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 .
Detail Action
On 07/08/2024, Application 18/766,563 is filed with claims 1-5.
That is a Non-Final Action.
Claim Rejections - 35 USC § 102
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 and 5 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Paduchuru US Publication 2023/0289877.
18/766,563
Paduchuru US Publication 2023/0289877
Claim 1
A big data platform for executing spark jobs in a batch pipeline used for building data products, comprising:
Paduchuru teaches enterprise scheduler may be utilized to schedule a job for the landing pad for file trigger and invoke the job (i.e., Spark job) via rest API for pod for batch manager service. Fig. 5, p0109-p0113;
a scheduler service, comprising an app written in Scala, capable of starting and/or stopping jobs, where the scheduler service abstracts away the complexity of running a Spark job;
Paduchuru Fig. 5, p0109-p0113;
Paduchuru teaches using Scala to build core engine; p0107.
a controller, capable of communication with the scheduler service, the controller orchestrating and managing running Spark clusters, provisioning clusters, and managing cluster scaling;
Paduchuru teaches PLADPM is a strategic state-of-the-art rules-based engine natively built on cloud leveraging big data technologies. It is a comprehensive framework to build out all fiduciary/supervision control in line with the regulations. It incorporates all aspects of the alert generation process right from the data ingestion, data filtering, alert detection, and alert distribution. Examiner interprets Pladpm as a controller Fig.4-5 p0086-p0135;
where the scheduler service takes as input a job request including a json object;
where the schedule service maintains a queue of active jobs, and matches job configuration with capacity for the job to run on a cluster.
Paduchuru teaches using JSON as data format; p0066-p0134.
Claim 5
The big data platform of claim 1, including a metric and event monitoring layer.
Paduchuru; p0109-p0112
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 2 is rejected under 35 U.S.C. 103 as being unpatentable over Paduchuru US Publication 2023/0289877 in view of Yarlagadda US Publiation 2025/0315663.
18/766,563
Paduchuru US Publication 2023/0289877 in view of Yarlagadda US Publiation 2025/0315663
Claim 2
The big data platform of claim 1, where jobs are submitted through an http api, taking a json file as input, where the api response is used to extract error messages, job status, and job diagnostics.
Paduchuru does not specifically teaches json as input;
Yarlagadda teaches accept JSON-encoded input data from a client device via an SSL connection, parse the data to extract variables, and store the data to a database; see Yarlagadda p1043
It would have been obvious at the time of the invention for a person of ordinary skill in the art (POSITA) to include Yarlagadda’s teaching with method of Paduchuru in order to allow system accept JSON data.
Claims 3 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Paduchuru US Publication 2023/0289877 in view of Siebel US Publication 2018/0191867.
18/766,563
Paduchuru US Publication 2023/0289877 in view of Siebel US Publication 2018/0191867
Claim 3
The big data platform of claim 1, where the controller utilizes spot instances, and supports adding additional storage into a cluster when needed.
Paduchuru does not specifically teaches adding additional storage;
Siebel teaches automatically adding storage nodes and processing nodes to a storage cluster to accommodate and process incoming data; and automatically rebalancing and partitioning the incoming data into the time-series data, unstructured and relational data across the storage cluster as the nodes are being added; see Claim 32;
It would have been obvious at the time of the invention for a person of ordinary skill in the art (POSITA) to include Siebel’s teaching with method of Paduchuru in order to provide user with flexible cluster storage.
Claim 4
The big data platform of claim 1, including incident analysis logs including cluster level information, node level information, and job level information.
Paduchuru does not specifically teaches level information;
Siebel teaches real-time event logs from the SCADA system to quickly identify anomalies at the both the individual sensor level as well as for clusters of sensors; see p0485;
It would have been obvious at the time of the invention for a person of ordinary skill in the art (POSITA) to include Siebel’s teaching with method of Paduchuru in order to monitor sensor level.
Related Prior Art
Here is a list of references relates to scheduler:
Manikani et al. US Publication 2024/0393769: A method can include receiving field equipment data from a source; detecting a data schema for the source; configuring a listener for the source according to a corresponding detected data schema to receive additional field equipment data; and assessing at least a portion of the additional field equipment data using an assessment engine to generate a hierarchy of data metric values for the source, where the hierarchy is customizable and navigable responsive to receipt of instructions.
Lohe US Publication 2021/0266167: The Social Aggregating, Fractionally Efficient Transfer Guidance, Conditional Triggered Transaction, Datastructures, Apparatuses, Methods and Systems (“SOCOACT”) transforms smart contract request, crypto currency deposit request, crypto collateral deposit request, crypto currency transfer request, crypto collateral transfer request inputs via SOCOACT components into transaction confirmation outputs. An aggregated crypto 2-party transaction trigger entry that specifies at least one associated aggregated blockchain oracle may be instantiated. A first encrypted token may be obtained from a first associated aggregated blockchain oracle. A second encrypted token may be obtained from a second associated aggregated blockchain oracle. It may be determined that an instantiated aggregated crypto 2-party transaction trigger entry unlock event occurred, and unlocking the instantiated aggregated crypto 2-party transaction trigger entry and providing the first encrypted token to a second party and providing the second encrypted token to a first party may be facilitated.
Redinger US Publication 2021/0043285: The technology disclosed relates to efficient tertiary analysis of genomic data. The technology disclosed includes splitting a genomic data file into a plurality of segments, and storing segments in the plurality of segments across nodes of a distributed storage system, pushing the segments from the nodes of the distributed storage system to nodes of a distributed, in-memory computing engine, distributing directives of tertiary analysis job contexts for the genomic data file across the nodes of the distributed, in-memory computing engine, directly executing the distributed directives on the segments stored on the nodes of the distributed, in-memory computing engine to cause parallel processing of the segments, and aggregating results of the parallel processing across the nodes of the distributed, in-memory computing engine to produce an output.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PENG KE whose telephone number is (571)272-4062. The examiner can normally be reached M-F 6:30-5:00.
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, Kevin Young can be reached at (571) 270-3180. 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.
PENG KE
Primary Examiner
Art Unit 2194
/PENG KE/Primary Examiner, Art Unit 2194
Prosecution Insights