DETAILED ACTION
Claims 1-20 are presented for examination.
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 statement (IDS) submitted on 1/15/26 and 5/18/26 have been considered by the Examiner.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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, 4-6, 8-11, 13-16, & 18-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Statton (U.S. Patent Publication 2024/0370339).
Regarding claims 1, 11, and 16:
Statton discloses a method, system, and non-transitory computer readable medium, comprising: obtaining, by a data management system (DMS) [the data platform element 150 of Figure 1; see also paragraph 0008], a first snapshot of a computing system, wherein the first snapshot comprises data associated with a set of files (paragraph 0031: “Data platform 150 includes backup manager 154 that provides backup of file system data for file system 153”; and paragraphs 0034-0035, including “A backup may include a full backup of the file system 153 data or may include less than a full backup of the file system 153 data, in accordance with backup policies. For example, a given backup of backups 142 may include all objects of file system 153 or one or more selected objects of file system 153” and “Backups 142 may be used to generate views and snapshots. A current view generally corresponds to a (near) real-time backup state of the file system 153. A snapshot represents a backup state of the primary storage system 105 at a particular point in time.”); determining, by the DMS, from among the set of files, a first subset of files or portions of files that comprise sensitive information (paragraph 0071: “Data platform 150 may provide role-based access controls (RBAC) for backup data and prevents users from accessing data they don't have permissions for, such as sensitive data (patient data/PII, trade secrets, financials, and more). Response generation platform 158 may in some examples incorporate RBAC, where filter generator 160 generates filter 304 to filter out data that does not align with users' permissions in order to provide responses that do align to users' permissions”; see also step 405 of Figure 4, and paragraph 0098); generating, by the DMS, one or more vectors based at least in part on data associated with a second subset of files or portions of files from among the set of files, the second subset of files or portions of files exclusive of files from the first subset of files or portions of files (paragraph 0100: “Filtered data is retrieved from data access layer 504, e.g., as a view or snapshot of file system 153 or filtered portion thereof at a particular time. The filtered data is presented to database layer 300. Embeddings generator 162 uses a vector database (not shown) along with a machine learning model 520 to calculate embeddings 164 for the data. Machine learning model 520 may be a language model. These embeddings 164, along with the data and metadata will be stored in the vector database of database layer 320.” and see also step 410 of Figure 4, and paragraph 0098); and adding, by the DMS, the one or more vectors to a vector database along with metadata or a pointer to the metadata, wherein the metadata is associated with the data from the first snapshot, and wherein the vector database comprises a knowledge repository that is accessible to an application associated with the DMS (paragraph 0106: “Once filtered by application of filter 505 and retrieved by data access layer 504 (533), embeddings generator 162 applying model 520 (534) processes the filtered dataset through a vectorization engine to catalog the filtered dataset and store the resultant vectors into a vector database with subsequent metadata (536). This vector database of vectors is depicted in FIG. 5 and described elsewhere in this document as index of embeddings 164 having embeddings. An entry into the vector database can either contain the full file, part of a file, or location of the file along with the embedding itself. Additional metadata may be added, such as file location (for citation purposes supporting a response to a query) and Access Control List (ACL) information”; see also step 415 of Figure 4, and paragraph 0098), the application further associated with communication with a large language model (paragraphs 0076-0077; see also step 420 of Figure 4, and paragraph 0098). Further regarding claim 9, Statton also discloses a processor (e.g. paragraphs 0050, 0054, & 0121) and memory (e.g. paragraphs 0025, 0032, & 0120).
Regarding claims 4, 13, and 18: Statton further discloses wherein, based at least in part on the first subset of files or portions of files comprising sensitive information, no vectors are added to the vector database based at least in part on data associated with the first subset of files or portions of files that comprise sensitive information (paragraph 0071: “…where filter generator 160 generates filter 304 to filter out data that does not align with users' permissions in order to provide responses that do align to users' permissions”).
Regarding claims 5, 14, and 19: Statton further discloses further comprising: generating, by the DMS, one or more second vectors based at least in part on second data from the first snapshot, wherein the second data is from at least some of the first subset of files or portions of files (paragraphs 0103-0104, including: “Embeddings generator 162 may be scalable to additional workloads for new filter selections 550. For example, response generation platform 500 may spawn an additional instance of embeddings generator 162 for computing embeddings for a newly retrieved filtered dataset retrieved by data access layer 504. Database layer 320 (including embeddings 164) may be sharded across multiple instances” and “Database layer 320 is configured to insert new entries of vectors/embeddings into embeddings 164 or be queried with an embedding/vector to return n-entries of approximate nearest neighbor from the submitted embedding”; see also all citations of Statton as cited in the rejection of claims 1, 11, & 16 supra, as the process is repeatable ad infinitum); and adding, by the DMS, the one or more second vectors to a second vector database along with second metadata or a second pointer to the second metadata, wherein the second metadata is associated with the data from the first snapshot, and wherein the second vector database comprises a second knowledge repository that is accessible to a second application associated with the DMS, the second application further associated with communication with the LLM (Ibid).
Regarding claim 6:
Statton further discloses: receiving, by the DMS, configuration information that schedules the DMS to generate first vectors for addition to the vector database and second vectors for addition to the second vector database in association with obtention of snapshots of the computing system, wherein generating the one or more vectors is based at least in part on the configuration information, and wherein generating the one or more second vectors is based at least in part on the configuration information (paragraph 0042: “Response generation platform 158 receives an input indicative of context for queries to response generation platform 158. This input may itself be a query. Filter generator 160 processes the input to determine types of data relevant to queries”; and paragraphs 0066-0072, including “In this example, filter generator 160 applies a machine learning model 306 to analyze input 300 to decode the types of data relevant to the queries. This analysis allows the system to accurately determine the user's intent and tailor the filter 304 accordingly, ensuring that the most relevant data is processed and included in the index of embeddings 164” through “Embeddings generator 162 processes the obtained, filtered text data that matches the generated filter 304 to generate index of embeddings 164”.
Regarding claim 8: Statton further discloses further comprising: receiving, by the DMS, a query for the LLM via the application (paragraphs 0076-0077; see also step 415 of Figure 4, and paragraph 0098); and providing, by the DMS via the application, a response to the query that is based at least in part on the LLM and the one or more vectors that were previously added to the vector database (paragraphs 0106, including: “Index of embeddings 164 that is created can now function and interact with post-processing interactions such as answering user questions (“queries”)”; see also step 420 of Figure 4, and paragraph 0098.
Regarding claims 9, 15, and 20: Statton further discloses further comprising: obtaining, by the DMS and subsequent to obtaining the first snapshot, a second snapshot of the computing system, wherein the second snapshot includes a second set of files that are modified with respect to the first snapshot (paragraphs 0034-0035: “Backups 142A-142K (collectively, “backups 142”) thus represent time series data for file system 153 in that each backup stores a representation of file system 153 at a particular time” and “A snapshot represents a backup state of the primary storage system 105 at a particular point in time. That is, each snapshot provides a state of data of file system 153, which can be restored to the primary storage system 105 if needed”; see also paragraph 0063: “…similar techniques may additionally or alternatively be applied for an archive, replica, mirror/clone, or snapshot functions performed by the data platform. In such cases, backups 142 would be archives, replicas, mirrors/clones, or snapshots, respectively”); determining, by the DMS, from among the second set of files, a third subset of files or portions of files that comprise sensitive information (see all previous citations of Statton as cited in the rejection of claims 1, 5, 6, 11, 14, 16, & 19 supra, as the disclosed process is repeatable ad infinitum); generating, by the DMS, one or more second vectors based at least in part on data associated with a fourth subset of files or portions of files from among the second set of files, the fourth subset of files or portions of files exclusive of files from the third subset of files or portions of files (Ibid); and adding, by the DMS, the one or more second vectors to the vector database along with second metadata or a second pointer to the second metadata, wherein the second metadata is associated with the second snapshot (Ibid).
Regarding claims 10: Statton further discloses wherein each vector of the one or more vectors corresponds to a respective portion of text within a file represented by the first snapshot (paragraph 0072: “Embeddings generator 162 obtains items of filtered text data (e.g., files, emails, text objects, etc.), encodes the items as embeddings, and indexes them to generate index of embeddings 164 (sometimes referred to as an embeddings database)”; see also paragraph 0100: “Embeddings generator 162 uses a vector database (not shown) along with a machine learning model 520 to calculate embeddings 164 for the data. Machine learning model 520 may be a language model. These embeddings 164, along with the data and metadata will be stored in the vector database of database layer 320”.
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.
Claims 2, 3, 7, 12, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Statton as applied to claims 1, 6, 11, & 16 above, and further in view of Sumedrea (U.S. Patent Publication 2025/0005175).
Regarding claims 2, 12, and 17: Statton further discloses: receiving, by the DMS, configuration information for determining that a file comprises sensitive information), wherein determining the first subset of files or portions of files is based at least in part on the filter generated thereby (e.g. paragraphs 0042 & 0066-0072); although Statton is technically silent regarding whether the filter specifically employs one or more rules for this purpose. However, Sumedrea discloses in a related invention for filtering sensitive information such as PII from the dataset(s) of an LLM (e.g. Abstract) wherein the use of one or more regex rules (in conjunction with other techniques) are used for this purpose (e.g. Sumedrea, paragraph 0016: “In an illustrative embodiment, a PII scrubbing management (PSM) system receives a request (e.g., scrubbing request) to process a record that includes data and PII. The PSM system identifies, by a processing device and based on one or more regex rules, a first set of scrubbing candidates associated with the record”). It would have been obvious prior to the effective filing date of the instant application for Statton’s filters to be implemented at least in part via one or more rules as suggested by Sumedrea, as the use of rules for filtering PII was a known option for filtering out at least some types of PII like IP addresses from the LLM’s datasets (Sumedrea, paragraph 0014).
Regarding claim 3:
The combination further discloses wherein the one or more rules are based at least in part on a file type, inclusion of one or more keywords in a file name, inclusion of one or more keywords in text of a file, inclusion of one or more types of data structures in a file, or any combination thereof (Statton, e.g. paragraph 0108; Sumedrea, e.g. paragraph 0026).
Regarding claim 7:
Statton is technically silent regarding wherein: the configuration information indicates one or more first rules for determining that a file comprises sensitive information in association with generating the one or more vectors, and the configuration information indicates one or more second rules for determining that a file comprises sensitive information in association with generating the one or more second vectors [emphasis Examiner’s]. However, Sumedrea discloses in a related invention for filtering sensitive information such as PII from the dataset(s) of an LLM (e.g. Abstract) wherein the use of one or more regex rules (in conjunction with other techniques) are used for this purpose (e.g. Sumedrea, paragraph 0016: “In an illustrative embodiment, a PII scrubbing management (PSM) system receives a request (e.g., scrubbing request) to process a record that includes data and PII. The PSM system identifies, by a processing device and based on one or more regex rules, a first set of scrubbing candidates associated with the record”). It would have been obvious prior to the effective filing date of the instant application for Statton’s filters to be implemented as one or more rules as suggested by Sumedrea, as the use of rules for filtering PII was a known option for filtering out at least some types of PII like IP addresses from the datasets (Sumedrea, paragraph 0014).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: U.S. Patent Publications 2026/0147788 (Krull), 2026/0030203 (Vajgel), and 2025/0139088 (Weik).
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THOMAS A. GYORFI
Examiner
Art Unit 2435
/THOMAS A GYORFI/Examiner, Art Unit 2435 5/29/2026
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