DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This is a first office action in response to application filed, with the above serial number, on 27 November 2024 in which claims 1-6 are presented for examination. A preliminary amendment was filed 18 July 2025 amending claim 1 and adding claims 7-8 but was not entered, see Notice of non-compliant 31 July 2025. Claims 1-6 are therefore pending in the application.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The claim recites in line 9-10 “processing, at the node, the log data to select a model processing result, wherein the model processing result is associated with a subset of the log data” and is amended to add “determining a subset of the log data that is relevant to the selected model processing result”. The amendment does not appear to further narrow the claim. It is assumed the second subset of data that is relevant is according to antecedent basis the same subset of data as that being processed to select a subset, and thus the subset is already associated, it is not clear being relevant is different from being associated with the subset as they are synonymous, and if already associated it is similarly already determined.
Claim Rejections - 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.
Claim(s) 1-6 are is/are rejected under 35 U.S.C. 103 as being unpatentable over Das et al (hereinafter “Das”, 10,601,640) in view of Prabhudesai et al (hereinafter “Prabhudesai”, 2021/0312277),
As per Claim 1, Das discloses a system comprising:
at least one processor; and memory, operatively connected to the at least one processor and storing instructions that, when executed by the at least one processor, cause the system to perform a set of operations (at least col. 15:4-34; memory, processor), the set of operations comprising:
accessing log data associated with a node of a network, wherein the log data comprises a plurality of events associated with a computing device of the node (at least col. 2:55-58; monitoring service 105 may evaluate log data generated by the cloud computer stack; Table 1 in col. 3 log parameters);
processing, at the node, the log data to select a model processing result, wherein the model processing result is associated with a subset of the log data (at least col. 5:66-6:35; 6:45-51; 7:1-3, 8:43-59, 9:2-29; 11:1-22; building a token from processed log data, diagnosing if the log data indicates a fault or predicting a fault, using a resolution model from stored resolutions in ontology repository to resolve fault; col. 4:10-12; extracted portions of log data);
generating, at the node, an indication of the model processing result (at least col. 9:33-45; 10:46-51; action taken to resolve fault and success, failure or outcome of attempted resolution); and
providing the indication to a parent node of the node (at least col. 4:22-42; SME interface 108 may receive resolution data, diagnosis data and/or computer resource information data from users involved in managing the operation of the cloud computer stack 104, including, for example, IT professionals and administrators; ITSM interface 112 may communicate with one or more ITSM system to receive the resolution data, diagnosis data, and/or computer resource information).
wherein processing the log data to select the model processing result comprises:
processing, at the node, the log data using a first model to generate a first model processing result (at least col. 5:66-6:35; 6:45-51; 7:1-3, 8:43-59, 9:2-29; 11:1-22; resolution model may include a machine learning model that maps a resolution identifier to information included in the stack tokens. For example, the nodes of the historical stack tokens may be mapped to one or more resolution identifier. During inference type, the nodes of a stack token may be extracted and submitted to the resolution model to infer one or more resolution);
determining a subset of the log data that is relevant to the selected model processing result (at least col. 5:66-6:35; 6:45-51; 11:1-22; col. 4:10-12; extracted log parameters comprising portions of log data).
Das fails to explicitly disclose a content distribution network (CDN) and
processing, at the node, the log data using a second model to generate a second model processing result; and
selecting the selected model processing result from the first model processing result and the second model processing result based at least in part on: a first model performance metric of the first model; and a second model performance metric of the second model,
wherein the first model and the second model are selected from a set of distinct models.
However, the use and advantages for using such a system was well known to one skilled in the art before the effective filing date of the claimed invention as evidenced by the teachings of Prabhudesai. Prabhudesai discloses, in an analogous art, processing CDN computing resource data with at least two distinct models and selecting the most accurate model (at least Prabhudesai paragraph 4-5, 196, 166, 35-36). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the use of Prabhudesai’s model selection and CDN with Das, as Das’ cloud service resolution models could be used on any type of network, using such system on a CDN would be obvious and Das networking is a network delivering content, Prabhudesai teaches selecting the best model as a more accurate model would better predict future usage of a resource based on historically logged demand of the past which would lead to more desirable consequences as Prabhudesai outlines.
As per Claim 2. The system of claim 1, wherein the set of operations further comprises: receiving, from the parent node in response to the indication, an action to perform at the node based on the model processing result (at least col. 9:33-45; The applied intelligence framework 102 may further include an action layer 132. The action layer 132 may resolve the identified fault in a cloud computing platform (220). For example, the applied intelligence framework 102 may access information in the stack token to determine the appropriate corrective action to correct or prevent a fault. The action layer 132 may include a resolution builder 134; col. 4:22-42; SME interface 108 may receive resolution data, diagnosis data and/or computer resource information data from users involved in managing the operation of the cloud computer stack 104, including, for example, IT professionals and administrators; ITSM interface 112 may communicate with one or more ITSM system to receive the resolution data, diagnosis data, and/or computer resource information).
As per Claim 3. The system of claim 1, wherein the indication of the model processing result comprises at least one of: the subset of the log data; an identifier associated with the node; or an identifier associated with the computing device of the node (at least col. 7:45-59; the ontology repository 126 may store resolutions and/or resolution identifiers. The resolution identifiers may be associated with other information, including, for example, historical log data and/or historical stack token information acquired from one or more cloud computing stacks. For example, the IT ontology repository 126 may include a mapping between a resolution identifier, a computer resource identifier, and/or a fault identifier; col. 10:36-11:6; the feedback controller 138 may enrich the ontology repository 126 based on the stack token. For example, the feedback controller 138 may append the stack token, or a portion thereof, in the ontology repository 126. In some examples, the stack token and the ontology repository may include respective information that follows a graph data structure. The feedback controller 138 may identify nodes of the resolution repository that match (or correspond to) nodes of the stack token).
As per Claim 4. The system of claim 1, wherein the set of operations further comprises: receiving, from the parent node, the first model to process log data of the node (at least col. 8:43-59; the ontology repository 126 may store resolutions and/or resolution identifiers. The resolution identifiers may be associated with other information, including, for example, historical log data and/or historical stack token information acquired from one or more cloud computing stacks. For example, the IT ontology repository 126 may include a mapping between a resolution identifier, a computer resource identifier, and/or a fault identifier).
As per Claim 5. The system of claim 1, wherein the set of operations further comprises: generating the first model based at least in part on historical log data of the node (at least col. 8:52-59; the ontology repository 126 may store resolutions and/or resolution identifiers. The resolution identifiers may be associated with other information, including, for example, historical log data and/or historical stack token information acquired from one or more cloud computing stacks).
As per Claim 6. The system of claim 5, wherein the first model is generated further based on service log data from a service (at least Das col. 3:1-32; performance log data; Prabhudesai paragraph 4-5, 196, 166, 35-36; CDN).
Conclusion
This is a Continuation of applicant's earlier Application No. 17/342,138. All claims are identical to, patentably indistinct from, or have unity of invention with the invention claimed in the earlier application (that is, restriction (including lack of unity) would not be proper) and could have been finally rejected on the grounds and art of record in the next Office action if they had been entered in the earlier application. Accordingly, THIS ACTION IS MADE FINAL even though it is a first action in this case. See MPEP § 706.07(b). 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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Givental par. 23 recites log processing using a plurality of different ML models.
See also Larumbe par. 107-109 and the other references on the 892 for multiple model processing.
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/GREGORY TODD/Primary Examiner, Art Unit 2443
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