DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 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.
This Office action is in response to communications dated 1/30/2025.
Claims 1-20 are pending.
Claims 1-20 are rejected.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/15/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. Independent claim 1 recites “…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 base repository that is accessible to an application associated with the DMS, the application further associated with communication with a large language model…” (independent claim 1, lines 6-10). The Examiner is uncertain whether this recitation means any of the following:
both of “the one or more vectors” and “metadata” are added “to a vector database” or a pointer to the metadata is added “to a vector database”;
“the one or more vectors” are added to “a vector database,” and either “metadata” or “a pointer to the metadata” is added to the vector database”; or
some other unconsidered interpretation.
For the sake of examination, the Examiner has interpreted “…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 base repository that is accessible to an application associated with the DMS, the application further associated with communication with a large language model…” to read “…adding, by the DMS, the one or more vectors and either metadata or a pointer to the metadata to a vector database, wherein the metadata is associated with the data from the first snapshot, and wherein the vector database comprises a knowledge base repository that is accessible to an application associated with the DMS, the application associated with the DMS further associated with communication with a large language model…” Dependent claims 2-9, which ultimately depend from independent claim 1, are rejected for carrying the same deficiency.
Claims 11-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. Independent claim 11 recites “…add, 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 base repository that is accessible to an application associated with the DMS, the application further associated with communication with a large language model…” (independent claim 11, lines 9-13). The Examiner is uncertain whether this recitation means any of the following:
both of “the one or more vectors” and “metadata” are added “to a vector database” or a pointer to the metadata is added “to a vector database”;
“the one or more vectors” are added to “a vector database,” and either “metadata” or “a pointer to the metadata” is added to the vector database”; or
some other unconsidered interpretation.
For the sake of examination, the Examiner has interpreted “…add, 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 base repository that is accessible to an application associated with the DMS, the application further associated with communication with a large language model…” to read “…add, by the DMS, the one or more vectors and either metadata or a pointer to the metadata to a vector database, wherein the metadata is associated with the data from the first snapshot, and wherein the vector database comprises a knowledge base repository that is accessible to an application associated with the DMS, the application associated with the DMS further associated with communication with a large language model…” Dependent claims 12-15, which ultimately depend from independent claim 11, are rejected for carrying the same deficiency.
Claims 16-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. Independent claim 16 recites “…add, 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 base repository that is accessible to an application associated with the DMS, the application further associated with communication with a large language model…” (independent claim 16, lines 7-11). The Examiner is uncertain whether this recitation means any of the following:
both of “the one or more vectors” and “metadata” are added “to a vector database” or a pointer to the metadata is added “to a vector database”;
“the one or more vectors” are added to “a vector database,” and either “metadata” or “a pointer to the metadata” is added to the vector database”; or
some other unconsidered interpretation.
For the sake of examination, the Examiner has interpreted “…add, 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 base repository that is accessible to an application associated with the DMS, the application further associated with communication with a large language model…” to read “…add, by the DMS, the one or more vectors and either metadata or a pointer to the metadata to a vector database, wherein the metadata is associated with the data from the first snapshot, and wherein the vector database comprises a knowledge base repository that is accessible to an application associated with the DMS, the application associated with the DMS further associated with communication with a large language model…” Dependent claims 17-20, which ultimately depend from independent claim 16, are rejected for carrying the same deficiency.
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(a) 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, 11, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over USPGPUB 2017/0237792 (“Taghavi”) in view of USPGPUB 2024/0168978 (“Goel”) and further in view of USPGPUB 2025/0086391 (“Kempf”).
As per claim 1, Taghavi substantially teaches a method (Taghavi, Abstract), comprising:
obtaining, by a data management (DMS), a first snapshot of a computing system; generating, by the DMS, one or more vectors based at least in part on data from the first snapshot; and adding, by the DMS, the one or more vectors to a vector database. and wherein the vector database comprises a knowledge repository that is accessible to an application associated with the DMS: (Taghavi, Abstract; FIG. 4; and paragraphs 0016-0020 and 0044-0049, where a snapshot of user data from a media streaming platform (i.e., a computer system) may be collected (i.e., obtained) and then encoded into a feature vector by a feature encoder prior to being stored to a database of feature vectors (i.e., a vector database) that is provided to a machine-learning model. The Examiner notes that the database of feature vectors is based on knowledge of user data, so the database of feature vectors is a knowledge repository that is accessible to a machine-learning model (i.e., an application associated with the system of Taghavi). Taghavi therefore substantially teaches obtaining, by a data management (DMS), a first snapshot of a computing system; generating, by the DMS, one or more vectors based at least in part on data from the first snapshot; and adding, by the DMS, the one or more vectors to a vector database. and wherein the vector database comprises a knowledge repository that is accessible to an application associated with the DMS).
Taghavi does not appear to explicitly teach the other limitations of this claim beyond those taught above; however, in an analogous art, Goel teaches system and method to implement a scalable vector database.
As per claim 1, Goel particularly teaches:
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: (Goel, Abstract; Figure 6, reference numeral 608; and paragraphs 0032 and 0061-0068, where the system of Goel may include an intermediate storage that stores a vector database that comprises metadata about an index of the vector database and metadata that is a snapshot of tenant information. Goel therefore particularly teaches 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)
It would have been obvious to a person having ordinary skill in the art, having the teachings of Goel and Taghavi before them before the instant application was effectively filed, to modify the invention of Taghavi to include the principles of Goel of using a scalable vector database to store metadata information.
The modification would have been obvious because a person having ordinary skill in the art would be motivated to increase system performance by implementing techniques for vector database management that enable efficient and reliable nearest-neighbor searches and index references (Goel, paragraph 0016).
Neither Taghavi nor Goel appears to explicitly teach the other limitations of this claim beyond those taught above; however, in an analogous art, Kempf teaches techniques for using generative artificial intelligence to formulate search answers.
As per claim 1, Kempf particularly teaches:
the application further associated with communication with a large language model (LLM): (Kempf, Abstract; FIG. 13; and paragraphs 0016 and 0108-0113, where the system of Kempf uses an LLM to summarize output information for a user. This means that the LLM is associated with operations within the system of Kempf. Kempf therefore particularly teaches the application further associated with communication with a large language model (LLM)).
It would have been obvious to a person having ordinary skill in the art, having the teachings of Kempf, Goel, and Taghavi before them before the instant application was effectively filed, to modify the combination of Goel with Taghavi to include the principles of Kempf of utilizing an LLM.
The modification would have been obvious because a person having ordinary skill in the art would be motivated to increase system flexibility by implementing use of an LLM to generate and summarize information using language (e.g., from a knowledge base) that is familiar to users (Kempf, paragraph 0046).
As per claim 11, an apparatus, comprising:
one or more memories storing processor-executable code; and the one or more processors coupled with the one or more memories and individually or collectively operable to execute the code to cause the apparatus to: obtain, by a data management system (DMS), a first snapshot of a computing system; generate, by the DMS, one or more vectors based at least in part on data from the first snapshot; and add, by the DMS, the one or more vectors to a vector database, and wherein the vector database comprises a knowledge repository that is accessible to an application associated with the DMS: (Taghavi, Abstract; FIG. 4; and paragraphs 0016-0020 and 0044-0049, where a snapshot of user data from a media streaming platform (i.e., a computer system) may be collected (i.e., obtained) and then encoded into a feature vector by a feature encoder prior to being stored to a database of feature vectors (i.e., a vector database) that is provided to a machine-learning model. The Examiner notes that the database of feature vectors is based on knowledge of user data, so the database of feature vectors is a knowledge repository that is accessible to a machine-learning model (i.e., an application associated with the system of Taghavi). The Examiner notes that the use of a database of feature vectors generated by vector encoders means that the system of Taghavi must comprise memory to store executable code for encoding features and one or more processors to execute the executable code to encode the features. Taghavi therefore substantially teaches one or more memories storing processor-executable code; and the one or more processors coupled with the one or more memories and individually or collectively operable to execute the code to cause the apparatus to: obtain, by a data management system (DMS), a first snapshot of a computing system; generate, by the DMS, one or more vectors based at least in part on data from the first snapshot; and add, by the DMS, the one or more vectors to a vector database, and wherein the vector database comprises a knowledge repository that is accessible to an application associated with the DMS).
Taghavi does not appear to explicitly teach the other limitations of this claim beyond those taught above; however, in an analogous art, Goel teaches system and method to implement a scalable vector database.
As per claim 11, Goel particularly teaches:
and add, 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: (Goel, Abstract; Figure 6, reference numeral 608; and paragraphs 0032 and 0061-0068, where the system of Goel may include an intermediate storage that stores a vector database that comprises metadata about an index of the vector database and metadata that is a snapshot of tenant information. Goel therefore particularly teaches and add, 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).
It would have been obvious to a person having ordinary skill in the art, having the teachings of Goel and Taghavi before them before the instant application was effectively filed, to modify the invention of Taghavi to include the principles of Goel of using a scalable vector database to store metadata information.
The modification would have been obvious because a person having ordinary skill in the art would be motivated to increase system performance by implementing techniques for vector database management that enable efficient and reliable nearest-neighbor searches and index references (Goel, paragraph 0016).
Neither Taghavi nor Goel appears to explicitly teach the other limitations of this claim beyond those taught above; however, in an analogous art, Kempf teaches techniques for using generative artificial intelligence to formulate search answers.
As per claim 11, Kempf particularly teaches:
the application further associated with communication with a large language model (LLM): (Kempf, Abstract; FIG. 13; and paragraphs 0016 and 0108-0113, where the system of Kempf uses an LLM to summarize output information for a user. This means that the LLM is associated with operations within the system of Kempf. Kempf therefore particularly teaches the application further associated with communication with a large language model (LLM)).
It would have been obvious to a person having ordinary skill in the art, having the teachings of Kempf, Goel, and Taghavi before them before the instant application was effectively filed, to modify the combination of Goel with Taghavi to include the principles of Kempf of utilizing an LLM.
The modification would have been obvious because a person having ordinary skill in the art would be motivated to increase system flexibility by implementing use of an LLM to generate and summarize information using language (e.g., from a knowledge base) that is familiar to users (Kempf, paragraph 0046).
As per claim 16, Taghavi substantially teaches a non-transitory computer-readable medium storing code, the code comprising instructions executable by one or more processors to:
obtain, by a data management system (DMS), a first snapshot of a computing system associated with the DMS; generate, by the DMS, one or more vectors based at least in part on data from the first snapshot; and add, by the DMS, the one or more vectors to a vector database, and wherein the vector database comprises a knowledge repository that is accessible to an application associated with the DMS: (Taghavi, Abstract; FIG. 4; and paragraphs 0016-0020 and 0044-0049, where a snapshot of user data from a media streaming platform (i.e., a computer system) may be collected (i.e., obtained) and then encoded into a feature vector by a feature encoder prior to being stored to a database of feature vectors (i.e., a vector database) that is provided to a machine-learning model. The Examiner notes that the database of feature vectors is based on knowledge of user data, so the database of feature vectors is a knowledge repository that is accessible to a machine-learning model (i.e., an application associated with the system of Taghavi). Taghavi therefore substantially teaches obtain, by a data management system (DMS), a first snapshot of a computing system associated with the DMS; generate, by the DMS, one or more vectors based at least in part on data from the first snapshot; and add, by the DMS, the one or more vectors to a vector database, and wherein the vector database comprises a knowledge repository that is accessible to an application associated with the DMS).
Taghavi does not appear to explicitly teach the other limitations of this claim beyond those taught above; however, in an analogous art, Goel teaches system and method to implement a scalable vector database.
As per claim 16, Goel particularly teaches:
and add, by the DMS, the one or more vectors to a vector database along with metadata of a pointer to the metadata, wherein the metadata is associated with the data from the first snapshot: (Goel, Abstract; Figure 6, reference numeral 608; and paragraphs 0032 and 0061-0068, where the system of Goel may include an intermediate storage that stores a vector database that comprises metadata about an index of the vector database and metadata that is a snapshot of tenant information. Goel therefore particularly teaches and add, by the DMS, the one or more vectors to a vector database along with metadata of a pointer to the metadata, wherein the metadata is associated with the data from the first snapshot).
It would have been obvious to a person having ordinary skill in the art, having the teachings of Goel and Taghavi before them before the instant application was effectively filed, to modify the invention of Taghavi to include the principles of Goel of using a scalable vector database to store metadata information.
The modification would have been obvious because a person having ordinary skill in the art would be motivated to increase system performance by implementing techniques for vector database management that enable efficient and reliable nearest-neighbor searches and index references (Goel, paragraph 0016).
Neither Taghavi nor Goel appears to explicitly teach the other limitations of this claim beyond those taught above; however, in an analogous art, Kempf teaches techniques for using generative artificial intelligence to formulate search answers.
As per claim 16, Kempf particularly teaches:
the application further associated with communication with a large language model (LLM): (Kempf, Abstract; FIG. 13; and paragraphs 0016 and 0108-0113, where the system of Kempf uses an LLM to summarize output information for a user. This means that the LLM is associated with operations within the system of Kempf. Kempf therefore particularly teaches the application further associated with communication with a large language model (LLM)).
It would have been obvious to a person having ordinary skill in the art, having the teachings of Kempf, Goel, and Taghavi before them before the instant application was effectively filed, to modify the combination of Goel with Taghavi to include the principles of Kempf of utilizing an LLM.
The modification would have been obvious because a person having ordinary skill in the art would be motivated to increase system flexibility by implementing use of an LLM to generate and summarize information using language (e.g., from a knowledge base) that is familiar to users (Kempf, paragraph 0046).
Conclusion
The following prior art is made of record and is not relied upon for any rejection but is considered pertinent to Applicant's disclosure:
Great Britain Patent Publication GB 2639568: teaches using a neural network LLM to perform queries.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Daniel C. Chappell whose telephone number is (571)272-5003. The examiner can normally be reached 1000-1800, Eastern.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jared I. Rutz can be reached at (571)272-5535. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Daniel C. Chappell
Primary Examiner
Art Unit 2135
/Daniel C. Chappell/Primary Examiner, Art Unit 2135