokenizing the training data and producing a tokenized input for the training engine, para. 57-64)); and
a processor coupled to the memory and the processor configured to generate, by entering the first partial log data to a trained machine learning model, a search program for searching second log data for second partial log data having a common pattern with the first partial log data by using the machine learning model (trained neural network or multiple trained neural networks to process or parse data logs … may include NLP machine learning model, para. 62, fig. 406).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 2 and 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Richardson et al. (US 2022/0138556) in further view of Kale et al. (US 2026/0030136).
In regard to claim 2, Richardson et al. does not explicitly teach the non-transitory computer-readable recording medium according to claim 1, wherein the search program includes a regular expression indicating the pattern.
Kale et al. teach of a proactive error resolution by applying regular expression (regex) to entries to extract one or more entries relating to error events (para. 20).
It would have been obvious to modify the medium of Richardson et al. by adding Kale et al. proactive error resolution. A person of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make the modification because it would aid in applying an input format such as a regular expression to identify related entries in a log (para. 20).
In regard to claim 5, Richardson et al. does not explicitly teach the non-transitory computer-readable recording medium according to claim 1, wherein the first partial log data is a part of the first log data, the part being quoted by failure report data indicating a failure in the information processing system.
Kale et al. teach of a proactive error resolution by applying regular expression (regex) to entries to extract one or more entries relating to error events (para. 20).
Refer to claim 2 for motivational statements.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO 892.
Teranishi et al. (US 2026/0064562) machine learning model for search program
Brammer et al. (US 2025/0312652) machine learning for recommendation model
Das et al. (US 2025/0285006) search program
Togawa (US 2020/0042422) log analysis with partial logs
Lou et al. (US 2019/0371450) generate a prediction of outcome
Rennison (US 8,620,909) context extraction
Dousson et al. (US 7,388,482) partial logs
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Loan Truong whose telephone number is 408-918-7552. The examiner can normally be reached on 10AM-6PM PST M-F.
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/Loan L.T. Truong/Primary Examiner, Art Unit 2114 HYPERLINK "mailto:Loan.truong@uspto.gov" Loan.truong@uspto.gov
