FINAL REJECTION, SECOND DETAILED ACTION
Status of Prosecution
The present application, 18/121,891 filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The application was filed in the Office on March 15, 2023. A letter granting Applicant’s request for suspension of examination was mailed on Dec. 6, 2023.
The Office mailed a non-final rejection, first detailed action on April 28, 2026.
Applicant’s representative initiated an interview on July 15, 2026. Applicant then filed amendments with arguments and remarks on July 20, 2026.
Claims 1-3, 5-21 are pending and are all rejected. Claims 1, 16 and 19 are independent.
Status of Claims
Claim 4 is canceled by this amendment and Claim 21 is new.
Claims 1, 2, 9, 12, 15, 16 and 19-20 are rejected under 35 U.S.C. § 103 as being unpatentable over Sade et al., (“Sade”), United States Patent 10,862,737 B1, filed on December 8, 2020 in view of Balani et al., (“Balani”), United States Patent 8,468,391 B2, filed on June 18, 2013 and in further view of non-patent literature Ekelhart et al., “The SLOGERT Framework for Automated Log Knowledge Graph Construction,” published in 2021 (“SLOGERT”).
Claims 3 and 17 are rejected under 35 U.S.C. § 103 as being unpatentable over Sade in view of Balani in view of SLOGERT and in further view of Mujumdar et al., (“Mujumdar”), United States Patent Application Publication 2023/0274160 filed on August 31, 2023.
Claims 5-8, 11 and 21 are rejected under 35 U.S.C. § 103 as being unpatentable over Sade in view of Balani in view of SLOGERT and in further view of Tonkin et al. (“Tonkin”), United States Patent Application Publication 2020/0372057 A1, published on Nov. 26, 2020.
Claim 10 is rejected under 35 U.S.C. § 103 as being unpatentable over Sade in view of Balani in view of SLOGERT in further view of Aleksandrovich et al. (“Aleksandrovich”), United States Patent Application Publication 2021/0200731 A1, published on July 1, 2021.
Claim 13 is rejected under 35 U.S.C. § 103 as being unpatentable over Sade in view of Balani in view of SLOGERT in further view of Aleksandrovich et al. (“Aleksandrovich”), United States Patent Application Publication 2021/0200731 A1, published on July 1, 2021.
Claims 14 and 18 are rejected under 35 U.S.C. § 103 as being unpatentable over Sade in view of Balani in view of SLOGERT in further view of Tonkin et al. (“Tonkin”), United States Patent Application Publication 2020/0372057 A1, published on Nov. 26, 2020.
Response to Remarks and Arguments
Examiner thanks Applicant for the courtesies extended during the July 15, 2026 interview. Examiner has reviewed the amendments and the remarks and arguments presented.
First, regarding the subject matter eligibility § 101 rejection, Examiner is persuaded by Applicant’s arguments and withdraws the rejection.
Second, regarding the prior art rejections, Examiner has considered the SLOGERT reference again carefully and as discussed in the interview, maintains its propriety and combinability for the amended portions of the independent claim. As no additional or new arguments have been presented regarding SLOGERT, Examiner maintains the rejection.
The claims stand rejected.
Claim Interpretation
A few particular definitions are highlighted for the reader’s convenience.
key-signal – “[i]n the context of the various embodiments, a key signal is a predefined attribute that serves as an informative aspect (e.g., a cue or a warning) related to the at least one downstream application. In the context of the various embodiments, a key signal is reflective of an issue or a datapoint related to one or more of the at least one downstream application. For instance, a key signal may indicate a warning associated with a downstream application task.” (Specification, par. 0047).
semi-structured data – “[i]n the context of the various embodiments, semi- structured data includes both structured data and unstructured data.” (Specification par. 0064).
Claim Rejection – 35 USC § 103
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.
A.
Claims 1, 2, 9, 12, 15, 16 and 19-20 are rejected under 35 U.S.C. § 103 as being unpatentable over Sade et al., (“Sade”), United States Patent 10,862,737 B1, filed on December 8, 2020 in view of Balani et al., (“Balani”), United States Patent 8,468,391 B2, filed on June 18, 2013 and in further view of non-patent literature Ekelhart et al., “The SLOGERT Framework for Automated Log Knowledge Graph Construction,” published in 2021 (“SLOGERT”).
As to Claim 1, Sade teaches: A computer-implemented log comprehension method comprising:
inducting data into a knowledge base associated with a log comprehension machine learning knowledge model (Sade: col. 6, lines 12-34, a machine learning prediction module [140 (i.e. a log comprehension machine learning knowledge model) has information fed into it to confer with log set knowledge base [150]) wherein inducting data into the knowledge base comprises:
extracting a plurality of key-value pairs from a log aggregator dataset including semi- structured data (Sade: Fig. 2, step [220], col. 11, lines 13-18, log sets (i.e. log files) are examined and key/value features (i.e. key-value pairs) are extracted; col. 1, lines 14 to 25, in general information may be extracted from all sourts of sources including unstructured customer text in addition, which Examiner asserts may be in the log);
deriving at least one value feature associated with the plurality of key-value pairs (Sade: col. 11, lines 27 to 34, the extracted features are presented);
deriving any key signal associated with the plurality of key-value pairs (Sade: col. 5, lines 47 to 56, “Accordingly, particular features extracted by the feature extractor 124 are inserted into corresponding entry positions in a vector representation generated by the representation generator 128. The resulting representation may be viewed as providing a "fingerprint" for the corresponding log set.”);
extracting at least one key-value pair from a log file including input data associated with at least one downstream application (Sade: Fig. 2, step [220], col. 11, lines 13-18, log sets (i.e. log files) are examined and key/value features (i.e. key-value pairs) are extracted. The features of the service issues to be investigated are in the key/value format (i.e. input data associated with at least one application);
deriving at least one value feature associated with the at least one key-value pair (Sade: col. 11, lines 27 to 34, the extracted features are presented);
deriving any key signal associated with the at least one key-value pair (Sade: col. 5, lines 47 to 56, “Accordingly, particular features extracted by the feature extractor 124 are inserted into corresponding entry positions in a vector representation generated by the representation generator 128. The resulting representation may be viewed as providing a "fingerprint" for the corresponding log set.”);
applying the log comprehension machine learning knowledge model in order to compare data associated with the at least one key-value pair extracted from the log file with knowledge base node key-value pair data (Sade: Fig. 2, step. [250], col. 11, lines 28-34, specified features are extracted are applied to the root cause machine learning prediction module [130] and/or the business intelligence reporting module [180]. Examiner asserts that the key/value format, which is the format the features are in, are compared); and
based upon the model application, creating mapping results compatible with the at least one downstream application (Sade: col. 11, lines 27 to 34, the extracted features are presented in a dashboard (i.e. a downstream application)).
PNG
media_image1.png
775
532
media_image1.png
Greyscale
Sade may not explicitly teach: inducting data into a knowledge base associated with a log comprehension machine learning knowledge model in order to configure a common log schema;
based upon the model application, creating mapping results formatted according to the common log schema and compatible with the at least one downstream application.
Balani teaches in general concepts related to log events being analyzed to determine the ontology of the log event (Balani: Abstract). Specifically, Balani teaches that log event ontology may be in a specific definition format (Balani: col. 9, lines 54-56, the definition [410] is a schema a type definition).
PNG
media_image2.png
797
648
media_image2.png
Greyscale
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified the Sade disclosures and teachings by consolidating the steps for the analysis as a single predetermined function that is optimized as taught by Balani. Such a person would have been motivated to do so with a reasonable expectation of success to capture the essence of optimizing all the steps in a single function for ease of optimization.
Sade and Balani may not explicitly teach: adding a node to a plurality of nodes of the knowledge base for each of the plurality of key-value pairs, wherein the node includes the at least one value feature and any key signal derived for the key-value pair.
SLOGERT teaches in general concepts related to analyzing logs using a knowledge-based approach (SLOGERT: Abstract). Specifically, SLOGERT teaches the use of RDF graph modelling from arbitrary unstructured log data (SLOGERT: Sec. 1, p. 2). Examiner notes that RDF (Resource Description Framework) is a well-known standard for a graphical approach of describing conceptualization of items with nodes and directed edges. SLOGERT teaches that the RDF may be used to capture the features extracted from the log data (SLOGERT: Fig. 1, Sec. 2.0, in the graph building phase, each line in a log file is parsed and a matching extraction and RDF modelling template is used to transform them into RDF).
PNG
media_image3.png
272
804
media_image3.png
Greyscale
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified the Sade-Balani disclosures and teachings by allowing for the graph building as taught by SLOGERT. Such a person would have been motivated to do so with a reasonable expectation of success to allow for the use of RDFs for a standardized approach of capturing the knowledge.
As to Claim 2, Sade, Balani teach the elements of claim 1.
Sade further teaches: transmitting the mapping results to the at least one downstream application (Sade: col. 11, lines 27 to 34, the extracted features are presented in a dashboard (i.e. a downstream application)).
As to Claim 9, Sade, Balani and SLOGERT teach the elements of claim 1.
SLOGERT further teaches: wherein inducting data into the knowledge base further comprises: updating the knowledge base responsive to user feedback (SLOGERT: Fig. 1, the Knowledge Engineer gives feedback accordingly).
As to Claim 12, Sade, Balani and SLOGERT teach the elements of claim 1.
Sade further teaches: wherein deriving the at least one value feature associated with the plurality of key-value pairs comprises: deriving at least one regular expression associated with the plurality of key-value pairs (Sade: col. 3, lines 16 to 22).
As to Claim 15, Sade, Balani and SLOGERT teach the elements of claim 1.
Sade further teaches: wherein deriving the at least one value feature associated with the at least one key-value pair comprises: deriving at least one regular expression associated with the at least one key-value pair (Sade: col. 3, lines 16 to 22).
As to Claim 16, it is rejected for similar reasons as claim 1. Sade further teaches processors and computer program product (Sade: col. 17, lines 49-62).
As to Claim 19, it is rejected for similar reasons as claim 1 and 16.
As to Claim 20, it is rejected for similar reasons as claim 2.
B.
Claims 3 and 17 are rejected under 35 U.S.C. § 103 as being unpatentable over Sade et al., (“Sade”), United States Patent 10,862,737 B1, filed on December 8, 2020 in view of Balani et al., (“Balani”), United States Patent 8,468,391 B2, filed on June 18, 2013 and in further view of non-patent literature Ekelhart et al., “The SLOGERT Framework for Automated Log Knowledge Graph Construction,” published in 2021 (“SLOGERT”) and in further view of Mujumdar et al., (“Mujumdar”), United States Patent Application Publication 2023/0274160 filed on August 31, 2023.
As to Claim 3, Sade, Balani and SLOGERT teach the elements of claim 2.
Sade, Balani and SLOGERT may not explicitly teach: updating the knowledge base responsive to log file mapping feedback associated with the at least one downstream application.
Mujumdar teaches in general concepts related to automatically detecting periods of normal activity by analyzing observability data in IT operations environments (Mujumdar: Abstract). Specifically, Mujumdar teaches that feedback is incorporated to further train and fine-tune the underlying clustering algorithm (Mujumdar: par. 0014).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified the Sade-Balani-SLOGERT disclosures and teachings by incorporating feedback using the user interface of Sade as taught and suggested by Mujumdar. Such a person would have been motivated to do so with a reasonable expectation of success to allow for best optimization of the downstream application with user feedback.
As to Claim 17, it is rejected for similar reasons as claim 3.
C.
Claims 5-8, 11 and 21 are rejected under 35 U.S.C. § 103 as being unpatentable over Sade et al., (“Sade”), United States Patent 10,862,737 B1, filed on December 8, 2020 in view of Balani et al., (“Balani”), United States Patent 8,468,391 B2, filed on June 18, 2013 and in further view of non-patent literature Ekelhart et al., “The SLOGERT Framework for Automated Log Knowledge Graph Construction,” published in 2021 (“SLOGERT”) and in further view of Tonkin et al. (“Tonkin”), United States Patent Application Publication 2020/0372057 A1, published on Nov. 26, 2020.
As to Claim 5, Sade, Balani and SLOGERT teach the elements of claim 1.
Sade, Balani and SLOGERT may not explicitly teach: wherein inducting data into the knowledge base further comprises: updating the knowledge base in accordance with the common log schema by pruning at least one node among the plurality of nodes.
Tonkin teaches in general concepts related to generating an ontology from a data structure associated with a data store (Tonkin: Abstract). Specifically, Tonkin teaches that the ontologies may be managed to allow for pruning or alignment (Tonkin: par. 0120, 123, a pruner module that determines a group of ontology terms).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified the Sade-Balani-SLOGERT disclosures and teachings by utilizing pruning techniques as taught by Tonkin. Such a person would have been motivated to do so with a reasonable expectation of success to allow for best maintenance of the ontology of the knowledge base.
As to Claim 6, Sade, Balani and SLOGERT teach the elements of claim 1.
Sade, Balani and SLOGERT may not explicitly teach: wherein inducting data into the knowledge base further comprises: updating the knowledge base in accordance with the common log schema by merging multiple nodes among the plurality of nodes.
Tonkin teaches in general concepts related to generating an ontology from a data structure associated with a data store (Tonkin: Abstract). Specifically, Tonkin teaches that the ontologies may be managed to allow for merging or alignment (Tonkin: par. 0228).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified the Sade-Balani-SLOGERT disclosures and teachings by utilizing merging techniques as taught by Tonkin. Such a person would have been motivated to do so with a reasonable expectation of success to allow for best maintenance of the ontology of the knowledge base.
As to Claim 7, Sade, Balani, SLOGERT and Tonkin teach the elements of claim 6.
Tonkin further teaches: wherein merging multiple nodes among the plurality of nodes comprises:
determining whether names of respective key elements of each of the multiple nodes have a semantic meaning exceeding a predetermined key similarity threshold (Tonkin: par. 0230, semantic similarity of is calculated for different terms to be matched, using a score (therefor a threshold);
determining whether respective relations of each of the multiple nodes have a semantic meaning exceeding a predetermined relation similarity threshold (Examiner asserts this would apply to the relationships as well in the RDF graphs of SLOGERT);
determining whether a level of pattern similarity among respective patterns of value elements associated with each of the multiple nodes exceeds a predetermined pattern similarity threshold (Examiner asserts this would apply to the patterns as well); and
determining whether a level of key signal similarity of derived key signals associated with each of the multiple nodes exceeds a predetermined key signal similarity threshold (Examiner asserts this would apply to the key signals as well).
As to Claim 8, Sade, Balani and SLOGERT teach the elements of claim 1.
Sade, Balani may not explicitly teach: wherein inducting data into the knowledge base further comprises: updating the knowledge base in accordance with the common log schema by splitting a single node among the plurality of nodes.
Tonkin teaches in general concepts related to generating an ontology from a data structure associated with a data store (Tonkin: Abstract). Specifically, Tonkin teaches that the ontologies may be managed to allow for merging or alignment (Tonkin: par. 0228). Additionally, various tables, such as a Type structure may be expanded, which is a splitting of a node in a sense.
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified the Sade-Balani-SLOGERT disclosures and teachings by utilizing merging and splitting techniques as taught by Tonkin. Such a person would have been motivated to do so with a reasonable expectation of success to allow for best maintenance of the ontology of the knowledge base.
As to Claim 11, Sade, Balani and SLOGERT teach the elements of claim 1.
Sade, Balani and SLOGERT may not explicitly teach: wherein deriving the at least one value feature associated with the plurality of key-value pairs comprises:
categorizing by value type one or more of the plurality of key-value pairs.
Tonkin teaches in general concepts related to generating an ontology from a data structure associated with a data store (Tonkin: Abstract). Specifically, Tonkin teaches that the ontologies may be managed to allow for merging or alignment (Tonkin: par. 0228). Additionally, the values may be assigned appropriate class annotations after the extraction and parsing from each of the ontologies accordingly (Tonkin: par. 0560, Examiner asserts that this assignment of a class annotation is a categorization).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified the Sade-Balani-SLOGERT disclosures and teachings by utilizing assignment/categorizations techniques as taught by Tonkin. Such a person would have been motivated to do so with a reasonable expectation of success to allow for best maintenance of the ontology of the knowledge base.
As to Claim 21, it is rejected for similar reasons as claim 5.
D.
Claim 10 is rejected under 35 U.S.C. § 103 as being unpatentable over Sade et al., (“Sade”), United States Patent 10,862,737 B1, filed on December 8, 2020 in view of Balani et al., (“Balani”), United States Patent 8,468,391 B2, filed on June 18, 2013 in view of non-patent literature Ekelhart et al., “The SLOGERT Framework for Automated Log Knowledge Graph Construction,” published in 2021 (“SLOGERT”) in further view of Aleksandrovich et al. (“Aleksandrovich”), United States Patent Application Publication 2021/0200731 A1, published on July 1, 2021.
As to Claim 10, Sade, Balani and SLOGERT teach the elements of claim 1.
Sade, Balani and SLOGERT may not explicitly teach: wherein extracting the plurality of key- value pairs from the log aggregator dataset comprises:
flattening the log aggregator dataset;
normalizing any universal entity in the flattened log aggregator dataset; and
parsing the plurality of key-value pairs from the flattened log aggregator dataset.
Aleksandrovich teaches in general concepts related to horizontally skimming composite datasets (Alekxandrovich: Abstract). Specifically, Aleksandrovitch teaches that for datasets that are to be analyzed, they may be flattened and normalized before being parsed (Aleksandrovith: par. 0054, the properties in a dataset object may be flattened into top-level properties; par. 0059, the data objects are normalized; par. 0088, first parser).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified the Sade-Balani-SLOGERT disclosures and teachings by performing the extraction of the plurality of key-value pairs with the flattening, normalizing and parsing as taught by Aleksandrovitch. Such a person would have been motivated to do so with a reasonable expectation of success to optimize distributed data processing of the logs (Aleksandrovich: pars. 0002-03).
E.
Claim 13 is rejected under 35 U.S.C. § 103 as being unpatentable over Sade et al., (“Sade”), United States Patent 10,862,737 B1, filed on December 8, 2020 in view of Balani et al., (“Balani”), United States Patent 8,468,391 B2, filed on June 18, 2013 and in further view of non-patent literature Ekelhart et al., “The SLOGERT Framework for Automated Log Knowledge Graph Construction,” published in 2021 (“SLOGERT”) in further view of Aleksandrovich et al. (“Aleksandrovich”), United States Patent Application Publication 2021/0200731 A1, published on July 1, 2021.
As to Claim 13, Sade, Balani and SLOGERT teach the elements of claim 1.
Sade, Balani and SLOGERT may not explicitly teach: wherein extracting the at least one key- value pair from the log file comprises:
flattening the log file;
normalizing any universal entity in the flattened log file; and
parsing the at least one key-value pair from the flattened log file.
Sade and Balani would have been modified in the similar fashion with the teachings and suggestions of Aleksandrovitch as noted in the rejection of claim 10.
F.
Claims 14 and 18 are rejected under 35 U.S.C. § 103 as being unpatentable over Sade et al., (“Sade”), United States Patent 10,862,737 B1, filed on December 8, 2020 in view of Balani et al., (“Balani”), United States Patent 8,468,391 B2, filed on June 18, 2013 and in further view of non-patent literature Ekelhart et al., “The SLOGERT Framework for Automated Log Knowledge Graph Construction,” published in 2021 (“SLOGERT”) in further view of Tonkin et al. (“Tonkin”), United States Patent Application Publication 2020/0372057 A1, published on Nov. 26, 2020.
As to Claim 14, Sade, SLOGERT Balani teach the elements of claim 1.
Sade, Balani and SLOGERT may not explicitly teach: wherein deriving the at least one value feature associated with the at least one key-value pair comprises: categorizing by value type one or more of the at least one key-value pair.
Sade, Balani and SLOGERT would have been modified in the similar fashion with the teachings and suggestions of Jiang as noted in the rejection of claim 11.
As to Claim 18, it is rejected for similar reasons as claim 14.
Conclusion
THIS ACTION IS MADE FINAL. 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 JAMES T TSAI whose telephone number is (571)270-3916. The examiner can normally be reached M-F 8-5 Eastern.
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, Viker Lamardo can be reached at 571-270-5871. 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.
/JAMES T TSAI/ Primary Examiner, Art Unit 2147
Prosecution Insights