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 .
DETAILED ACTION
1. The pending claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 101
2. 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.
3. 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 below of the claims’ subject matter eligibility follows the guidance set forth in MPEP 2106 which has incorporated the 2019 PEG.
Regarding to claim 1,
Step 1 Analysis: Claim 1 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 1 recites: A method of data retrieval, comprising:
“receiving a search query including a search value and a context radius indicating a number (N) of terms representing a range of contextual information”. This element reads on a person receives a search query including a search value and a context radius indicating a number (N) of terms representing a range of contextual information which could be considered a mental process of an observation or evaluation.
“retrieving, from a vector repository storing a plurality of vector embeddings associated with a data asset, one or more vector embeddings of the plurality of vector embeddings that match the search value”. This element reads on a person retrieves, from a vector repository storing a plurality of vector embeddings associated with a data asset, one or more vector embeddings of the plurality of vector embeddings that match the search value which could be considered a mental process of an observation or evaluation.
“retrieving, from the vector repository, N additional vector embeddings of the plurality of vector embeddings for each matching vector embedding of the one or more matching vector embeddings based on a hierarchy of terms associated with the data asset”. This element reads on a person retrieves, from the vector repository, N additional vector embeddings of the plurality of vector embeddings for each matching vector embedding of the one or more matching vector embeddings based on a hierarchy of terms associated with the data asset which could be considered a mental process of an observation or evaluation.
Overall, the limitations directed to retrieves N nearest matching data assets and the various mental process limitations in the context of this claim encompasses limitations that are not only considered to be directed to limitations that could be practically performed in the human mind (including observations and preform an evaluation, judgment, and opinion) aided by the use of pen and paper. If the claim limitations, under their broadest reasonable interpretations, cover performance of the limitation in the mind but for the recitation of generic computer components, then they fall within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: In Step 2A Prong 2, we are directed to Identify whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluate those additional elements to determine whether they integrate the exception into a practical application of the exception.
There is no additional element integrate the judicial exception into a practical application.
Step 2B Analysis: In Step 2B, we are directed to Identify whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluate those additional elements to determine whether the additional elements, taken individually and in combination, result in the claim as a whole amounting to significantly more than the judicial exception.
There is no additional element, taken individually and in combination, result in the claim as a whole amounting to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 2,
Step 1 Analysis: Claim 2 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 2 is dependent on claim 1, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 2 recites “determining the hierarchy of terms based on metadata stored in a metadata repository associated with the vector repository" That is, the claim recites determining the hierarchy of terms based on metadata stored in a metadata repository associated with the vector repository. The above-noted limitation of claim 2, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 3,
Step 1 Analysis: Claim 3 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 3 is dependent on claim 1, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 3 recites “the hierarchy of terms indicates an ordinal position for each of the plurality of vector embeddings relative to the data asset" That is, the claim recites the hierarchy of terms indicates an ordinal position for each of the plurality of vector embeddings relative to the data asset. The above-noted limitation of claim 3, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 4,
Step 1 Analysis: Claim 4 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 4 is dependent on claims 1&3, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 4 recites “
determining the ordinal position for the matching vector embedding; and
determining the N additional vector embeddings based on the ordinal position of the matching vector embedding" That is, the claim recites determining the ordinal position for the matching vector embedding; and determining the N additional vector embeddings based on the ordinal position of the matching vector embedding. The above-noted limitation of claim 4, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 5,
Step 1 Analysis: Claim 5 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 5 is dependent on claims 1, 3-4, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 5 recites “the ordinal positions for the N additional vector embeddings immediately precede the ordinal position of the matching vector embedding" That is, the claim recites the ordinal positions for the N additional vector embeddings immediately precede the ordinal position of the matching vector embedding. The above-noted limitation of claim 5, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 6,
Step 1 Analysis: Claim 6 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 6 is dependent on claims 1, 3-4, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 6 recites “the ordinal positions for the N additional vector embeddings immediately follow the ordinal position of the matching vector embedding" That is, the claim recites the ordinal positions for the N additional vector embeddings immediately follow the ordinal position of the matching vector embedding. The above-noted limitation of claim 6, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 7,
Step 1 Analysis: Claim 7 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 7 is dependent on claims 1, 3-4, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 7 recites “the ordinal positions for a number (M) of the additional vector embeddings immediately precede the ordinal position of the matching vector embedding and the ordinal positions for the remaining M-N additional vector embeddings immediately follow the ordinal position of the matching vector embedding." That is, the claim recites the ordinal positions for a number (M) of the additional vector embeddings immediately precede the ordinal position of the matching vector embedding and the ordinal positions for the remaining M-N additional vector embeddings immediately follow the ordinal position of the matching vector embedding.. The above-noted limitation of claim 7, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Accordingly, this additional element, taken individually and in combination, does not result in the claim as a whole amounting to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 8,
Step 1 Analysis: Claim 8 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 8 is dependent on claims 1&3, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 8 recites “the one or more matching vector embeddings comprises a number (K) of highest-matching vector embeddings, among the plurality of vector embeddings, based on a similarity measure" That is, the claim recites the one or more matching vector embeddings comprises a number (K) of highest-matching vector embeddings, among the plurality of vector embeddings, based on a similarity measure. The above-noted limitation of claim 8, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 9,
Step 1 Analysis: Claim 9 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 9 is dependent on claims 1, 3, 8, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 9 recites “presenting each matching vector embedding of the K highest-matching vector embeddings as a tuple that includes the N additional vector embeddings associated therewith" That is, the claim recites presenting each matching vector embedding of the K highest-matching vector embeddings as a tuple that includes the N additional vector embeddings associated therewith. The above-noted limitation of claim 9, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 10,
Step 1 Analysis: Claim 10 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 10 is dependent on claims 1, 3, 8-9, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 10 recites “ranking the K highest-matching vector embeddings based at least in part on the similarity measure" That is, the claim recites ranking the K highest-matching vector embeddings based at least in part on the similarity measure. The above-noted limitation of claim 10, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 11,
Step 1 Analysis: Claim 11 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 11 is dependent on claims 1, 3, 8-9, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 11 recites “ranking the K highest-matching vector embeddings based at least in part on their ordinal positions" That is, the claim recites ranking the K highest-matching vector embeddings based at least in part on their ordinal positions. The above-noted limitation of claim 11, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 12,
Step 1 Analysis: Claim 12 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 12 is dependent on claims 1, 3, 8, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 12 recites “generating a prompt for a large language model (LLM) based at least in part on the K*N vector embeddings retrieved from the vector repository" That is, the claim recites generating a prompt for a large language model (LLM) based at least in part on the K*N vector embeddings retrieved from the vector repository. The above-noted limitation of claim 12, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claims 13-20 are rejected under 35 U.S.C. 101 with the same rational of claims 1-4 and 8-11.
Claim Rejections - 35 USC § 102
4. 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)(2) the invention was described in (1) an application for patent, published under section 122(b), by another filed in the United States before the invention by the applicant for patent or (2) a patent granted on an application for patent by another filed in the United States before the invention by the applicant for patent, except that an international application filed under the treaty defined in section 351(a) shall have the effects for purposes of this subsection of an application filed in the United States only if the international application designated the United States and was published under Article 21(2) of such treaty in the English language.
5. Claims 1-2 and 13-14 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Takacs (US 20260094161 A1, hereinafter, “Takacs”).
6. With respect to claim 1,
Takacs discloses a method of data retrieval, comprising:
receiving a search query including a search value and a context radius indicating a number (N) of terms representing a range of contextual information (Takacs [0266] – [0268], [0303], [0307] e.g. [0266] In some embodiments, the incident grouping module 104 may maintain a plurality of incidents 102 as vector representations, where the vector representations are based on suspect identifiers (FIG. 2, operation 202). This vector-based maintenance approach may organize incidents such that incidents with related suspect identifiers have nearby vectors in the vector space, enabling efficient similarity computations and pattern recognition operations. The system 100 may transform suspect identifiers into high-dimensional vector embeddings using various techniques, such as neural network based embeddings, transformer models, or specialized fraud detection embedding algorithms. The vector space organization may allow the system 100 to capture semantic relationships between suspect identifiers that may not be apparent through traditional string matching or exact comparison methods. The vector query may employ some combination of k-nearest neighbor and/or range (radius) search techniques to identify relevant incidents within the vector embedding space.);
retrieving, from a vector repository storing a plurality of vector embeddings associated with a data asset, one or more vector embeddings of the plurality of vector embeddings that match the search value (Takacs abstract, [0010], [0048], [0071], [0075] -[0082], [0086], [0088], [0090] – [0092], [0102] - [0104], [0108] – [0109], [0139] – [0140], [0151], [0153] – [0157], [0176] – [0191], [0266] – [0269], [0277], [0288] e.g. vector embedding … match); and
retrieving, from the vector repository, N additional vector embeddings of the plurality of vector embeddings for each matching vector embedding of the one or more matching vector embeddings based on a hierarchy of terms associated with the data asset (Takacs [0076], [0106], [0109], [0266] – [0268] e.g. [0267] When the group analysis module 110 receives a new incident 112 in vector representation form (FIG. 2, operation 204), the system 100 may initiate a vector-based fetching process that leverages the spatial relationships within the vector embedding space. The new incident 112 may be converted into a vector representation using the same embedding techniques applied to the plurality of incidents 102, ensuring consistency in the vector space representation. This conversion process may incorporate multiple suspect identifiers from the new incident 112 into a single composite vector representation, or may generate separate vectors for different types of suspect identifiers that are subsequently combined for analysis purposes. In more advanced embodiments, the vector query may adaptively home in on the nearest cluster by incorporating an initial promising neighbor and expanding toward the most relevant region of the space. [0268] The system 100 may fetch a group of incidents from the plurality of incidents 102 based on nearby neighbors to the new incident 112's vector representation in the vector embedding space (FIG. 2, operation 206). This fetching process may employ various nearest neighbor search algorithms, such as k-nearest neighbor (k-NN) searches, range queries within a specified radius, or more sophisticated approximate nearest neighbor techniques for large-scale datasets. Advanced embodiments may include any one or more of the following techniques, in any combination: routing via a coarse quantizer and probing adjacent cells in an inverted file index (IVF), optionally with product quantization (PQ) and/or optimized product quantization (OPQ), such as multi-probe IVF; performing a best-first traversal over a proximity graph, such as Hierarchical Navigable Small World (HNSW), that expands from the initial neighbor; and/or probing predicted neighboring buckets in multi-probe locality sensitive hashing (LSH). The vector-based fetching approach may enable the system 100 to identify incidents that are semantically related to the new incident 112 even when the suspect identifiers do not match exactly, capturing subtle variations and obfuscation attempts that fraudsters may employ to avoid detection.).
7. With respect to claim 2,
Takacs discloses determining the hierarchy of terms based on metadata stored in a metadata (Takacs [0047] – [0048], [0057], [0087], [0113], [0189], [0246], [0274], [0308] – [0309] e.g. metadata) repository associated with the vector repository.
8. Claims 13-14 are same as claims 1-2 and are rejected for the same reasons as applied hereinabove
Claim Rejections - 35 USC § 103
9. 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.
10. 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.
11. 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.
12. 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 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.
13. Claims 3-4, 6, 8-11 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Takacs in view of Nicholson (U.S. 20240338716 A1 hereinafter, “Nicholson”).
14. With respect to claim 3,
Although Takacs substantially teaches the claimed invention, Takacs does not explicitly indicate
wherein the hierarchy of terms indicates an ordinal position for each of the plurality of vector embeddings relative to the data asset.
Nicholson teaches the limitations by stating wherein the hierarchy of terms indicates an ordinal position (Nicholson [0004] – [0006], [0035], [0038] - [0040], [0055], [0061] – [0063] e.g. [0005] In a second embodiment, a system for handling user searches for digital assets in a database includes an online marketplace engine including a dense vector embedding tool and a user behavior signals tool, and a search engine comprising a scoring tool and a ranking tool. In the system, the dense vector embedding tool is configured to generate an embedded keyword vector for a user provided search query to the search engine and embedded asset vectors for digital assets stored in a database, the scoring tool is configured to generate a score for each of the embedded asset vectors based on a one or more user behavior signals stored in the database by the user behavior signals tool, and the ranking tool is configured to rank the embedded asset vectors based on the score and a similarity radius with the embedded keyword vector) (PENG abstract e.g. similarity ranking. [0039] In some embodiments, scoring tool 244 may operate in a search engine 234 that is configured for sparse vectors only. In this configuration, metadata for digital assets keywords can either come from the contributors, or be derived from other ML services. For each keyword, the system computes a similarity between the dense vector associated with that keyword (e.g., search query) and the dense vector associated with one or more digital assets that users have interacted with. Scoring tool 244 then generates, for each digital asset in database 252, a scored list of keywords, which can then be indexed by ranking tool 246. In some embodiments, scoring tool 244 scores the keywords according to the user behavior that went into creating the dense vectors for the search keywords, as provided by user behavior signals tool 242. Thus, scoring tool 244 extrapolates user behavior to digital assets that have been recently uploaded to database 252 and have no prior user interaction data. Ranking tool 246 can index the scores associated with the keywords, and incorporate these scores into its ranking formula, either in an unsupervised way, or using learning-to-rank algorithms. [0061] … a ranking tool (e.g., dense vector embedding tool 240, user behavior signals tool 242, scoring tool 244, and ranking tool 246).) for each of the plurality of vector embeddings relative to the data asset.
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the invention, in view of the teachings of Takacs and Nicholson, to overcome the drawback of the sparsity of the vector space substantially reduces the accuracy and nuance of the engine, leading to results that are likely to be rejected by the users, or substantially depart from user desirability (Nicholson [0003]).
15. With respect to claim 4,
Takacs further discloses
determining the ordinal position for the matching vector embedding; and
determining the N additional vector embeddings based on the ordinal position of the matching vector embedding (Takacs [0266] – [0268], [0303], [0307]).
16. With respect to claim 6,
Takacs further discloses wherein the ordinal positions for the N additional vector embeddings immediately follow (Takacs [0076], [0106], [0109], [0266] – [0268] e.g. expand) the ordinal position of the matching vector embedding.
17. With respect to claim 8,
Takacs further discloses wherein the one or more matching vector embeddings comprises a number (K) of highest-matching vector embeddings, among the plurality of vector embeddings, based on a similarity measure (Takacs [0087], [0108], [0145], [0181] - [0182], [0189], [0191], [0197], [0218], [0247], [0262], [0269], [0282], [0290], [0303], [0310], [0320], [0322] e.g. similarity score).
18. With respect to claim 9,
Takacs further discloses presenting each matching vector embedding of the K highest-matching vector embeddings as a tuple (Takacs abstract, [0010], [0048], [0071], [0075] -[0082], [0086], [0088], [0090] – [0092], [0102] - [0104], [0108] – [0109], [0139] – [0140], [0151], [0153] – [0157], [0176] – [0191], [0266] – [0269], [0277], [0288] e.g. vector) that includes the N additional vector embeddings associated therewith.
19. With respect to claim 10,
Nicholson further discloses ranking (Nicholson [0004] – [0006], [0035], [0038] - [0040], [0055], [0061] – [0063] e.g. rank) the K highest-matching vector embeddings based at least in part on the similarity measure.
20. With respect to claim 11,
Nicholson further discloses ranking (Nicholson [0004] – [0006], [0035], [0038] - [0040], [0055], [0061] – [0063] e.g. rank) the K highest-matching vector embeddings based at least in part on their ordinal positions.
21. Claims 15-20 are same as claims 1-4 and 8-11 and are rejected for the same reasons as applied hereinabove.
22. Claims 5 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Takacs in view of Nicholson, and further view of Verrier (U.S. 12566782 B1 hereinafter, “Verrier”).
23. With respect to claim 5,
Although Takacs and Nicholson combination substantially teaches the claimed invention, they do not explicitly indicate wherein the ordinal positions for the N additional vector embeddings immediately precede the ordinal position of the matching vector embedding.
Verrier teaches the limitations by stating wherein the ordinal positions for the N additional vector embeddings immediately precede (Verrier col. 11 lines 20-44, col. 12 lines 47-54 e.g. [col. 11 lines 20-44] (85) The vectorization step captures the semantic essence of each summary in a fixed-length numerical representation. These embeddings encapsulate semantic relationships in a format conducive to efficient computational processing. Once generated, the vector embeddings are integrated into the corresponding topic maps alongside the textual summaries. (86) The update process in operation 212 now involves a two-fold augmentation of the topic maps. First, the textual summaries are added to their respective topic map structures. Concurrently, one or more embodiments append the newly generated vector embeddings to the same topic map entries. This dual update ensures that each topic map contains both human-readable summaries and machine-optimized vector representations. (87) By incorporating these vector embeddings, the system enhances its capability for semantic similarity comparisons and efficient information retrieval. The embeddings facilitate rapid similarity searches, enabling more nuanced and contextually relevant topic identification in subsequent query processing steps. This augmented approach synergizes the benefits of human-interpretable summaries with computationally efficient vector representations, thereby enhancing the overall functionality and performance of the topic map system. [col. 12 lines 47-54] (99) In an embodiment, the topic maps 140 stored in the topic vault 130 are structured as complex objects, each including any of the following: a unique identifier, the topic name, the topic identifier, the topic description, the topic summary, a vector representation (e.g., an embedding) of the topic (for similarity matching), metadata such, as creation date, last updated date, and associated tenant ID, or a list or array of references to content items 146 relevant to the topic) (OU [0065] – [0066] e.g. [0065] generates embeddings using the same strategy as the original reference set, and appends the resulting vectors to the database … similarity search is constrained to vectors matching a specified tag set) the ordinal position of the matching vector embedding.
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the invention, in view of the teachings of Takacs, Nicholson and Verrier, to overcome the drawback of the sparsity of the vector space substantially reduces the accuracy and nuance of the engine, leading to results that are likely to be rejected by the users, or substantially depart from user desirability (Nicholson [0003]).
24. With respect to claim 7,
Verrier further discloses wherein the ordinal positions for a number (M) of the additional vector embeddings immediately precede the ordinal position of the matching vector embedding and the ordinal positions for the remaining M-N additional vector embeddings immediately follow the ordinal position of the matching vector embedding (Verrier col. 11 lines 20-44, col. 12 lines 47-54).
25. Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Takacs in view of Nicholson, and further view of COULTER et al (U.S. 20250053587 A1 hereinafter, “COULTER”).
26. With respect to claim 12,
Although Takacs and Nicholson combination substantially teaches the claimed invention, they do not explicitly indicate generating a prompt for a large language model (LLM) based at least in part on the K*N vector embeddings retrieved from the vector repository.
COULTER teaches the limitations by stating generating a prompt for a large language model (LLM) based at least in part on the K*N vector embeddings retrieved from the vector repository (COULTER [0071] – [0073], [0108] e.g. prompt … LLM).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the invention, in view of the teachings of Takacs, Nicholson and COULTER, to overcome the drawback of the sparsity of the vector space substantially reduces the accuracy and nuance of the engine, leading to results that are likely to be rejected by the users, or substantially depart from user desirability (Nicholson [0003]).
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon, if any, is considered pertinent to applicant's disclosure.
27. The examiner requests, in response to this office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
28. When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the reference cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SyLing Yen whose telephone number is 571-270-1306. The examiner can normally be reached on Mon-Fri 8:30am - 5:00pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sanjiv Shah can be reached at 571-272-4098. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SYLING YEN/Primary Examiner, Art Unit 2166
August 6, 2026
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