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 .
Detailed Action
This is a first final Office Action for application 18/268,382 in response to arguments and amendments filed on 12/30/2025. Claims 1-20 are pending and examined below.
Specification
The amendment to the specification is sufficient to overcome the previous objection, therefore the previous objection is withdrawn.
Response to Arguments
Applicant’s arguments, see pgs. 10-13, filed 12/30/2025, with respect to the rejection(s) of claim(s) 1-3 and 5-20 under 35 USC § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Chowhudry et al. (US Pub. 2025/0225328).
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) 1-3 and 5-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brunswig et al. (EP 3913496) in view of Gibson (US Pub. 2021/0117437), Chowhudry et al. (US Pub. 2025/0225328), and Okorafor et al. (US Pat. 11,321338).
Regarding claim(s) 1, Brunswig teaches
A system, comprising: a memory that stores computer executable components; (Fig. 1 #102-105)
a processor that executes computer executable components stored in the memory, wherein the computer executable components comprise: a meta machine data capture component that receives input data and stores, to a metadata repository, meta machine data indicative of a context for machine data; and (Fig. 2; Par. [0043] the metadata provider (#236) retrieves metadata from the DW metadata store (#252) )
a data governance data structure configured to indicate administrative governance information with respect to the machine data. (Par. [0052] privacy component (#306; i.e. data governance) includes authorizations (#312))
Brunswig does not explicitly teach
a meta machine data generation component that generates the meta machine data according to a format that is determined based on the input data, wherein the meta machine data generation component uses a chatbot with access to an artificial intelligence model to at least one of: generate the meta machine data or determine a structure for the format according to the input data wherein the meta machine data generated by the meta machine data generation component comprises: a data pipeline configuration data structure configured to store configuration information of a data pipeline that communicates the machine data;
a data quality metrics data structure configured to store data quality information of a consuming application that consumes the machine data; and
However, from the same field Gibson teaches
a meta machine data generation component that generates the meta machine data according to a format that is determined based on the input data, wherein the meta machine data generated by the meta machine data generation component comprises: a data pipeline configuration data structure configured to store configuration information of a data pipeline that communicates the machine data; (Par. [0033, 43] pipeline generation component (#120) generates a pipeline based in part on metadata)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the pipeline generation component of Gibson into the metadata store of Brunswig. The motivation for this combination would have been to improve the speed, consistency, and reduce the error rate of generated code as explained in Gibson (Par. [0029]).
The combination of Brunswig and Gibson not explicitly teach
wherein the meta machine data generation component uses a chatbot with access to an artificial intelligence model to at least one of: generate the meta machine data or determine a structure for the format according to the input data
a data quality metrics data structure configured to store data quality information of a consuming application that consumes the machine data; and
However, from the same field, Chowhudry teaches
wherein the meta machine data generation component uses a chatbot with access to an artificial intelligence model to at least one of: generate the meta machine data or determine a structure for the format according to the input data (Par. [0021, 23] the illustrated embodiment is a computing tool for training an LLM (i.e. chatbot) so that the trained foundation model may process structured data structures, and perform operations to automatically generate/semantically enhance the metadata associated with these structured data structures)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the LLM of Chowhudry into the metadata machine data generation component of Gibson. The motivation for this combination would have been to provide timely and innovative solutions for end users seeking to obtain actionable information from their big data as explained in Chowhudry (Col. 2 [Lines 30-38]).
The combination of Brunswig, Gibson and Chowhudry do not explicitly teach
a data quality metrics data structure configured to store data quality information of a consuming application that consumes the machine data; and
However, from the same field Okorafor teaches
a data quality metrics data structure configured to store data quality information of a consuming application that consumes the machine data; and (Fig. 1; Col. 6 [Lines 37-47] a data quality control module (#124) uses provided metadata as input for quality analysis)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the quality control module of Okorafor into the metadata store of Brunswig. The motivation for this combination would have been to improve customizability of the architecture (Col. 2 [Lines 30-39]).
Regarding claim(s) 3, Brunswig, Gibson, Chowhudry and Okorafor teach claim 1 as shown above, and Gibson further teaches
The system of claim 1, wherein the configuration information of the data pipeline comprises data pipeline input data that describes an input to the data pipeline, processing data that describes processing that the data pipeline performs, storage data that describes data that is stored by the data pipeline, and output data that describes an output of the data pipeline. (Par. [0050] the transformation pipeline (i.e. input to output of the data pipeline) can use metadata provided by the entity model )
Regarding claim(s) 5, Brunswig, Gibson, Chowhudry and Okorafor teach claim 1 as shown above, and Brunswig further teaches
The system of claim 1, wherein the administrative governance information comprises: stakeholder information indicative of a data owner identifier of the machine data, a data steward identifier of the machine data, a privacy reviewer identifier of the machine data, or a notifier identifier of the machine data; (Par. [0052] privacy component (#306) includes authorizations (i.e. data owner #312))
Okorafor further teaches
policy information indicative of a policy that is applicable to the machine data; and (Fig. 1; Col. 6 [Lines 37-47] a data quality control module (#124) controls data based on policy for data quality analysis)
process information indicative of a process that is applicable to the machine data. (Fig. 1; Col. 6 [Lines 37-47] a data quality control module (#124) controls data based on policy for data quality analysis)
Regarding claim(s) 6, Brunswig, Gibson, Chowhudry and Okorafor teach claim 1 as shown above, and Gibson further teaches
The system of claim 1, wherein the format generated by the meta machine data generation component further comprises a data context data structure configured to indicate a context of the machine data, wherein the context of the machine data comprises a name of a topic for the machine data, a type of the topic for the machine data, a description of the topic for the machine data, and a name or modality of a domain entity that generates the machine data. (Par. [0033, 50, 113] the transformation pipeline can use metadata including entities, attributes, semantic metadata (i.e. name, type, description, name, etc.) and relationships provided by the entity model )
Regarding claim(s) 7, Brunswig, Gibson, Chowhudry and Okorafor teach claim 1 as shown above, and Gibson further teaches
The system of claim 1, wherein the format generated by the meta machine data generation component further comprises a data actors data structure configured to indicate provenance information of the machine data, wherein the provenance information comprises a producer identifier of the machine data, a consumer identifier of the machine data, and a domain entity identifier that identifies a domain entity that generates the machine data. (Par. [0033, 50, 113] the transformation pipeline can use metadata including entities, attributes, semantic metadata and relationships (i.e. producer ID, consumer ID, domain ID, etc.) provided by the entity model )
Regarding claim(s) 8, Brunswig, Gibson, Chowhudry and Okorafor teach claim 1 as shown above, and Gibson further teaches
The system of claim 1, wherein the format generated by the meta machine data generation component further comprises a data definitions data structure configured to indicate structural information of the machine data and semantic information of the machine data, wherein the structural information indicates a schema utilized by the machine data or a data type associated with a data element of the machine data, and wherein the semantic information indicates a relationship between at least two different data elements of the machine data. (Par. [0033, 50, 113] the transformation pipeline can use metadata including schema and relationships )
Regarding claim(s) 9, Brunswig, Gibson, Chowhudry and Okorafor teach claim 1 as shown above, and Gibson further teaches
The system of claim 1, wherein the format generated by the meta machine data generation component further comprises a data metrics data structure configured to indicate a range of suitable values for the machine data and a privacy classification of the machine data. (Par. [0113] partition directive also includes a range)
Regarding claim(s) 10, Brunswig, Gibson, Chowhudry and Okorafor teach claim 1 as shown above, and Gibson further teaches
The system of claim 1, wherein the computer executable components further comprise an artifact generation component that generates a group of artifacts based on the meta machine data, wherein the group of artifacts comprises at least one of a document, a portion of code, or a configuration that is used by a development and operations pipeline. (Par. [0029, 113] partition directive also includes a schema code)
Regarding claim(s) 11, Brunswig, Gibson, Chowhudry and Okorafor teach claim 1 as shown above, and Gibson further teaches
The system of claim 10, wherein the computer executable components further comprise a telemetry component that: generates a telemetry bundle comprising a portion of the group of artifacts; and (Par. [0029, 113] partition directive also includes a schema code)
transmits the telemetry bundle to a configurable device, wherein the configurable device is at least one of a domain entity device that generates the machine data, an edge device that communicates the machine data received from the domain entity device to a data warehouse device that stores the machine data, or the data warehouse device. (Par. [0029, 113, 125] partition directive also includes a schema code and can include upserts (i.e. stored machine data))
Regarding claim(s) 12, Brunswig, Gibson, Chowhudry and Okorafor teach claim 1 as shown above, and Gibson further teaches
The system of claim 11, wherein the computer executable components further comprise a data flow component that, in response to examining the meta machine data and a state of the configurable device, determines an optimization that modifies a data flow of machine data being delivered to the data warehouse device. (Par. [0029, 113, 125] the automatically generated ETL code can also be optimized so that the target data model creation process runs faster)
Regarding claim(s) 13, Brunswig, Gibson, Chowhudry and Okorafor teach claim 1 as shown above, and Gibson further teaches
The system of claim 12, wherein the optimization further modifies a configuration of the configurable device. (Par. [0029, 113, 125] the automatically generated ETL code can also be optimized so that the target data model creation process runs faster)
Regarding claim(s) 14, while worded slightly different than claim 1, is rejected under the same rationale.
Regarding claim(s) 15, while worded slightly different than claim 10, is rejected under the same rationale.
Regarding claim(s) 16, while worded slightly different than claim 11, is rejected under the same rationale.
Regarding claim(s) 17, while worded slightly different than claims 12 and 13, is rejected under the same rationale.
Regarding claim(s) 18, while worded slightly different than claim 1, is rejected under the same rationale.
Regarding claim(s) 19, while worded slightly different than claim 11, is rejected under the same rationale.
Regarding claim(s) 20, while worded slightly different than claim 17, is rejected under the same rationale.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brunswig et al. (EP 3913496) in view of Gibson (US Pub. 2021/0117437), Chowhudry et al. (US Pub. 2025/0225328) and Okorafor et al. (US Pat. 11,321338), and further in view of Behzadi et al. (US Pat. 11,126,635).
Regarding claim(s) 4, Brunswig, Gibson Chowhudry, and Okorafor teach claim 1 as shown above, and Okorafor further teaches
The system of claim 1, wherein the data quality information of the consuming application comprises app importance data that indicates a degree of importance of the machine data to the consuming application, volume data that indicates an amount or velocity of the machine data that is expected to be consumed by the consuming application, retention data that indicates a time to retain the machine data, loss data that indicates a data loss threshold for the machine data, and latency data that indicates a time from collection of the machine data until the machine data is ready for consumption by the consuming application. (Par. [0128] added metadata can include historical retention (i.e. time to retain the machine) policies (Examiner notes information other than retention is show immediately below, but the language is being left here for referential clarity))
The combination of Brunswig, Gibson, Chowhudry and Okorafor do not explicitly teach
The system of claim 1, wherein the data quality information of the consuming application comprises app importance data that indicates a degree of importance of the machine data to the consuming application, volume data that indicates an amount or velocity of the machine data that is expected to be consumed by the consuming application, loss data that indicates a data loss threshold for the machine data, and latency data that indicates a time from collection of the machine data until the machine data is ready for consumption by the consuming application. (Col. 123 [15-42], Col. 65 [Lines 6-20], ranked data (i.e. degree of importance), a wide variety of database types are used depending on the throughput requirements (i.e. volume and latency data), and completeness (i.e. data loss) threshold)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the quality metrics of Behzadi into the quality control module of Okorafor. The motivation for this combination would have been to take advantage of distributed software architectures and applications as explained in Behzadi (Col. 6 [Lines 59-65]).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to J MITCHELL CURRAN whose telephone number is (469)295-9081. The examiner can normally be reached M-F 8:00am - 5:00pm.
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/J MITCHELL CURRAN/Examiner, Art Unit 2161
/SHERIEF BADAWI/Supervisory Patent Examiner, Art Unit 2169
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