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 .
Claims 1-20 are presented for examination.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
The abstract of the disclosure is objected to because it contains fewer than 50 words. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
The use of the terms AMAZON (paragraphs 75-76) and MICROSOFT (paragraphs 75-76), which are trade names or marks used in commerce, has been noted in this application. The terms should be accompanied by the generic terminology; furthermore, the terms should be capitalized wherever they appear or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the terms.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) is permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Claim Objections
Claim 9 is objected to because of the following informalities: “data is” should be “data are”.
Claim 16 is objected to because of the following informalities: “at least on processor” should be “at least one processor”. Claims 17-20 are objected to for dependency on claim 16.
Appropriate correction is required.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
Claim 1
Step 1: The claim recites a method; therefore, it is directed to the statutory category of processes.
Step 2A Prong 1: The claim recites, inter alia, “permuting … the embedding vector to generate a permuted embedding vector”. This limitation could encompass mentally permuting the vector; permutation is also a mathematical concept.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further requires that the permutation be performed “by the computing system”. However, this limitation amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f). The claim further recites “receiving, by a computing system, an embedding vector associated with first data” and “providing, by the computing system, the permuted embedding vector to a vector database”. However, these limitations are directed to the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of step 2A, prong 2, except insofar as the receiving and providing limitations, in addition to being insignificant extra-solution activity, also recite the well-understood, routine, and conventional activity of storing and retrieving information in memory. MPEP § 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). As an ordered whole, the claim is directed to a mentally performable method of permuting vectors. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 2
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia, that “the permuted embedding vector is associated with content from a knowledge store”. Permuting the vector remains a mental process/mathematical concept under these further assumptions.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “providing the permuted embedding vector to be maintained in the vector database.” However, this limitation is directed to the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites “providing the permuted embedding vector to be maintained in the vector database.” However, this limitation is directed to the well-understood, routine, and conventional activity of storing and retrieving information in memory. MPEP § 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Claim 3
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia, that “the permuted embedding vector is associated with a query”. Permuting the embedding vector remains a mental process/mathematical concept under these further assumptions.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “providing the permuted embedding vector for a search of the vector database.” However, this limitation is directed to the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites “providing the permuted embedding vector for a search of the vector database.” However, this limitation is directed to the well-understood, routine, and conventional activity of storing and retrieving information in memory. MPEP § 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Claim 4
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia, that “embedding vectors associated with content from a knowledge store and an embedding vector associated with a query are permuted in the same manner based on [an] acquired seed.” Permuting several vectors in the same way according to data such as a seed is a mentally performable mathematical concept.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “acquiring a seed of a plurality of seeds, each seed associated with a corresponding permutation”. However, this limitation is directed to the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites “acquiring a seed of a plurality of seeds, each seed associated with a corresponding permutation”. However, this limitation is directed to the well-understood, routine, and conventional activity of receiving and transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Claim 5
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia, “encrypting the acquired seed”. In the absence of further elucidation, this limitation could encompass mentally encrypting the seed.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “storing the encrypted acquired seed independently from the vector database.” However, this limitation is directed to the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites “storing the encrypted acquired seed independently from the vector database.” However, this limitation is directed to the well-understood, routine, and conventional activity of storing and retrieving information in memory. MPEP § 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Claim 6
Step 1: A process, as above.
Step 2A Prong 1: The claim recites that “the acquired seed is randomly generated.” Encrypting a randomly generated seed remains mentally performable.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. See claim 4 analysis.
Step 2B: The claim does not contain significantly more than the judicial exception. See claim 4 analysis.
Claim 7
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia, that “a permutation associated with a seed is applied to embedding vectors associated with the first data and the second data.” This limitation could encompass mentally permuting the vectors associated with data based on a seed; permutation is also a mathematical concept.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that “the first data and second data are associated with at least one of different accounts, different domains, or different chatbots”. The receiving of the vector associated with the data remains insignificant extra-solution activity under these further assumptions.
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that “the first data and second data are associated with at least one of different accounts, different domains, or different chatbots”. The receiving of the vector associated with the data remains well-understood, routine, and conventional activity under these further assumptions.
Claim 8
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia, that “a first permutation associated with a first seed is applied to embedding vectors associated with the first data, and a second permutation associated with a second seed is applied to embedding vectors associated with the second data.” This limitation could encompass mentally permuting the vectors associated with data based on a seed; permutation is also a mathematical concept.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that “the first data and second data are associated with at least one of different accounts, different domains, or different chatbots”. The receiving of the vector associated with the data remains insignificant extra-solution activity under these further assumptions.
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that “the first data and second data are associated with at least one of different accounts, different domains, or different chatbots”. The receiving of the vector associated with the data remains well-understood, routine, and conventional activity under these further assumptions.
Claim 9
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the same judicial exceptions as in claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that “the first data [are] associated with at least one of textual information, visual information, or audio information.” The receiving of the vector associated with the data remains insignificant extra-solution activity under these further assumptions.
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that “the first data [are] associated with at least one of textual information, visual information, or audio information.” The receiving of the vector associated with the data remains well-understood, routine, and conventional activity under these further assumptions.
Claim 10
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia, “determining metadata associated with a resulting permuted embedding vector from the vector database that is responsive to a query; [and] determining content associated with the resulting permuted embedding vector based on the metadata”. These two determinations could be performed mentally.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “utilizing the content in a prompt for provision to a large language model.” This limitation merely limits the field of use of the judicial exception to LLMs. MPEP § 2106.05(h).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites “utilizing the content in a prompt for provision to a large language model.” This limitation merely limits the field of use of the judicial exception to LLMs. MPEP § 2106.05(h).
Claims 11-15
Step 1: The claims recite a system comprising a processor and a memory; therefore, they are directed to the statutory category of machines.
Step 2A Prong 1: The claims recite the same judicial exceptions as in claims 1-5, respectively.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The analysis at this step mirrors that of claims 1-5, respectively, except insofar as these claims additionally recite a “system comprising: at least one processor; and a memory storing instructions that, when executed by the at least one data processor, cause the system to perform [the method]”. However, this amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of claims 1-5, respectively, except insofar as these claims additionally recite a “system comprising: at least one processor; and a memory storing instructions that, when executed by the at least one data processor, cause the system to perform [the method]”. However, this amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Claims 16-20
Step 1: The claims recite a non-transitory computer-readable storage medium; therefore, they are directed to the statutory category of articles of manufacture.
Step 2A Prong 1: The claims recite the same judicial exceptions as in claims 1-5, respectively.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The analysis at this step mirrors that of claims 1-5, respectively, except insofar as these claims additionally recite a “non-transitory computer-readable storage medium including instructions that, when executed by at least [one] processor of a computing system, cause the computing system to perform [the method]”. However, this amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of claims 1-5, respectively, except insofar as these claims additionally recite a “non-transitory computer-readable storage medium including instructions that, when executed by at least [one] processor of a computing system, cause the computing system to perform [the method]”. However, this amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Claim Rejections - 35 USC § 102
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 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 1-3, 9, 11-13, and 16-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kutt et al. (US 20240160914) (“Kutt”).
Regarding claim 1, Kutt discloses “[a] computer-implemented method (see Kutt Fig. 8, processor 801 and memory 807) comprising:
receiving, by a computing system, an embedding vector associated with first data (Kutt Fig. 2 shows embedding vectors 206 [associated with data] being retrieved from embedding vector database 212 and sent to natural language processor 203);
permuting, by the computing system, the embedding vector to generate a permuted embedding vector (Kutt paragraph 21 discloses that a scramble transformation randomly selects indices of embedding vectors and randomly scrambles their order, e.g., by choosing a permutation uniformly at random); and
providing, by the computing system, the permuted embedding vector to a vector database (Kutt Fig. 2 and paragraph 42 disclose that the embedding vectors are stored in embedding vector database 212).”
Claim 11 is a system claim corresponding to method claim 1 and is rejected for the same reasons as given in the rejection of that claim. Similarly, claim 16 is a system claim corresponding to method claim 1 and is rejected for the same reasons as given in the rejection of that claim.
Regarding claim 2, Kutt discloses that “the permuted embedding vector is associated with content from a knowledge store (public data loss prevention (DLP) database [knowledge store] comprises a database of DLP samples [content] with known/ground truth labels that are publicly available; these samples comprise text documents; the trainer receives samples and generates embeddings that convert the text documents to embedding vectors – Kutt, paragraph 18; see also paragraph 21 (describing the permutation of the embedding vectors)), the method further comprising:
providing the permuted embedding vector to be maintained in the vector database (Kutt Fig. 2 and paragraph 42 disclose that the embedding vectors are stored in embedding vector database 212; see also paragraph 21 (describing the permutation of the embedding vectors)).”
Claim 12 is a system claim corresponding to method claim 2 and is rejected for the same reasons as given in the rejection of that claim. Similarly, claim 17 is a system claim corresponding to method claim 2 and is rejected for the same reasons as given in the rejection of that claim.
Regarding claim 3, Kutt discloses that “the permuted embedding vector is associated with a query (natural language processor receives token identifiers and queries the embedding vector database with a token identifier query indicating the token identifiers – Kutt, paragraph 42; see also paragraph 21 (describing the permutation of the embedding vectors)), the method further comprising:
providing the permuted embedding vector for a search of the vector database (natural language processor receives token identifiers and queries [searches] the embedding vector database with a token identifier query indicating the token identifiers; the embedding vector database returns corresponding vectors corresponding to the token identifiers – Kutt, paragraph 42; see also Fig. 2 and paragraph 42 (disclosing that the embedding vectors are stored in embedding vector database 212), paragraph 21 (describing the permutation of the embedding vectors)).”
Claim 13 is a system claim corresponding to method claim 3 and is rejected for the same reasons as given in the rejection of that claim. Similarly, claim 18 is a system claim corresponding to method claim 3 and is rejected for the same reasons as given in the rejection of that claim.
Regarding claim 9, Kutt discloses that “the first data is associated with at least one of textual information, visual information, or audio information (trainer receives samples and generates embeddings that convert the text documents to embedding vectors [i.e., the vectors are associated with textual first data] – Kutt, paragraph 18).”
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.
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 4, 6, 14, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kutt in view of Derakhshani et al. (US 20220398262) (“Derakhshani”).
Regarding claim 4, the rejection of claim 1 is incorporated. Kutt further discloses that “embedding vectors associated with content from a knowledge store and an embedding vector associated with a query are permuted in the same manner (public data loss prevention (DLP) database [knowledge store] comprises a database of DLP samples [content] with known/ground truth labels that are publicly available; these samples comprise text documents; the trainer receives samples and generates embeddings that convert the text documents to embedding vectors – Kutt, paragraph 18; natural language processor receives token identifiers and queries the embedding vector database with a token identifier query indicating the token identifiers – id. at paragraph 42; see also paragraph 21 (describing the permutation of the embedding vectors; note that, since only one method of permutation is disclosed, it may be inferred that both the content-related embedding vectors and the query-related embedding vectors are permuted using that method)) ….”
Kutt appears not to disclose explicitly the further limitations of the claim. However, Derakhshani discloses “acquiring a seed of a plurality of seeds, each seed associated with a corresponding permutation, wherein [data] … are permuted … based on the acquired seed (in permuted MNIST, 20 different MNIST datasets are generated; each dataset is created by a special pixel permutation of the input images, without changing their corresponding labels; each dataset has its own permutation by owning a random seed [i.e., each dataset has its own seed] – Derakhshani, paragraph 69).”
Derakhshani and the instant application both relate to machine learning and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kutt to permute the data based on seeds, as disclosed by Derakhshani, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would increase the size of the dataset on which the models can be trained, thereby increasing their accuracy. See Derakhshani, paragraph 69.
Claim 14 is a system claim corresponding to method claim 4 and is rejected for the same reasons as given in the rejection of that claim. Similarly, claim 19 is a system claim corresponding to method claim 4 and is rejected for the same reasons as given in the rejection of that claim.
Regarding claim 6, Kutt, as modified by Derakhshani, discloses that “the acquired seed is randomly generated (in permuted MNIST, 20 different MNIST datasets are generated; each dataset is created by a special pixel permutation of the input images, without changing their corresponding labels; each dataset has its own permutation by owning a random [randomly generated] seed – Derakhshani, paragraph 69).” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kutt to use a randomly generated seed, as disclosed by Derakhshani, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would increase the size of the dataset on which the models can be trained, thereby increasing their accuracy. See Derakhshani, paragraph 69.
Claims 5, 15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kutt in view of Lafontaine (US 20210350357) (“Lafontaine”).
Regarding claim 5, the rejection of claim 1 is incorporated. Kutt further discloses “storing the [data] … independently from the vector database (Kutt Figs. 1-2 depict a public data loss prevention (DLP) database 100 that is separate from a CCP neural network that contains the embedding vector database 212).”
Kutt appears not to disclose explicitly the further limitations of the claim. However, Lafontaine discloses “encrypting the acquired seed (user wallet generates a seed and obfuscates the seed using encryption; the obfuscated seed is also encrypted on the server – Lafontaine, paragraph 192); and
storing the encrypted acquired seed (user wallet generates a seed and obfuscates the seed using encryption; the obfuscated seed is also encrypted on the server [i.e., is stored thereon] – Lafontaine, paragraph 192) ….”
LaFontaine and the instant application both relate to machine learning and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kutt to encrypt the seed, as disclosed by Lafontaine, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would ensure that the seed cannot be accessed by unauthorized users. See Lafontaine, paragraph 192.
Claim 15 is a system claim corresponding to method claim 5 and is rejected for the same reasons as given in the rejection of that claim. Similarly, claim 20 is a system claim corresponding to method claim 5 and is rejected for the same reasons as given in the rejection of that claim.
Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Kutt in view of Derakhshani and further in view of Marrero et al. (US 20220108134) (“Marrero”).
Regarding claim 7, the rejection of claim 1 is incorporated. Kutt further discloses “embedding vectors associated with … data”, as shown in the rejection of claim 1.
Kutt appears not to disclose explicitly the further limitations of the claim. However, Derakhshani discloses that “a permutation associated with a seed is applied to … the first data and the second data (in permuted MNIST, 20 different MNIST datasets are generated; each dataset is created by a special pixel permutation of the input images, without changing their corresponding labels; each dataset has its own permutation by owning a random seed [i.e., each of the 20 datasets is permuted] – Derakhshani, paragraph 69).” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kutt to permute the data based on a seed, as disclosed by Derakhshani, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would increase the size of the dataset on which the models can be trained, thereby increasing their accuracy. See Derakhshani, paragraph 69.
Neither Kutt nor Derakhshani appears to disclose explicitly the further limitations of the claim. However, Marrero discloses that “the first data and second data are associated with at least one of different accounts, different domains, or different chatbots (set of training data that include labeled images from a first domain and unlabeled images from a second domain obtained or generated – Marrero, paragraph 69) ….”
Marrero and the instant application both relate to machine learning and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Kutt and Derakhshani to use data from different domains, as disclosed by Marrero, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow the model to generalize so that it can accurately analyze data from multiple domains. See Marrero, paragraph 2.
Regarding claim 8, the rejection of claim 1 is incorporated. Kutt further discloses “embedding vectors associated with … data”, as shown in the rejection of claim 1.
Kutt appears not to disclose explicitly the further limitations of the claim. However, Derakhshani discloses that “a first permutation associated with a first seed is applied to … the first data, and a second permutation associated with a second seed is applied to … the second data (in permuted MNIST, 20 different MNIST datasets are generated; each dataset is created by a special pixel permutation of the input images, without changing their corresponding labels; each dataset has its own permutation by owning a random seed [i.e., each of the 20 datasets is permuted based on its own seed] – Derakhshani, paragraph 69).” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kutt to permute the data based on a seed, as disclosed by Derakhshani, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would increase the size of the dataset on which the models can be trained, thereby increasing their accuracy. See Derakhshani, paragraph 69.
Neither Kutt nor Derakhshani appears to disclose explicitly the further limitations of the claim. However, Marrero discloses that “the first data and second data are associated with at least one of different accounts, different domains, or different chatbots (set of training data that include labeled images from a first domain and unlabeled images from a second domain obtained or generated – Marrero, paragraph 69) ….” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Kutt and Derakhshani to use data from different domains, as disclosed by Marrero, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow the model to generalize so that it can accurately analyze data from multiple domains. See Marrero, paragraph 2.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Kutt in view of Singh et al., “Analyzing Embedding Models for Embedding Vectors in Vector Databases,” in Int’l Conf. ICT in Bus. Industry & Gov’t (2023) (“Singh”) and further in view of Gandhi et al. (US 20240378399) (“Gandhi”).
Regarding claim 10, the rejection of claim 1 is incorporated. Kutt further discloses a “permuted embedding vector” as shown in the rejection of claim 1.
Kutt appears not to disclose explicitly the further limitations of the claim. However, Singh discloses “determining metadata associated with a resulting … embedding vector from the vector database that is responsive to a query (Singh Fig. 2 shows that a vector embedding is produced by an embedding model responsive to a query from an application and is sent to a vector database; Fig. 3 and p. 4, first full paragraph on right-hand column disclose that a coordinator layer preserves collection metadata from a query coordinator [i.e., the metadata are associated with the embedding vector insofar as they are associated with the query that produced the embedding vector]); [and]
determining content associated with the resulting … embedding vector based on the metadata (Singh Fig. 2 shows that a query result [content] is produced by the database that received the vector embedding [i.e., is associated with the embedding vector]; Fig. 3 and p. 4, first full paragraph on right-hand column disclose that a coordinator layer preserves collection metadata from a query coordinator [i.e., since the metadata are associated with the embedding vector, the query result/content produced using the embedding vector is based on these metadata]) ….”
Singh and the instant application both relate to machine learning using vector embeddings and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kutt to determine content associated with an embedding vector based on metadata, as disclosed by Singh, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow the content to be retrieved using a numerical representation that can be understood by the model. See Singh, sec. I, fifth paragraph.
Neither Kutt nor Singh appears to disclose explicitly the further limitations of the claim. However, Gandhi discloses “utilizing the content in a prompt for provision to a large language model (prompt concatenation unit concatenates, inter alia, the natural language query and the grounded textual context [content] into a prompt to be provided to an LLM to cause the LLM to generate program code that includes knowledge-grounded information – Gandhi, paragraph 68).”
Gandhi and the instant application both relate to LLMs and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Kutt and Singh to utilize content in a prompt for an LLM, as disclosed by Gandhi, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would reduce the likelihood of the LLM hallucinating by feeding it more information. See Gandhi, paragraph 68.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Fang et al., “Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defenses,” in arXiv preprint arXiv:2402.04013 (2024) (providing a survey of model inversion attacks).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN C VAUGHN whose telephone number is (571)272-4849. The examiner can normally be reached M-R 7:00a-5:00p ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar, can be reached at 571-272-7796. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RYAN C VAUGHN/ Primary Examiner, Art Unit 2125
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