DETAILED ACTION
Claims 1-18 are pending. Claims 1-18 are considered in this Office action.
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 (IDSs) submitted on 1/11/2025, 9/11/2025, 9/24/2025, 11/7/2025, 1/15/2026, 2/27/2026, and 5/23/2025 have been acknowledged.
The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner. The initialed and dated copies of Applicant’s IDS form 1449 is attached to the instant Office action.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Alice – Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 17, and 18 recite the limitations to scanning a plurality of different data domains of an enterprise information environment (Receiving Information, an Observation, a Mental Process; a Fundamental Economic Process, i.e. analyzing data for determined relationships, a Certain Method of Organizing Human Activity), chunking a plurality of data records of multiple enterprise data sources of the plurality of different data domains of the enterprise information environment, the chunking generating one or more respective data record segments for each of the plurality of data records (Analyzing the Information, an Evaluation, a Mental Process; a Fundamental Economic Process, i.e. analyzing data for determined relationships, a Certain Method of Organizing Human Activity), generating respective contextual metadata for each of the one or more respective data record segments, each respective contextual metadata indicating semantic or contextual descriptions of the respective data records segment, and at least one of the respective contextual metadata being capable of facilitating a determination of a relationship between one of the respective data record segments of a particular respective data record and another one of the respective data segments of another respective data record (Analyzing the Information, an Evaluation, a Mental Process; a Fundamental Economic Process, i.e. analyzing data for determined relationships, a Certain Method of Organizing Human Activity), generating a respective segment embedding for each data record segment based on the respective contextual metadata (Analyzing the Information, an Evaluation, a Mental Process; a Fundamental Economic Process, i.e. analyzing data for determined relationships, a Certain Method of Organizing Human Activity), and storing the segment embeddings in an embeddings datastore (Receiving and Storing Information, an Observation, a Mental Process; a Fundamental Economic Process, i.e. analyzing data for bias, a Certain Method of Organizing Human Activity), which under their broadest reasonable interpretation, covers performance of the limitation in the mind for the purposes of evaluating data using a comparison, but for the recitation of generic computer components. That is, other than reciting a computer-readable medium, system, memory, an enterprise information environment, an embeddings datastore, and processor, nothing in the claim element precludes the step from practically being performed or read into the mind for the purposes of evaluating data for segmentation, which is a Fundamental Economic Process, a Certain Method of Organizing Human Activity. For example, generating a respective segment embedding for each data record based on the respective contextual metadata encompasses a data analyst or statistician performing analysis on data for segmenting the data, an observation, evaluation, and judgment. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas, an observation, evaluation, and judgment. Further, as described above, the claims recite limitations for a Fundamental Economic Process, a “Certain Method of Organizing Human Activity”. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the above stated additional elements to perform the abstract limitations as above. The system, enterprise information environment, embeddings datastore, memory, processors, and computer-readable medium are recited at a high-level of generality (i.e., as a generic software/module performing a generic computer function of storing, retrieving, sending, and processing data) such that they amount to no more than mere instructions to apply the exception using generic computer components. Even if taken as an additional element, the collecting, storing, and transmitting steps above are at best insignificant extra-solution activity as these are receiving, storing, and transmitting data as per the MPEP 2106.05(d). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered both individually and as an ordered combination. As discussed above with respect to integration of the abstract idea into a practical application, the additional element being used to perform the abstract limitations stated above amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible. Applicant’s Specification states:
“[0221]FIG. 17 depicts a diagram 1700 of an example of a computing device 1702. Any of the systems, engines, datastores, and/or networks described herein may comprise an instance of one or more computing devices 1702. In some embodiments, functionality of the computing device 1702 is improved to the perform some or all of the functionality described herein. The computing device 1702 comprises a processor 1704, memory 1706, storage 1708, an input device 1710, a communication network interface 1712, and an output device 1714 communicatively coupled to a communication channel 1716. The processor 1704 is configured to execute executable instructions (e.g., programs). In some embodiments, the processor 1704 comprises circuitry or any processor capable of processing the executable instructions. ”
Which shows that system/computer has the generic pieces of a processor, memory input device, output device, etc., and thus any generic computing device can be used to perform the abstract limitations, such as a laptop, phone, desktop, etc., and from this interpretation, one would reasonably deduce the aforementioned steps are all functions that can be done on generic components, and thus application of an abstract idea on a generic computer, as per the Alice decision and not requiring further analysis under Berkheimer, but for edification the Applicant’s specification has been used as above satisfying any such requirement. This is “Applying It” by utilizing current technologies. For the receiving, storing, and transmitting steps that were considered extra-solution activity in Step 2A above, if they were to be considered additional elements, they have been re-evaluated in Step 2B and determined to be well-understood, routine, conventional, activity in the field. The background does not provide any indication that the additional elements, such as the system, memory, processors, etc., nor the receiving, storing, or collecting steps as above, are anything other than a generic, and the MPEP Section 2106.05(d) indicates that mere collection or receipt, storing, or transmission of data is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For these reasons, there is no inventive concept. The claim is not patent eligible.
Claims 2-16 contain the identified abstract ideas, further narrowing them, with no new additional elements to be considered as part of a practical application or under prong 2 of the Alice analysis of the MPEP, thus not integrated into a practical application, nor are they significantly more for the same reasons and rationale as above.
After considering all claim elements, both individually and in combination, Examiner has determined that the claims are directed to the above abstract ideas and do not amount to significantly more. Therefore, the claims and dependent claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank International, No. 13–298.
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 of this title, 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 1-12 and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Agrawal (U.S. Publication No. 2021/017,3744) in view of Panuganty (U.S. Publication No. 2020/021,0647).
Regarding Claims 1, 17, and 18, Agrawal, a system and method for optimizing restoration of deduplicated data stored in cloud-based storage resources, teaches a method comprising:
scanning a plurality of different data domains of an enterprise information environment ([0197] one or more components, scan data and/or associated metadata for classification purposes and [0129] segment of an enterprise, such as a Chicago office, and a second cell may represent a different geographic segment, such as a New York City office);
chunking a plurality of data records of multiple enterprise data sources of the plurality of different data domains of the enterprise information environment ([0260] each chunk can include a header and a payload. the payload can include files (or other data units) or subsets thereof included in the chunk), the chunking generating one or respective data record segments for each of the plurality of data records ([0373] generate a first list of data segments which includes the first data segment that are physically stored consecutively in the storage resources);
generating respective contextual metadata for each of the one or more respective data record segments, each respective contextual metadata indicating semantic or contextual descriptions of the respective data records segment ([0212] another type of information management policy 148 is an audit policy or security policy, which comprises preferences, rules and/or criteria that protect sensitive data in system 100, an audit policy may generate sensitive objects (contextual metadata) which are files or data objects that contain particular keywords confidential, or privileged (contextual descriptions) and/or are associated with particular keywords (e.g., in metadata) or particular flags (e.g. in metadata identifying a document or email as personal, confidential, etc.).
Although Agarwal teaches segments and generating contextual metadata data for them as above, it does not explicitly state a relationship between one or more segments, at least one of the respective contextual metadata being capable of facilitating a determination of a relationship between one of the respective data record segments of a particular respective data record and another one of the respective data segments of another respective data record, generating a respective segment embedding for each data record segment based on the, respective contextual metadata, or storing the segment embeddings in an embeddings datastore.
Panuganty, a system and method for automated summarization of extracted insight data, teaches at least one of the respective contextual metadata being capable of facilitating a determination of a relationship between one of the respective data record segments of a particular respective data record and another one the respective data segments of another respective data record ([0163] curation engine module identify and/or generate inter-data relationship information, and store this information in relational module, relational module represents data relationships identified by the curation engine module that are used to form data structures within a corresponding database, and [0176] parent / child relationship between data points it is to be appreciated that this is merely for discussion purposes, and alternate types of relationship attributes can be modeled and/or identified);
generating a respective segment embedding for cache data record segment based on the respective contextual metadata, and storing the segment embeddings in an embeddings datastore ([0175] applying machine - learning algorithms to the curated data and/or attributes to generate the relational data models stored via curated relational data model database).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the segments of Agrawal with the generating and storing the segment embeddings in an embeddings datastore of Panuganty as they are both analogous art along with the claimed invention which teach solutions to issues in segmenting of data, and the combination would lead to an improved system for the purpose of helping organizations share information acquired at one computing device with other computing devices in the organization as taught in [0066] of Panuganty.
Examiner notes Agrawal teaches a system with one or more processors, memory, and a computer-readable medium ([0069] [0381]).
Regarding Claim 2, Agrawal teaches wherein the contextual metadata indicates a particular data domain of the plurality of different data domains of the enterprise information environment ([0129] other cells may represent departments within a particular office, e.g., human resources, finance, engineering, etc., different frequency and under different retention rules).
Regarding Claim 3, Agrawal teaches wherein the data records include any of documents, database tables, models, text, images, video, audio, artificial intelligence insights, application outputs, applications, source code, scripts, and compiled source code ([0094] primary data 112 objects including word processing documents 119A - B, spreadsheets 120, presentation documents 122, video files 124, image files 126; paragraph).
Regarding Claim 4, Agrawal teaches wherein the contextual metadata indicates enterprise access control information associated with an enterprise access control system of the enterprise information environment and any of the respective data domains, data records, and data record segments ([0094] primary data 112 objects including word processing documents 119A - B, spreadsheets 120, presentation documents 122, video files 124, image files 126; paragraph).
Regarding Claim 5, Although Agrawal teaches segments as in Claim 1 above, it does not explicitly teach segment embeddings.
Panuganty teaches wherein the respective segment embeddings comprise vectors and the embeddings datastore comprises one or more vector datastores ([0171] parameter can be extracted and propagated by the proximity platform, such as weights used in an artificial neural network, support vectors in a support vector machine, coefficients in a linear regression or logistic regression algorithm).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the segments of Agrawal with the embeddings and vectors of Panuganty as they are both analogous art along with the claimed invention which teach solutions to issues in segmenting of data, and the combination would lead to an improved system for the purpose of helping organizations share information acquired at one computing device with other computing devices in the organization as taught in [0066] of Panuganty.
Regarding Claim 6, Agrawal teaches wherein the contextual metadata is stored in respective headers of the respective segments ([0260] component may divide files into chunks and generate headers for each chunk by processing the files, headers can include a variety of information such as file and/or volume identifier(s), offset(s), and/or other information associated with the pay load data items, a chunk sequence number).
Regarding Claim 7, Agrawal teaches The method of claim 3, wherein each of the segments comprises a respective sub-model of at least one of the models ([0197] depending on the embodiment, the data classification database(s) can be organized in a variety of different ways, including centralization, logical sub-divisions, and/or physical sub-divisions).
Regarding Claim 8, the combination of Agrawal and Panganty teaches The method of claim 7, but Agrawal does not teach wherein the models comprise different types of machine learning models.
Panuganty teaches wherein the models comprise different types of machine learning models ([0086], [0171]each respective user profile has private data curation, private relational data models, and so forth, which is used to enhance and/or educate various machine learning algorithms, machine - learning algorithms, module, and/or models to aid in curating and/or analyzing data;).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the segments of Agrawal with the models comprise different types of machine learning models of Panuganty as they are both analogous art along with the claimed invention which teach solutions to issues in segmenting of data, and the combination would lead to an improved system for helping organizations share information acquired at one computing device with other computing devices in the organization as taught in [0066] of Panuganty.
Regarding Claim 9, the combination of Agrawal and Panuganty teaches the method of claim 8, but Agrawal does not teach large language models.
Panuganty teaches wherein the different types of machine learning models include large language models ([0177] models can be defined using any type of diagramming techniques and/or schemas, such as Bachman notation, Barker's notation, Unified Modeling Language (UML), Object - role modeling (ORM), Extensible Markup Language (XML) schema etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the segments of Agrawal with the models comprise different types of machine learning models of Panuganty as they are both analogous art along with the claimed invention which teach solutions to issues in segmenting of data, and the combination would lead to an improved system for helping organizations share information acquired at one computing device with other computing devices in the organization as taught in [0066] of Panuganty.
Regarding Claim 10, Agrawal teaches wherein the scanning is performed continuously and/or on-demand ([0354] the data chunks received in response to parallel read requests are stored in data bucket (s) at media agent and are served on demand to data agent 142).
Regarding Claim 11, Agrawal teaches wherein the chunking is performed parallel with the scanning, and the chunking is triggered based on one or more scanning operations of the scanning ([0354] causing work queue to issue read requests in parallel to take advantage of parallel read processing capabilities at file system and the data chunks received in response to parallel read requests which are stored in data bucket (s) 727 at media agent 344 and are served on demand).
Regarding Claim 12, Agrawal teaches wherein each of the data records segments is stored in a hierarchical structure of the embeddings datastore ([0129] multiple cells may be organized hierarchically, so that cells may inherit properties from hierarchically superior cells or be controlled by other cells in the hierarchy, the hierarchical information is maintained by one or more storage managers 140 that manage the respective cells (e.g., in corresponding management database).
Regarding Claim 15, Agrawal teaches wherein the chunking is performed by an agent, the segment embeddings are generated by another agent, and information is retrieved from the embeddings store based on the respective embedding by an additional agent, and wherein the agents are supervised by an orchestrator ([0315] media agent 344 is analogous to media agent 144 and further comprises additional features for operating in system 300, such as logic for orchestrating the disclosed optimization techniques, (look - ahead reader) logic, logic for performing SFile Runs, logic for aggregating read requests and creating read lists).
Regarding Claim 16, Agrawal teaches wherein the plurality of data records are processed by a type system prior to being any of scanned and chunked ([0246] storage manager 140 may similarly update its index 150 to include information relating to the secondary copy operation such as information relating to the type of operation a physical location associated with one or more copies created by the operation; paragraph [0246]).
Claims 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Agrawal (U.S. Publication No. 2021/017,3744) in view of Panuganty (U.S. Publication No. 2020/021,0647) in further view of Hankins (U.S. Publication No 2022/016,4109).
Regarding Claim 13, the combination of Agrawal and Panuganty teaches the method of claim 12 as above, but Agrawal does not teach wherein the one or more information retrieval processes comprises a portion of one or more generative artificial intelligence processes, and neither does Panuganty.
Hankins, a data replication system and method, teaches wherein the one or more information retrieval processes comprises a portion of one or more generative artificial intelligence processes ([0180] support the serialized or simultaneous execution of artificial intelligence applications, machine learning applications, data analytics applications, data transformations, and other tasks that collectively may form an Al ladder)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the artificial intelligence of the combination of Agrawal and Panuganty with a portion of one or more generative artificial intelligence processes of Hankins as they are all analogous art along with the claimed invention which teach solutions to issues in segmenting of data, and the combination would lead to an improved system for the purpose of producing data streams to distribute replicated data and metadata across partitions in a destination storage system as taught in [0025] of Hankins.
Regarding Claim 14, the combination of Agrawal and Panuganty teaches the method of claim 12 as above, but Agrawal does not teach wherein the one or more information retrieval processes comprises a portion of one or more generative artificial intelligence processes, and neither does Panuganty.
Hankins, a data replication system and method, teaches wherein the one or more information retrieval processes comprises a portion of one or more generative artificial intelligence processes ([0180] support the serialized or simultaneous execution of artificial intelligence applications, machine learning applications, data analytics applications, data transformations, and other tasks that collectively may form an Al ladder)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the artificial intelligence of the combination of Agrawal and Panuganty with a portion of one or more generative artificial intelligence processes of Hankins as they are all analogous art along with the claimed invention which teach solutions to issues in segmenting of data, and the combination would lead to an improved system for the purpose of producing data streams to distribute replicated data and metadata across partitions in a destination storage system as taught in [0025] of Hankins.
Conclusion
The prior art made of record is considered pertinent to applicant's disclosure.
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SCAM EVALUATION SYSTEM
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/JOSEPH M WAESCO/Primary Examiner, Art Unit 3625B 5/25/2026
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