DETAILED 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 .
Response to Amendment
Applicant’s Amendments, filed December 2, 2025, have been entered. Claims 1, 8 and 15 have been amended, and claims 1-20 are currently pending.
Response to Arguments
Applicant’s arguments, see Remarks p. 8, filed December 2, 2025, with respect to the rejections of claims 1-20 under 35 U.S.C. 101 have been fully considered and are persuasive. Therefore, the 35 U.S.C. 101 rejection of claims 1-20 has been withdrawn.
Applicant’s arguments, see Remarks p. 9, filed December 2, 2025, with respect to the rejections of claims 1-20 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Fatal et al. (Pub. No. US 2025/0291836 A1, hereinafter “Fatal”) and Morales (Pub. No. US 2025/0036291 A1, hereinafter “Morales”).
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 factual inquiries 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 4, 6, 7, 8, 11, 13, 14, 15, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Fatal in view of Morales in view of Rapoport et al. (Patent No. US 9,584,395 B1, hereinafter “Rapoport”).
Regarding claim 1, Fatal teaches:
receiving by a sustainable storage program a dataset comprising raw data and one or more associated tags that give context to an embedding model; transforming by the embedding model, the received raw data and the one or more associated tags, into vector embeddings (Fatal – see [0043], where the tag mapper is generally responsible for receiving and processing real-time raw data from the enterprise data source(s) by automatically mapping the real-time raw data to the one or more tags generated by the tag generator and/or one or more new tags. The tag mapper may use or represent a machine learning or other model (i.e. embedding mode) that tokenizes, and numerically embeds (e.g., in vectors, i.e. vector embeddings) the raw data to classify which tag the real-time raw data belongs to within the tag(s).)
and continuously updating and monitoring the embedding model [and vector database], based on receiving additional raw data and one or more additional associated tags (Fatal – see [0043], where the tag mapper is generally responsible for receiving and processing real-time raw data from the enterprise data source(s) by automatically mapping the real-time raw data to the one or more tags generated by the tag generator and/or one or more new tags. The tag mapper may use or represent a machine learning or other model (i.e. embedding mode) that tokenizes, and numerically embeds (e.g., in vectors, i.e. vector embeddings) the raw data to classify which tag the real-time raw data belongs to within the tag(s).)
Fatal does not appear to teach:
updating, by the sustainable storage program, a vector database, using the vector embeddings.
assigning incoming data to a storage tier
based on a similarity search of the vector database, wherein the similarity measures a proximity or distance of two vectors in the vector database
and the vector database
However, Morales teaches:
updating, by the sustainable storage program, a vector database, using the vector embeddings (Morales – see [0245], where the method of Fig. 4 includes storing, in the object store (i.e. vector database), the data object and the vector embedding.)
based on a similarity search of the vector database, wherein the similarity measures a proximity or distance of two vectors in the vector database (Morales – see [0264], where one or more vector embeddings may be selected from the plurality of vector embeddings based on their distance relative to the other vector embedding based on the query. The distance between any two vector embeddings may be calculated as a distance between two points in a multidimensional space. For example, the distance between any two vector embeddings may be calculated as a Euclidian distance or according to another approach for calculating distances between multidimensional points can be appreciated. Also see Applicant’s Specification [0016], where a storage tier can be identified for each cluster. To perform similarity search and retrieval in a vector database, a query vector represents the search criteria, such as similarity of vectorized data to be written to a storage device to an existing closest vector, and therefore, best tier for data storage. A similarity measure calculates how close or distance two vectors in the vector space.)
and the vector database (Morales – see [0245], where the method of Fig. 4 includes storing, in the object store (i.e. vector database), the data object and the vector embedding.)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Fatal and Morales before them, to modify the system of Fatal with the teachings of Morales, as indicated above. One would have been motivated to make such a modification to store and retrieve data using vector embeddings (Morales - [Abstract]).
Fatal modified by Morales does not appear to teach:
assigning incoming data to a storage tier
However, Rapoport teaches:
assigning incoming data to a storage tier (Rapoport – at block 301, the storage controller computer determines which storage tiers within the storage system to store the one or more metric records. The storage controller computer stores policy data that specifies the rules by which the storage controller computer maintains data between storage tier 1, storage tier 2, and storage tier 3. Thus, the rules may specify that metric records pertaining to a particular metric label or whose key/value pairs meet a particular criterion should be stored in a particular storage tier [Col. 9 lines 16-34].)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Fatal, Morales and Rapoport before them, to modify the system of Fatal and Morales with the teachings of Rapoport, as indicated above. One would have been motivated to make such a modification to grapple with the issue of how to efficiently store massive amounts of metric data using a finite set of resources (Rapoport [Col. 1 lines 33-35]).
Claims 8 and 15 correspond to claim 1 and are rejected accordingly.
Regarding claim 4, Fatal teaches:
wherein new data is tagged to identify a cluster to which it belongs within a model based on a distance to each existing cluster (Fatal – see [0047], where machine learning models, like clustering algorithms or classification models, can learn patterns in data and group similar data points indicated in the real-time raw data together with those found in the registration raw data. These models can thus help identify and map similar raw data to the same tag or different tag.)
Claims 11 and 18 correspond to claim 4 and are rejected accordingly.
Regarding claim 6, Fatal teaches:
wherein a data type is tagged using supervised, semi-supervised, or unsupervised learning (Fatal – see [0064], where neural network generates one or more tags and represents suitable model functionality, such as supervised, unsupervised and semi-supervised learning.)
Claims 13 corresponds to claim 6 and are rejected accordingly.
Regarding claim 7, Fatal teaches:
receiving, by a channel subsystem, a command to write data; the sustainable storage program causing the channel subsystem to vectorize the data to write (Fatal – see [0043], where the tag mapper is generally responsible for receiving and processing real-time raw data from the enterprise data source(s) by automatically mapping the real-time raw data to the one or more tags generated by the tag generator and/or one or more new tags. The tag mapper may use or represent a machine learning or other model (i.e. embedding mode) that tokenizes, and numerically embeds (e.g., in vectors, i.e. vector embeddings) the raw data to classify which tag the real-time raw data belongs to within the tag(s).)
and updating the vector database (Fatal – see [0043], where the tag mapper is generally responsible for receiving and processing real-time raw data from the enterprise data source(s) by automatically mapping the real-time raw data to the one or more tags generated by the tag generator and/or one or more new tags. The tag mapper may use or represent a machine learning or other model (i.e. embedding mode) that tokenizes, and numerically embeds (e.g., in vectors, i.e. vector embeddings) the raw data to classify which tag the real-time raw data belongs to within the tag(s).)
Fatal does not appear to teach:
the sustainable storage program causing the channel subsystem to locate a closest vector in the vector database
the sustainable storage program causing the channel subsystem to determine a storage classification tier
storing the data to write at the determined storage classification tier
However, Morales teaches:
the sustainable storage program causing the channel subsystem to locate a closest vector in the vector database (Morales – see [0264], where one or more vector embeddings may be selected from the plurality of vector embeddings based on their distance relative to the other vector embedding based on the query. The distance between any two vector embeddings may be calculated as a distance between two points in a multidimensional space. For example, the distance between any two vector embeddings may be calculated as a Euclidian distance or according to another approach for calculating distances between multidimensional points can be appreciated. Also see Applicant’s Specification [0016], where a storage tier can be identified for each cluster. To perform similarity search and retrieval in a vector database, a query vector represents the search criteria, such as similarity of vectorized data to be written to a storage device to an existing closest vector, and therefore, best tier for data storage. A similarity measure calculates how close or distance two vectors in the vector space.)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Fatal and Morales before them, to modify the system of Fatal with the teachings of Morales, as indicated above. One would have been motivated to make such a modification to store and retrieve data using vector embeddings (Morales - [Abstract]).
Fatal modified by Morales does not appear to teach:
the sustainable storage program causing the channel subsystem to determine a storage classification tier
storing the data to write at the determined storage classification tier
However, Rapoport teaches:
the sustainable storage program causing the channel subsystem to determine a storage classification tier (Rapoport – at block 301, the storage controller computer determines which storage tiers within the storage system to store the one or more metric records. The storage controller computer stores policy data that specifies the rules by which the storage controller computer maintains data between storage tier 1, storage tier 2, and storage tier 3. Thus, the rules may specify that metric records pertaining to a particular metric label or whose key/value pairs meet a particular criterion should be stored in a particular storage tier [Col. 9 lines 16-34].)
storing the data to write at the determined storage classification tier; (Rapoport - the storage controller computer may execute database software configured to store the metrics in the storage system as an object-oriented (i.e. vector database) or relational database [Col. 6 lines 36-39].)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Fatal, Morales and Rapoport before them, to modify the system of Fatal and Morales with the teachings of Rapoport, as indicated above. One would have been motivated to make such a modification to grapple with the issue of how to efficiently store massive amounts of metric data using a finite set of resources (Rapoport [Col. 1 lines 33-35]).
Claims 14 and 20 correspond to claim 7 and are rejected accordingly.
Claims 2-3, 5, 9-10, 12 and 16-17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Fatal in view of Morales in view of Rapoport in view of Ganesan et al. (Pub. No. US 2022/0269555 A1, hereinafter “Ganesan”).
Regarding claim 2, Fatal modified by Morales and Rapoport does not appear to teach:
wherein the data context includes: origin of the data; transaction type; level of sensitivity; encryption requirements
However, Ganesan teaches:
wherein the data context includes: origin of the data; transaction type; level of sensitivity; encryption requirements (Ganesan – illustrative metrics can be characterized as “determinants” and “variables” [0052]. In Fig. 2A, step 202, data is periodically collected for both types of system metrics, i.e., determinants and variables. The data for determinants include power consumption, and I/O performance (i.e. transaction type), and data for variables such as controller queue size, predictive failure, number of bad blocks, inverse of disk fragmentation, inverse disk usage ratio, polling frequency of a foreign configuration, PERC parameters (i.e. origin of data, level of sensitivity, encryption requirements), busy-ness, QoS parameters, network efficiency, are collectable from one or more components of information processing system such as host devices, storage array, storage devices and storage controllers [0058].)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Fatal, Morales, Rapoport and Ganesan before them, to modify the system of Fatal, Morales and Rapoport with the teachings of Ganesan, as indicated above. One would have been motivated to make such a modification to improve performance of the system and/or protect the system against malicious activity (Ganesan [0003]).
Claims 9 and 16 correspond to claim 2 and are rejected accordingly.
Regarding claim 3, Fatal modified by Morales and Rapoport does not appear to teach:
wherein an optionally defined custom tag is set on one or more data types or data clusters to cause storage in at least one tier lower than the sustainable storage program determines
However, Ganesan teaches:
wherein an optionally defined custom tag is set on one or more data types or data clusters to cause storage in at least one tier lower than the sustainable storage program determines (Ganesan – in some embodiments, when there are mixed rows (some with average values and some with other values), a mixed set can be created based on a whether or not there is a sufficient number of rows in the data. The number of rows that constitute a sufficient number can be preset by an administrator and/or a system [0063].)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Fatal, Morales, Rapoport and Ganesan before them, to modify the system of Fatal, Morales and Rapoport with the teachings of Ganesan, as indicated above. One would have been motivated to make such a modification to improve performance of the system and/or protect the system against malicious activity (Ganesan [0003]).
Claims 10 and 17 correspond to claim 3 and are rejected accordingly.
Regarding claim 5, Fatal modified by Morales and Rapoport does not appear to teach:
wherein an entire data cluster automatically moves tiers up or down in response to access statistics and configurable thresholds
However, Ganesan teaches:
wherein an entire data cluster automatically moves tiers up or down in response to access statistics and configurable thresholds (Ganesan – in step 212, remediation, or at least partial remediation, can be provided here when an appropriate action can be identified, by way of example only, some actions associated with tuning storage and/or network modules for selective out-of-range values. For example, actions may comprise adjusting one or more variables based on their tagged usage bands (i.e. threshold) such as adjusting the polling frequency of the foreign configuration, increasing the controller queue size, increasing buffers, adjusting kernel parameters, increasing memory, or halting certain processes [0067].)
Accordingly, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed, having the teachings of Fatal, Morales, Rapoport and Ganesan before them, to modify the system of Fatal, Morales and Rapoport with the teachings of Ganesan, as indicated above. One would have been motivated to make such a modification to improve performance of the system and/or protect the system against malicious activity (Ganesan [0003]).
Claims 12 and 19 correspond to claim 5 and are rejected accordingly.
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 RANJIT P DORAISWAMY whose telephone number is (571)270-5759. The examiner can normally be reached Monday-Friday 9:00 AM - 5:00 PM.
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/RANJIT P DORAISWAMY/ Examiner, Art Unit 2166
/SANJIV SHAH/ Supervisory Patent Examiner, Art Unit 2166
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