CTNF 19/216,687 CTNF 81693 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This action is in response to Application filed on 05/22/2025. Claims 1-20 are pending. Priority This application claims priority from U.S. Provisional Patent Application No. 63/650,863 filed 05/22/2024. The disclosure of the provisional application provides sufficient support for the claimed invention of this application as requirements under 35 U.S.C. § 112(a) or pre-AIA 35 U.S.C. § 112, first paragraph. Therefore, the effective filing date of this application is 05/22/2024. Information Disclosure Statement The Information Disclosure Statement (IDS) filed by Applicant on 09/18/2025 has been considered. A copy of the considered IDS is enclosed with this Office action. Claim Rejections - 35 USC § 112 07-30-02 AIA 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. 07-34-01 Claims 8 and 12 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 8 recites the limitation "the neural network" in line 5. There is insufficient antecedent basis for this limitation in the claim. In addition, it is unclear if the recited “the respective set of embedding vectors of the neural network” in lines 4-5 should be “the respective set of embedding vectors of the data item”. 07-34-05 AIA Claim 12 recites the limitation " the one or more locality-sensitive hash functions " in line 1 . There is insufficient antecedent basis for this limitation in the claim. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-13 and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (e.g., mental processes) without significantly more. The claims recite an abstract idea of encoding a query with vector(s) and searching using vectors based on broadly recited steps of encoding, performing and identifying, which are broadly recited steps/concepts that can be performed in the human mind or with the aid of pencil and paper and directed to mental processes grouping of abstract ideas . This judicial exception is not integrated into a practical application because other additional elements including genetic computer components and common computer functionality (e.g., accessing, storing, etc.) and/or insignificant extra-solution activity (e.g., mere data gathering) for implementing the abstract idea are not sufficient to integrate the abstract idea into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because additional elements include only generic/common computer components (e.g., memory, processor, program instructions, etc.) and generic/common computer functions (e.g., accessing, storing, etc.) and/or insignificant extra-solution activity (e.g., mere data gathering), which are not sufficient to amount to significantly more than the recited abstract idea. Abstract idea analysis as follows: Step 1: According to the first part of the analysis, in the instant claims, claims 1-18 are directed to a method (i.e. a process), claim 19 is directed to one or more non-transitory computer storage media (i.e., a manufacture), and claim 20 is directed to system comprising one or more computers (i.e., a machine). Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture or composition of matter). Step 2a Prong 1 (claims 1, 19 and 20) : The following limitations recited in claims 1, 19 and 20 are abstract ideas that fall under mental processes: encoding the set of embedding vectors of the query in the embedding vector space into an encoded vector of the query in the target vector space (this step of encoding as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper (e.g., thinking about using a set of vectors and/or a vector for representing a query)); performing, with respect to the encoded vector of the query, a k-nearest neighbors search on the respective encoded vectors of each of the plurality of data items (this step of performing as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper (e.g., thinking about performing a search using a vector as a query)); and identifying, from the k-nearest neighbors search, a top-k subset of the plurality of data items (this step of identifying as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper, given a vector representing a query and a plurality of other vectors representing a plurality of data items). All the limitations above are mental steps that can be performed in the human mind or with the aid of pencil and paper. Step 2a Prong 2 (Claims 1, 19 and 20) : The following limitations in claims 1, 19 and 20 are additional elements: obtaining a set of embedding vectors of a query in an embedding vector space (this step of obtaining as broadly recited is directed to data gathering recited at high level of generality or being insignificant extra-solution activity); obtaining, for each of a plurality of data items, a respective encoded vector of the data item in a target vector space (this step of obtaining as broadly recited is directed to data gathering recited at high level of generality or being insignificant extra-solution activity); one or more non-transitory computer storage media storing instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising (these elements are directed to generic computer components and/or mere instructions for implementing or applying the abstract idea); one or more computers (these elements are directed to generic computers); and one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising (these elements are directed to generic computer components and/or mere instructions for implementing or applying the abstract idea). These are a generic computer and/or generic computer components used to perform generic computer functions or insignificant extra-solution activity for implementing or applying the abstract. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s). Step 2b (Claims 1, 19 and 20): The following limitations in claims 1, 19 and 20 are additional elements: obtaining a set of embedding vectors of a query in an embedding vector space (this step of obtaining as broadly recited is directed to data gathering recited at high level of generality or being insignificant extra-solution activity or well-understood, routine, conventional activity); obtaining, for each of a plurality of data items, a respective encoded vector of the data item in a target vector space (this step of obtaining as broadly recited is directed to data gathering recited at high level of generality or being insignificant extra-solution activity or well-understood, routine, conventional activity); one or more non-transitory computer storage media storing instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising (these elements are directed to generic computer components and/or mere instructions for implementing or applying the abstract idea); one or more computers (these elements are directed to generic computers); and one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising (these elements are directed to generic computer components and/or mere instructions for implementing or applying the abstract idea). These are a generic computer and/or generic computer components used to perform generic computer functions or well-understood, routine, conventional activity, and do not amount to significantly more, see MPEP 2106.05(d)(II). Regarding claim 2, claim 2 depends on claim 1. As such, claim 2 recites the abstract idea as presented in claim 1. In addition, claim 2 includes additional elements: wherein obtaining the set of embedding vectors of the query in the embedding vector space comprises: (these element specifying obtaining process which is directed to mere data gathering) receiving the query (this step of receiving as broadly recited is directed to data gathering recited at high level of generality or being insignificant extra-solution activity); and processing the query, using an encoder neural network, to generate the set of embedding vectors of the query in the embedding vector space (this step of processing as broadly recited can be mentally preformed with the human mind or with the aid of pencil and paper, wherein reciting “using an encoder neural network” provides nothing more than “apply it” to the abstract idea, and the “encoder neural network” as broadly recited without limiting how it functions amounts nothing more than mere instructions to implement an abstract idea on a generic computer). These are additional elements directed to an insignificant extra-solution activity and/or mere instructions for implementing the abstract idea, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II). Regarding claim 3, claim 3 depends on claim 1. As such, claim 3 recites the abstract idea as presented in claim 1. In addition, claim 3 includes additional elements: wherein the k-nearest neighbors search is an exact k-nearest neighbors search (this element specifying a search approach/technique recited at high level of generality, which amounts nothing more than a label/name or mere data). These are additional elements directed to mere data for implementing the abstract idea, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II). Regarding claim 4, claim 4 depends on claim 1. As such, claim 4 recites the abstract idea as presented in claim 1. In addition, claim 4 includes additional elements: wherein the k-nearest neighbors search is an appropriate k-nearest neighbors search (this element specifying a search approach/technique recited at high level of generality, which amounts nothing more than a label/name or mere data). These are additional elements directed to mere data for implementing the abstract idea, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II). Regarding claim 5, claim 5 depends on claim 1. As such, claim 5 recites the abstract idea as presented in claim 1. In addition, claim 5 includes additional elements: wherein the k-nearest neighbors search is a maximum inner product search (this element specifying a search approach/technique recited at high level of generality, which amounts nothing more than a label/name or mere data). These are additional elements directed to mere data for implementing the abstract idea, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II). Regarding claim 6, claim 6 depends on claim 1. As such, claim 6 recites the abstract idea as presented in claim 1. In addition, claim 6 includes additional elements: wherein for each of the plurality of data items, a respective inner product between (i) the encoded vector of the query in the target vector space and (ii) the respective encoded vector of the data item in the target vector space approximates a respective Chamfer similarity between (i) the set of embedding vectors of the query in the embedding vector space and (ii) a respective set of embedding vectors of the data item in the embedding vector space (these elements describe relation between elements/items and are directed to mere data/information). These are additional elements directed to mere data for implementing the abstract idea, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II). Regarding claim 7, claim 7 depends on claim 1. As such, claim 7 recites the abstract idea as presented in claim 1. In addition, claim 7 includes additional elements: for each data item in the top-k subset: obtaining a respective set of embedding vectors of the data item in the embedding vector space (this step of obtaining as broadly recited is directed to mere data gathering recited at high level of generality or being insignificant extra-solution activity); computing a respective Chamfer similarity between: (i) the set of embedding vectors of the query, and (ii) the respective set of embedding vectors of the data item (this step of computing a respective Chamfer similarity as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper); and determining a respective score for the data item based on the respective Chamfer similarity for the data item (this step of determining as broadly recited can be performed in the human mind or with the aid of pencil and paper); re-ranking the data items in the top-k subset according to their respective scores (this step of determining as broadly recited can be performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and opinion); and selecting, from the re-ranked top-k subset, the data item having the greatest respective score based on the respective Chamfer similarity for the data item (this step of determining as broadly recited can be performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and opinion). These are additional elements directed to mental processes (i.e., the abstract idea), which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II). Regarding claim 8, claim 8 depends on claim 7. As such, claim 8 recites the abstract idea as presented in claim 7. In addition, claim 8 includes additional elements: wherein for each data item in the top-k subset, obtaining the respective set of embedding vectors of the data item in the embedding vector space comprises: (these element specifying obtaining process which is directed to mere data gathering) obtaining the data item (this step of obtaining as broadly recited is directed to mere data gathering recited at high level of generality and being insignificant extra-solution activity); and processing the data item, using an encoder neural network, to generate the respective set of embedding vectors of the neural network in the embedding vector space (this step of processing as broadly recited can be mentally preformed with the human mind or with the aid of pencil and paper, wherein reciting “using an encoder neural network” provides nothing more than “apply it” to the abstract idea, and the “encoder neural network” as broadly recited without limiting how it functions amounts nothing more than mere instructions to implement an abstract idea on a generic computer). These are additional elements directed to an insignificant extra-solution activity and/or mere instructions for implementing the abstract idea, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II). Regarding claim 9, claim 9 depends on claim 1. As such, claim 9 recites the abstract idea as presented in claim 1. In addition, claim 9 includes additional elements: wherein encoding the set of embedding vectors of the query in the embedding vector space into the encoded vector of the query in the target vector space comprises: (this element specifying the encoding) processing the set of embedding vectors of the query, using each of one or more space partitioning functions, to generate a respective space encoded vector of the query for the space partitioning function (this step of processing as broadly recited can be mentally performed with the human mind or with the aid of pencil and paper); and concatenating the respective space encoded vectors of the query for each of the one or more space partitioning functions to generate the encoded vector of the query (this step of concatenating as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper). These are additional elements directed to mental processes or the abstract idea, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II). Regarding claim 10, claim 10 depends on claim 9. As such, claim 10 recites the abstract idea as presented in claim 9. In addition, claim 10 includes additional elements: wherein each of the one or more space partitioning functions implements random partitioning or k-means partitioning (this element specifying a partitioning function recited at high level of generality, which amounts nothing more than a label/name or mere data). These are additional elements directed to mere data for implementing the abstract idea, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II). Regarding claim 11, claim 11 depends on claim 9. As such, claim 11 recites the abstract idea as presented in claim 9. In addition, claim 11 includes additional elements: wherein each of the one or more space partitioning functions is a locality-sensitive hash function (this element specifying a partitioning function recited at high level of generality, which amounts nothing more than a label/name or mere data). These are additional elements directed to mere data for implementing the abstract idea, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II). Regarding claim 12, claim 12 depends on claim 11. As such, claim 12 recites the abstract idea as presented in claim 11. In addition, claim 12 includes additional elements: wherein each of the one or more locality-sensitive hash functions implements SimHash partitioning (this element specifying a partitioning function recited at high level of generality, which amounts nothing more than a label/name or mere data). These are additional elements directed to mere data for implementing the abstract idea, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II). Regarding claim 13, claim 13 depends on claim 9. As such, claim 12 recites the abstract idea as presented in claim 9. In addition, claim 13 includes additional elements: the one or more space partitioning functions are each associated with a respective plurality of partitions of the embedding vector space (this element specifies relation between functions and partitions and is directed to mere descriptive data), and each of the one or more space partitioning functions is configured to: (this element describes partitioning function) receive an input embedding vector belonging to the embedding vector space (this step of receiving as broadly recited is directed to mere data gathering recited at high level of generality or being insignificant extra-solution activity); and process the input embedding vector to assign the input embedding vector to one of the respective plurality of partitions of the embedding vector space associated with the space partitioning function (this step of processing as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper). These are additional elements directed to mental processes and/or insignificant extra-solution activity or well-understood, routine, conventional activity, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II). Regarding claim 18, claim 18 depends on claim 1. As such, claim 18 recites the abstract idea as presented in claim 1. In addition, claim 18 includes additional elements: wherein each of the query and plurality of data items comprises one or more of: a respective text sequence, a respective image, a respective video, a respective audio waveform, or a respective sensor dataset (this element describes the query and plurality of data items and is directed to mere data). These are additional elements directed to mere data for implementing the abstract idea, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II). Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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. 07-07-aia AIA 07-07 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 – 07-12-aia AIA (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. 07-15-03-aia AIA Claim s 1-5, 9-10, 13 and 18-20 (effective filing date 05/22/2024) are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Fayyaz et al. (U.S. Publication No. 2024/0354317, effectively filed date 04/21/2023) . As to claim 1, Fayyaz et al. teaches: “A method performed by one or more computers” (see Fayyaz et al. , Abstract and Fig. 1), the method comprising: “obtaining a set of embedding vectors of a query in an embedding vector space” (see Fayyaz at al. , [0046] and [0062] for obtaining individual embeddings for an input item (e.g., an input query) from plurality of input-encoding subsystems (e.g., a text encoder, an image encoder, an audio encoder, etc.), wherein each embedding is a vector (see [0038] and [0041]), wherein the vector space in which the individual embeddings/vectors are created can be interpreted as an embedding vector space as recited; also see Fig. 2); “obtaining, for each of a plurality of data items, a respective encoded vector of the data item in a target vector space” (see Fayyaz et al. , [0038], [0041] and [0059] for obtaining the target item embeddings (i.e., vectors) associated with content items from the retrieval index, wherein the vector space associated with the retrieval index as disclosed can be interpreted as a target vector space); “encoding the set of embedding vectors of the query in the embedding vector space into an encoded vector of the query in the target vector space” (see Fayyaz et al. , [0046] and [0062] for assembling the individual embeddings/vectors of an input item (e.g., an input query) from a plurality of input-encoding subsystems (e.g., a text encoder, an image encoder, etc.) into the input-system embedding, and then mapping the input system embedding into the query embedding (i.e., an encoded vector of the query); also see [0059]); “performing, with respect to the encoded vector of the query, a k-nearest neighbors search on the respective encoded vectors of each of the plurality of data items” (see Fayyaz et al. , [0063] for matching the query embedding (i.e., the encoded vector of the query) against the target item embeddings/vectors in the retrieval index using an appropriate nearest neighbor (ANN) search technique); and “identifying, from the k-nearest neighbors search, a top-k subset of the plurality of data items” (see Fayyaz et al. , [0063] for matching the query embedding (i.e., the encoded vector of the query) against the target item embeddings/vectors in the retrieval index to identify/produce a candidate set of top K target item embeddings, wherein each target item embedding represents a content item (see [0038])). As to claim 2, this claim is rejected based on the same arguments as above to reject claim 1 and is similarly rejected including the following: Fayyaz et al. teaches: “wherein obtaining the set of embedding vectors of the query in the embedding vector space comprises” (see Fayyaz et al. , Fig. 1 and Fig. 2): “receiving the query” (see Fayyaz et al. , Fig. 1 and [0062] for receiving an input query); and “processing the query, using an encoder neural network, to generate the set of embedding vectors of the query in the embedding vector space” (see Fayyaz et al. , Fig. 2, [0048] and [0062] for processing the input item (e.g., an input query) through the input-embedding system including a plurality of input-encoding subsystems (e.g., a text encoder, an image encoder, etc.) to generate a plurality of individual embeddings/vectors (e.g., a text embedding, an image embedding, etc.), wherein the vector space associated with the input embedding system can be interpreted as the embedding vector space as recited). As to claim 3, this claim is rejected based on the same arguments as above to reject claim 1 and is similarly rejected including the following: Fayyaz et al. teaches: “wherein the k-nearest neighbors search is an exact k-nearest neighbors search” (see Fayyaz et al. , [0063] for performing an exhaustive search through all of the target item embeddings in the retrieval index to find the K target item embeddings having closest distance to the query input embedding (i.e., an exact k-nearest neighbors search)). As to claim 4, this claim is rejected based on the same arguments as above to reject claim 1 and is similarly rejected including the following: Fayyaz et al. teaches: “wherein the k-nearest neighbors search is an approximate k-nearest neighbors search” (see Fayyaz et al. , [0063] for using an approximate nearest neighbor (ANN) technique to search the retrieval index). As to claim 5, this claim is rejected based on the same arguments as above to reject claim 1 and is similarly rejected including the following: Fayyaz et al. teaches: “wherein the k-nearest neighbors search is a maximum inner product search” (see Fayyaz et al. , [0063] for searching for a candidate set of top K target item embeddings based on cosine similarity/distance (e.g., highest similarity or closest distance), which is based on dot product or inner product between the query embedding and any target item embeddings). As to claim 9, this claim is rejected based on the same arguments as above to reject claim 1 and is similarly rejected including the following: Fayyaz et al. teaches: “wherein encoding the set of embedding vectors of the query in the embedding vector space into the encoded vector of the query in the target vector space comprises” (see Fayyaz et al. , [0046] and [0062] for assembling the individual embeddings/vectors into the input-system embedding, and then mapping the input-system embedding into a query embedding (i.e., encoded vector of the query): “processing the set of embedding vectors of the query, using each of one or more space partitioning functions, to generate a respective space encoded vector of the query for the space partitioning function” (see Fayyaz et al. , [0053] wherein each of input-encoding subsystems (e.g., text encoder, image encoder, audio encoder, etc.) performs their own respective forms of content partitioning and embedding, wherein each form of partitioning can be interpreted as a partitioning function as recited); and “concatenating the respective space encoded vectors of the query for each of the one or more space partitioning functions to generate the encoded vector of the query” (see Fayyaz et al. , [0046] and [0062] for assembling/concatenating the individual embeddings/vectors into the input-system embedding, and then mapping the input-system embedding into a query embedding (i.e., encoded vector of the query)). As to claim 10, this claim is rejected based on the same arguments as above to reject claim 9 and is similarly rejected including the following: Fayyaz et al. teaches: “wherein each of the one or more space partitioning functions implements random partitioning or k-means partitioning” (see Fayyaz et al. , [0057] for partitioning video content in any manner). As to claim 18, this claim is rejected based on the same arguments as above to reject claim 1 and is similarly rejected including the following: Fayyaz et al. teaches: “wherein each of the query and plurality of data items comprises one or more of: a respective text sequence, a respective image, a respective video, a respective audio waveform, or a respective sensor dataset” (see Fayyaz et al. , [0029] and [0062] for multimodal item, e.g., an input query or data item can include text content or combination of text content and image content). As to claim 19, Fayyaz et al. teaches: “One or more non-transitory computer storage media storing instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising” (see Fayyaz et al. , Abstract, Fig. 1 and [0113]): “obtaining a set of embedding vectors of a query in an embedding vector space” (see Fayyaz at al. , [0046] and [0062] for obtaining individual embeddings for an input item (e.g., an input query) from plurality of input-encoding subsystems (e.g., a text encoder, an image encoder, an audio encoder, etc.), wherein each embedding is a vector (see [0038] and [0041]), wherein the vector space in which the individual embeddings/vectors are created can be interpreted as an embedding vector space as recited); “obtaining, for each of a plurality of data items, a respective encoded vector of the data item in a target vector space” (see Fayyaz et al. , [0038], [0041] and [0059] for obtaining the target item embeddings (i.e., vectors) associated with content items from the retrieval index, wherein the vector space associated with the retrieval index as disclosed can be interpreted as a target vector space); “encoding the set of embedding vectors of the query in the embedding vector space into an encoded vector of the query in the target vector space” (see Fayyaz et al. , [0046] and [0062] for assembling the individual embeddings/vectors of an input item (e.g., an input query) from a plurality of input-encoding subsystems (e.g., a text encoder, an image encoder, etc.) into the input-system embedding, and then mapping the input system embedding into the query embedding (i.e., an encoded vector of the query); also see [0059]); “performing, with respect to the encoded vector of the query, a k-nearest neighbors search on the respective encoded vectors of each of the plurality of data items” (see Fayyaz et al. , [0063] for matching the query embedding (i.e., the encoded vector of the query) against the target item embeddings/vectors in the retrieval index using an appropriate nearest neighbor (ANN) search technique); and “identifying, from the k-nearest neighbors search, a top-k subset of the plurality of data items” (see Fayyaz et al. , [0063] for matching the query embedding (i.e., the encoded vector of the query) against the target item embeddings/vectors in the retrieval index to identify/produce a candidate set of top K target item embeddings, wherein each target item embedding represents a content item (see [0038])). As to claim 20, Fayyaz et al. teaches: “A system comprising” (see Fayyaz et al. , Abstract and Fig. 1): “one or more computers” (see Fayyaz et al. , Fig. 1 and [0027] wherein the execution platform corresponds to any type of computing system or combination of computing devices (i.e., computers); also see Fig. 12); and “one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising” (see Fayyaz et al. , Fig. 1 and [0038] wherein the index storage represents one or more storage devices): “obtaining a set of embedding vectors of a query in an embedding vector space” (see Fayyaz at al. , [0046] and [0062] for obtaining individual embeddings for an input item (e.g., an input query) from plurality of input-encoding subsystems (e.g., a text encoder, an image encoder, an audio encoder, etc.), wherein each embedding is a vector (see [0038] and [0041]), wherein the vector space in which the individual embeddings/vectors are created can be interpreted as an embedding vector space as recited; also see Fig. 2); “obtaining, for each of a plurality of data items, a respective encoded vector of the data item in a target vector space” (see Fayyaz et al. , [0038], [0041] and [0059] for obtaining the target item embeddings (i.e., vectors) associated with content items from the retrieval index, wherein the vector space associated with the retrieval index as disclosed can be interpreted as a target vector space); “encoding the set of embedding vectors of the query in the embedding vector space into an encoded vector of the query in the target vector space” (see Fayyaz et al. , [0046] and [0062] for assembling the individual embeddings/vectors of an input item (e.g., an input query) from a plurality of input-encoding subsystems (e.g., a text encoder, an image encoder, etc.) into the input-system embedding, and then mapping the input system embedding into the query embedding (i.e., an encoded vector of the query); also see [0059]); “performing, with respect to the encoded vector of the query, a k-nearest neighbors search on the respective encoded vectors of each of the plurality of data items” (see Fayyaz et al. , [0063] for matching the query embedding (i.e., the encoded vector of the query) against the target item embeddings/vectors in the retrieval index using an appropriate nearest neighbor (ANN) search technique); and “identifying, from the k-nearest neighbors search, a top-k subset of the plurality of data items” (see Fayyaz et al. , [0063] for matching the query embedding (i.e., the encoded vector of the query) against the target item embeddings/vectors in the retrieval index to identify/produce a candidate set of top K target item embeddings, wherein each target item embedding represents a content item (see [0038])) . Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim 6 (effective filing date 05/22/2024) is rejected under 35 U.S.C. 103 as being unpatentable over Fayyaz et al. (U.S. Publication No. 2024/0354317, effectively filed date 04/21/2023), and further in view of Shmueli et al. (U.S. Publication No. 2023/0061341, Publication date 03/02/2023) . As to claim 6, Fayyaz et al. teaches all limitations as recited in claim 5. However, Fayyaz et al. does not explicitly teach: “wherein for each of the plurality of data items, a respective inner product between (i) the encoded vector of the query in the target vector space and (ii) the respective encoded vector of the data item in the target vector space approximates a respective Chamfer similarity between (i) the set of embedding vectors of the query in the embedding vector space and (ii) a respective set of embedding vectors of the data item in the embedding vector space”. On the other hand, Shmueli et al. explicitly teaches: “wherein for each of the plurality of data items, a respective inner product between (i) the encoded vector of the query in the target vector space and (ii) the respective encoded vector of the data item in the target vector space approximates a respective Chamfer similarity between (i) the set of embedding vectors of the query in the embedding vector space and (ii) a respective set of embedding vectors of the data item in the embedding vector space” (see Shmueli et al. , [0021]-[0022] wherein the dot product (i.e., inner product) between a first single long vector computed from the first set of vectors and a second single long vector computed from a second set of vectors computes similarities (e.g., Chamfer similarity) between two sets corresponding to the first set of vectors and the second set of records). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Shmueli et al. 's teaching to Fayyaz et al. ’s system. Skilled artisan would have been motivated to do so to realize the relation between a set of embedding vectors and an encoded vector where both represent a same item (e.g., a query or a data item). In addition, both of the references ( Fayyaz et al. and Shmueli et al. ) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, a vector search system that comprises generating a set of vectors for a data item, processing the set of vectors to generate a singular vector for the data item, and performing searching based on the singular vector. This close relation between both of the references highly suggests an expectation of success when combined . 07-21-aia AIA Claim s 7-8 (effective filing date 05/22/2024) are rejected under 35 U.S.C. 103 as being unpatentable over Fayyaz et al. (U.S. Publication No. 2024/0354317, effectively filed date 04/21/2023), and further in view of Wang et al. (U.S. Publication No. 2012/0054177, Publication date 03/01/2012) . As to claim 7, Fayyaz et al. teaches all limitations as recited in claim 1. However, Fayyaz et al. does not explicitly teach a feature for calculating a Chamfer similarity between sets to rank and select the top data item(s) as equivalently recited as follows: “for each data item in the top-k subset: obtaining a respective set of embedding vectors of the data item in the embedding vector space; computing a respective Chamfer similarity between: (i) the set of embedding vectors of the query, and (ii) the respective set of embedding vectors of the data item; and determining a respective score for the data item based on the respective Chamfer similarity for the data item; re-ranking the data items in the top-k subset according to their respective scores; and selecting, from the re-ranked top-k subset, the data item having the greatest respective score based on the respective Chamfer similarity for the data item”. On the other hand, Wang et al. explicitly teaches a feature for calculating a Chamfer similarity between sets to rank and select the top data item(s) (see Wang et al. , Fig. 4, [0098] and [0102] for calculating Chamfer similarity based on distance maps (i.e., between a set representing a query (e.g., query curve) and a set representing an image (e.g., image curve) to determine the rank for the top N images and/or selecting image(s) with the highest score). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Wang et al. 's teaching to Fayyaz et al. ’s system by implementing a feature of calculating a Chamfer similarity between the set of embedding vectors of the query and the set of embedding vectors of a respective data item to score/rank the respective data item. Skilled artisan would have been motivated to do so to provide Fayyaz et al. ’s system with additional effective way to rank/re-rank the top-k subset. In addition, both of the references ( Fayyaz et al. and Wang et al. ) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, a search system for using a set of data (e.g., value or vector) to represent an item. This close relation between both of the references highly suggests an expectation of success when combined. As to claim 8, this claim is rejected based on the same arguments as above to reject claim 7 and is similarly rejected including the following: Fayyaz et al. as modified by Wang et al. teaches: “wherein for each data item in the top-k subset, obtaining the respective set of embedding vectors of the data item in the embedding vector space comprises” (see Fayyaz et al. , Fig. 2 and [0046] for obtaining a set of individual embeddings/vector from a plurality of input-encoding subsystems of the input-embedding system, wherein the vector space associated with the input-embedding system is interpreted as the embedding vector space): “obtaining the data item” (see Fayyaz et al. , Fig. 1 and [0045] for receiving content item(s) by the encoder system, which comprises an input-embedding system and an embedding-mapping system); and “processing the data item, using an encoder neural network, to generate the respective set of embedding vectors of the neural network in the embedding vector space” (see Fayyaz et al. , Fig. 2 and [0046] for processing the content item or the input item by the encoder system (i.e., an encoder neural network) to generate a set of individual embeddings/vector from a plurality of input-encoding subsystems of the input-embedding system, wherein the vector space associated with the input-embedding system is interpreted as the embedding vector space) . 07-21-aia AIA Claim s 11-12 (effective filing date 05/22/2024) are rejected under 35 U.S.C. 103 as being unpatentable over Fayyaz et al. (U.S. Publication No. 2024/0354317, effectively filed date 04/21/2023), and further in view of Kleber et al. (U.S. Publication No. 2021/0042787, Publication date 02/11/2021) . As to claim 11, Fayyaz et al. teaches all limitations as recited in claim 9 including partitioning (see Fayyaz et al. , [0053] for different forms/methods/functions of partitioning used by input-encoding subsystems). However, Fayyaz et al. does not explicitly teach: “wherein each of the one or more space partitioning functions is a locality-sensitive hash function”. On the other hand, Kleber et al. explicitly teaches: “wherein each of the one or more space partitioning functions is a locality-sensitive hash function” (see Kleber et al. , [0119] and [0166] for a classification model or algorithm (i.e., partitioning function), which may be a locality-sensitive hash function). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Kleber et al. 's teaching to Fayyaz et al. ’s system by implementing a feature for using a locality-sensitive hash function for partitioning. Skilled artisan would have been motivated to do so to provide Fayyaz et al.’s system with an effective way for partitioning data/space because locality-sensitive hashing is well-known and well-used in the art for partitioning data. As to claim 12, this claim is rejected based on the same arguments as above to reject claim 11 and is similarly rejected including the following: Fayyaz et al. as modified by Kleber et al. teaches: “wherein each of the one or more locality-sensitive hash functions implements SimHash partitioning” (see Kleber et al. , [0119] wherein a locality-sensitive hashing scheme/function may include SimHash) . 07-21-aia AIA Claim 13 (effective filing date 05/22/2024) is rejected under 35 U.S.C. 103 as being unpatentable over Fayyaz et al. (U.S. Publication No. 2024/0354317, effectively filed date 04/21/2023), and further in view of Walker et al. (U.S. Publication No. 2019/0325531, Publication date 10/24/2019) . As to claim 13, Fayyaz et al. teaches all limitations as recited in claim 9. However, Fayyaz et al. does not explicitly teach a feature of using a partitioning function to assign/place vectors of a vector space into partitions in the vector space as equivalently recited as follows: “the one or more space partitioning functions are each associated with a respective plurality of partitions of the embedding vector space, and “each of the one or more space partitioning functions is configured to: receive an input embedding vector belonging to the embedding vector space; and process the input embedding vector to assign the input embedding vector to one of the respective plurality of partitions of the embedding vector space associated with the space partitioning function”. On the other hand, Walker et al. explicitly teaches a feature of using a partitioning function to assign/place vectors of a vector space into partitions in the vector space (see Walker et al. , [0028], [0032] and [0043] for using hash functions to partition a vector space of vectors or place vectors of the vector space into partitions in the vector space, wherein the hash functions as disclosed can be interpreted as equivalent to partitioning functions as recited). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Walker et al. 's teaching to Fayyaz et al. ’s system by implementing a feature of using a partitioning function to assign/place vectors of a vector space into partitions in the vector space . Skilled artisan would have been motivated to do so provide Fayyaz et al. ’s system with an effective way to identify related or similar items/entities as suggested by Walker et al. (see [0043]). In addition, both of the references ( Fayyaz et al. and Walker et al. ) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, a search/mapping system that allows to identify data based on vectors. This close relation between both of the references highly suggests an expectation of success when combined . Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim s 14-17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. 13-03-01 AIA The following is a statement of reasons for the indication of allowable subject matter: The prior art of record fails to anticipate or suggest or render obvious the feature of processing the set of embedding vectors of the query, using each of the one or more space partitioning functions, to generate the respective space encoded vector of the query for the space partitioning function as specified in claim 14 as follows: for each of the one or more space partitioning functions: processing each embedding vector in the set of embedding vectors of the query, using the space partitioning function, to assign the embedding vector to one of the respective plurality of partitions of the embedding vector space associated with the space partitioning function; for each of the respective plurality of partitions of the embedding vector space associated with the space partitioning function: summing each of the embedding vectors in the set of embedding vectors of the query assigned to the partition to generate a respective partition encoded vector of the query for the partition; and concatenating the respective partition encoded vectors of the query for each of the respective plurality of partitions of the embedding vector space associated with the space partitioning function to generate the respective space encoded vector of the query for the space partitioning function . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHUONG THAO CAO whose telephone number is (571)272-2735. The examiner can normally be reached Monday - Friday: 9:00 am - 6:00 pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amy Ng can be reached at 571-270-1698. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Phuong Thao Cao/Primary Examiner, Art Unit 2164 Application/Control Number: 19/216,687 Page 2 Art Unit: 2164 Application/Control Number: 19/216,687 Page 3 Art Unit: 2164 Application/Control Number: 19/216,687 Page 4 Art Unit: 2164 Application/Control Number: 19/216,687 Page 5 Art Unit: 2164 Application/Control Number: 19/216,687 Page 6 Art Unit: 2164 Application/Control Number: 19/216,687 Page 7 Art Unit: 2164 Application/Control Number: 19/216,687 Page 8 Art Unit: 2164 Application/Control Number: 19/216,687 Page 9 Art Unit: 2164 Application/Control Number: 19/216,687 Page 10 Art Unit: 2164 Application/Control Number: 19/216,687 Page 11 Art Unit: 2164 Application/Control Number: 19/216,687 Page 12 Art Unit: 2164 Application/Control Number: 19/216,687 Page 13 Art Unit: 2164 Application/Control Number: 19/216,687 Page 14 Art Unit: 2164 Application/Control Number: 19/216,687 Page 15 Art Unit: 2164 Application/Control Number: 19/216,687 Page 16 Art Unit: 2164 Application/Control Number: 19/216,687 Page 17 Art Unit: 2164 Application/Control Number: 19/216,687 Page 18 Art Unit: 2164 Application/Control Number: 19/216,687 Page 19 Art Unit: 2164 Application/Control Number: 19/216,687 Page 20 Art Unit: 2164 Application/Control Number: 19/216,687 Page 21 Art Unit: 2164 Application/Control Number: 19/216,687 Page 22 Art Unit: 2164 Application/Control Number: 19/216,687 Page 23 Art Unit: 2164 Application/Control Number: 19/216,687 Page 24 Art Unit: 2164 Application/Control Number: 19/216,687 Page 25 Art Unit: 2164 Application/Control Number: 19/216,687 Page 26 Art Unit: 2164 Application/Control Number: 19/216,687 Page 27 Art Unit: 2164 Application/Control Number: 19/216,687 Page 28 Art Unit: 2164 Application/Control Number: 19/216,687 Page 29 Art Unit: 2164 Application/Control Number: 19/216,687 Page 30 Art Unit: 2164 Application/Control Number: 19/216,687 Page 31 Art Unit: 2164 Application/Control Number: 19/216,687 Page 32 Art Unit: 2164
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