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 .
This communication is in response to the Amendment filed 2/26/2026.
Response to Arguments
Claims 1 – 14 and 16 – 21 are pending in this Office Action. After a further search and a thorough examination of the present application, claims 1 – 14 and 16 – 21 remain rejected.
Applicant's arguments filed with respect to claims 1 – 14 and 16 – 21 have been fully considered but they are not persuasive.
Applicant argues that there is no teaching in Pathak or Zhu of generating queries and the system does not compare new search queries to the generated queries. Additionally, the system in Pathak does not determine whether a new search query corresponds to a category associated with the generated queries based on the comparison.
In response to Applicant’s argument, the Examiner submits that Pathak in combination with Zhu teaches of generating queries and the system does not compare new search queries to the generated queries in paragraph 65 and 81 stating the partitioning component performs its operation upon the introduction of any new information item, such as a new input query. The partition established thereby remains in place for subsequent dialogue turns. A new partitioning may be appropriate depending on the nature of a question being posed in a current dialogue turn. Additionally, the system in Pathak does determine whether a new search query corresponds to a category associated with the generated queries based on the comparison in paragraph 65 and 107, it states the user progressively builds on a particular line of questioning over the first three dialogue turns, but then effectively starts with a new line of inquiry pertaining to a new topic in the fourth dialogue turn. In response to this query behavior, the prompt-managing component progressively increases the size of the instances of prompt information over the course of the first three dialogue turns, but then formulates a comparatively smaller instance of prompt information for the fourth dialogue turn. This fourth-turn behavior is performed because the prompt-managing component determines that the information imparted by the first three dialogue turns is not relevant to the topic raised in the fourth dialogue turn.
In regards with claim 21, Applicant argues that there is no teaching in Pathak or Zhu, alone or in combination of determining distances between the respective vector for each query of the first search queries and respective vector for each query of the second search queries or mapping each of the second search queries to a respective one of the first search queries based at least in part on the distances.
In response to Applicant’s argument, the Examiner submits that Pathak in combination with Zhu teaches determining distances between the respective vector for each query of the first search queries and respective vector for each query of the second search queries or mapping each of the second search queries to a respective one of the first search queries based at least in part on the distances. In paragraph 77 Pathak teaches mapping the input query to an embedding further mapped to a classification. Paragraph 82 – 83 further teach mapping the input query into a query embedding (e.g., a distributed vector VQ), and maps each dialogue part into a dialogue part embedding (e.g., a distributed vector VDP1 for the first dialogue part). The mapping component can use any neural network to perform mapping and the mapping component performs the mapping using a feed-forward neural network, a convolutional neural network. A relevance-evaluating component determines the proximity of the query embedding to each dialogue-part embedding. The relevance-evaluating component can use any metric to perform this assessing, such as cosine similarity, inner product, Euclidean distance, etc. The analysis performed by the mapping component and the relevance-evaluating component is said to be vector-based because it relies on comparisons of vectors in a vector space and is based on distance for similarity. Also see paragraphs 102 – 105 in regards with details of comparison based on distances.
Remaining claims in instant application recite the same subject matter and for the same reasons as cited above the rejection is maintained. Hence, Applicant’s arguments do not distinguish the claimed invention over the prior art of record. In light of the foregoing arguments, the 103 rejections are maintained.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1 – 14 and 16 – 21 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Pathak et al. (US 20240394477 A1) (‘Pathak’ hereinafter) further in view of Zhu et al. (US 20240168948 A1) (‘Zhu’ herein after).
With respect to claim 1, 16,
Pathak discloses method of facilitating content selection, the method comprising: generating, by a computing system, queries for a particular category, wherein generating the queries includes applying a text prompt as input to a language model trained on a knowledge base, and wherein the text prompt requests search queries indicative of user interest in the particular category (figures 1, 4, 14 – 17 and paragraphs 77, 127 – 128 teaches the generation of a response from the machine trained language model which had received a prompt/query along with context, Pathak); and selecting, by the computing system and responsive to new search queries entered by users, content items associated with the particular category for delivery to client devices of the users, wherein selecting the content items includes determining whether the new search queries correspond to the particular category at least in part by comparing the new search queries to a query set that includes the generated queries (figures 1, 4, 14 – 17 and paragraphs 77, 127 – 128 teaches the generation of a response from the machine trained language model which had received a prompt/query along with context where queries submitted by users are taken into consideration for context and compared with the content units, Pathak) and providing, by the computing system, the selected content items to the client devices of the users (figures 1, 4, 14 – 17, Pathak).
Pathak teaches facilitating content selection with the use of language models using prompts and queries but does not explicitly state benchmark queries.
Zhu teaches benchmark data and benchmark queries for use of related data and category related datasets along with prediction in paragraphs 38 and 42 – 49 where it teaches groups benchmark queries of benchmark queries into “benchmark workloads.” In accordance with an embodiment, a joint enumeration and/or random sampling technique is used to group benchmark queries of benchmark queries into benchmark workloads. Frequency and concurrency of benchmark workloads may be varied. Each benchmark workload may be executed for a period of time using various hardware and/or software configurations.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Pathak and Zhu because they are from the same field of invention. Furthermore, Zhu using the benchmark queries along with the trained data on historical queries helps predict the related and relevant content and profiles that match the prompt query.
With respect to claim 2,
Pathak as modified discloses the method of claim 1, wherein the knowledge base includes Internet information regarding the particular category (paragraphs 77 and 152 – 154, Pathak and figures 1, 3 and paragraphs 38 – 39 and 42 – 45, Zhu).
With respect to claim 3,
Pathak as modified discloses the method of claim 1, wherein the text prompt includes text reviews of one or more service providers associated with the particular category (paragraphs 77 and 152 – 154, Pathak and figures 1, 3, Zhu).
With respect to claim 4,
Pathak as modified discloses the method of claim 1, wherein the text prompt includes content of, or references, one or more websites of service providers associated with the particular category (paragraphs 77 and 152 – 154, Pathak and paragraphs 38 – 39 and 42 – 45, Zhu).
With respect to claim 5,
Pathak as modified discloses the method of claim 1, wherein the text prompt includes one or more search queries known to be associated with the particular category (figures 1, 4, paragraphs 152 – 154, Pathak and figures 1, 3 and paragraphs 43 – 44, Zhu).
With respect to claim 6,
Pathak as modified discloses the method of claim 1, wherein the text prompt includes text content of one or more digital advertisements of one or more service providers associated with the particular category (paragraphs 77, Pathak and paragraphs 38 – 39, Zhu).
With respect to claim 7,
Pathak as modified discloses the method of claim 1, wherein the text prompt includes text content of, or references, one or more search results for one or more search queries associated with the particular category (figures 1, 4, 14 – 17, paragraphs 152 – 154, Pathak and figures 1, 3, Zhu).
With respect to claim 8, 18,
Pathak as modified discloses the method of claim 1, wherein the text prompt includes one or more constraints on the benchmark queries, the one or more constraints preventing the benchmark queries from including one or more of: any benchmark query indicative of interest in a particular location; any benchmark query indicative of interest in buying a product rather than a service; any benchmark query indicative of interest in an online publication; or any benchmark query indicative of interest in instructions for providing self-service (paragraphs 77, 127 – 128, Pathak and paragraphs 38 and 42 – 49, Zhu).
With respect to claim 9, 19,
Pathak as modified discloses the method of claim 1, further comprising, for each query of the benchmark queries: determining, by the computing system, that the query satisfies one or more criteria and responsive to determining that the query satisfies the one or more criteria, retaining, by the computing system, the query as a benchmark query (figures 1, 3, paragraphs 38 and 42 – 49, Zhu).
With respect to claim 10,
Pathak as modified discloses the method of claim 9, wherein the one or more criteria include one or both of: satisfying a minimum frequency at which the query is entered by users and satisfying a threshold value for a performance metric indicative of how often users that enter the query select content associated with the particular category (paragraphs 152 – 154, Pathak and paragraphs 46 – 48 and 83, Zhu).
With respect to claim 11,
Pathak as modified discloses the method of claim 9, wherein generating the benchmark queries includes removing location-specific information associated with the benchmark queries (figures 1, 3, paragraphs 38 and 42 – 49, Zhu).
With respect to claim 12,
Pathak as modified discloses the method of claim 1, wherein the benchmark queries are a subset of the query set, and wherein the method further comprises: expanding, by the computing system, the benchmark queries to the query set, at least in part by mapping each of a plurality of search queries to a respective one of the benchmark queries (figures 1, 2, 7 Pathak and paragraphs 38 – 39, Zhu).
With respect to claim 13, 20,
Pathak as modified discloses the method of claim 12, wherein mapping each of the plurality of search queries to a respective one of the benchmark queries includes: using one or more machine learning models to embed each of the plurality of search queries and each of the benchmark queries as a respective vector in a multi-dimensional space; determining distances between the respective vector for each of the plurality of search queries and the respective vector for each of the benchmark queries and mapping each of the plurality of search queries to the respective one of the benchmark queries based at least in part on the distances (paragraphs 82 – 83 and 88, Pathak and figure 1, paragraphs 36 and 91, Zhu).
With respect to claim 14,
Pathak as modified discloses the method of claim 1, wherein generating the benchmark queries is performed by a first server of the computing system, and wherein selecting the content items for delivery to the client devices is performed by a second server of the computing system (figures 1, 4, 14 – 17, Pathak and figures 1, 3, Zhu).
With respect to claim 17,
Pathak as modified discloses the computing system of claim 16, wherein the text prompt includes one or more of: text reviews of one or more service providers associated with the particular category; content of, or references, one or more websites of service providers associated with the particular category; one or more search queries known to be associated with the particular category; text content of one or more digital advertisements of one or more service providers associated with the particular category; or text content of, or references, one or more search results for one or more search queries associated with the particular category (figures 1, 4, paragraphs 77 and 152 – 154, Pathak and figures 1, 3 and paragraphs 38 – 39 and 42 – 45, Zhu).
With respect to claim 21,
Pathak discloses a method of search query matching, the method comprising: obtaining, by a computing system, first search queries and second search queries (figures 4, 14 – 17 and paragraphs 127 – 128 teaches the prompts and queries along with context, Pathak); embedding, by the computing system and using one or more models, each query of the first search queries and the second search queries as a respective vector in a multi-dimensional space (paragraphs 82 – 82 and 88, Pathak); determining, by the computing system, distances between (i) the respective vector for each query of the first search queries and (ii) the respective vector for each query of the second search queries (paragraphs 82 – 83 and 88, Pathak); and mapping, by the computing system, each query of the second search queries to a respective one of the first search queries based at least in part on the distances (figures 1, 2, 7, paragraph 77 and 152 – 154 Pathak).
Pathak teaches facilitating content selection with the use of language models using prompts and queries but does not explicitly state machine learning models.
Zhu teaches using training and machine learning models in paragraphs 36, 42 – 49, 91 where it teaches machine learning models groups are used by queries to be trained and for benchmark queries. In accordance with an embodiment, a joint enumeration and/or random sampling technique is used to group benchmark queries of benchmark queries into benchmark workloads. Frequency and concurrency of benchmark workloads may be varied. Each benchmark workload may be executed for a period of time using various hardware and/or software configurations.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Pathak and Zhu because they are from the same field of invention. Furthermore, Zhu using the benchmark queries along with the trained data on historical queries helps predict the related and relevant content and profiles that match the prompt query.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20240394519 A1 teaches obtaining a benchmark query specifying a target of a benchmark. The method includes determining at least one target block to be used to obtain the benchmark prediction result corresponding to the benchmark query among pre-stored blocks based on the benchmark query. The blocks comprise a node identifying a function or an operation constituting a model, and an edge connecting nodes. The method includes obtaining the benchmark prediction result corresponding to the benchmark query, using a benchmark result related to the determined at least one target block.
US 20240119052 A1 teaches automatically tuning quantization-based approximate nearest neighbors (ANN) search methods and systems (e.g., search engines) to perform at the speed-recall pareto frontier. With a desired search cost or recall as input, the embodiments employ Lagrangian-based methods to perform constrained optimization on theoretically-grounded search cost and recall models. The resulting tunings, when paired with the efficient quantization-based ANN implementation of the embodiments, exhibit excellent performance on standard benchmarks while requiring minimal tuning or configuration complexity.
US 20170140419 A1 teaches a system that collects and analyzes signals from online sources, producing reports, analytics, benchmarks, and alerts regarding offline activity at the local/store-front level. The system normalizes the signals from various sources, analyzes the signals at the individual location level, aggregates the data across various dimensions, builds benchmarks for comparison, and fires triggers notifying appropriate people upon detecting a meaningful variance.
US 20150134694 A1 teaches access to comparison metrics data relating to the comparison of a test or target group with a reference group, such as a benchmark group. The apparatus comprises a database of reference metrics data determined from testing of members of a reference population; selecting target group metrics data, the metrics data determined from testing of the members of the target group and associated with metadata relating to the target group; selecting at least one item of metadata; selecting a reference group from the reference population being associated with reference group metrics data and outputting the resulting comparison data.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAVNEET K GMAHL whose telephone number is 571-272-5636.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SANJIV SHAH can be reached on (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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/NAVNEET GMAHL/Examiner, Art Unit 2166 Dated: 6/18/2026
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166
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