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 Office Action has been issued in response to Applicant’s Communication of application S/N 18/425,972 filed on April 27, 2026. Claims 1-2, 4-13, 15-17, 19, 21-22, and 24-25 are currently pending with the application.
Examiner Notes
The 35 USC § 101 rejection previously raised has been withdrawn.
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 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.
Claim(s) 1, 11, 21, 24, and 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fusco et al. (U.S. Patent No.: 11361571 B1) hereinafter Fusco, in view of Bennett et al. (U.S. Publication No.: US 20100293174 A1) hereinafter Bennett, and further in view of Na et al. (U.S. Publication No.: US 20240249335 A1) hereinafter Na.
As to claim 1:
Fusco discloses:
A system comprising: one or more processors; and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations comprising:
receiving a first query [Column 6 Lines 43-46 teaches document centric knowledge graphs may often use word clouds now comprised in non-phrases (or multi-word expressions) to aid in searches (e.g., for literature) and/or complex queries.]
transforming, using a sequence transformer model, the first queries into a first vector of query embeddings [Column 1 Lines 65-67 and Column 2 Lines 1-4 teach each of the candidate multi-word expressions as a distance between an embedding vector corresponding to the identified text snippet and an embedding vector corresponding to the candidate multi-word expression, to select remaining expressions from the candidate multi-word expressions using a function of a specificity value. Column 6 Lines 43-46 teaches document centric knowledge graphs may often use word clouds now comprised in non-phrases (or multi-word expressions) to aid in searches (e.g., for literature) and/or complex queries. Column 11 Lines 34-38 teach a multi-word vector 416 is created from the recognized, 406, multi-word “machine learning” and multiplied (using the vector dot product) by the topic vector 404 if the text snippet 402 giving a distance of, e.g., 0.7. Note: Using a machine learning model generate vector embeddings for a first of a plurality of queries reads on the claims.]
transforming, using a machine-learning classifier, the query vector of queries embeddings into a first specificity score for a specificity of the first query [Column 2 Lines 57-64 teach the specificity score value may be determined by using a pre-trained static embedding matrix or by using context dependent embeddings originating from a transformer based-system. For this, a pre-trained bidirectional encoder representations from transformers (BERT) or any other transformer-based language model may be used. Column 6 Lines 43-46 teaches document centric knowledge graphs may often use word clouds now comprised in non-phrases (or multi-word expressions) to aid in searches (e.g., for literature) and/or complex queries. Column 10 Lines 1-3 teaches the method 100 comprises determining (106) a specificity score value for each of the candidate multi-word expressions. Note: The examiner interprets candidate multi-word expressions include the claimed first query and second query, wherein a specificity score is determined from each vector embedding from a query.]
Fusco discloses some of the limitations as set forth in claim 1 but does not appear to expressly disclose setting a second specificity score, for a specificity of the second query, based on the first specificity score and based on the first query and the second query being sibling queries that share at least two types of explicit attributes, training a machine-learning classifier by adjusting parameter weights of the machine-learning classifier based on the first query and the second query being known queries, based on the first score for the first query, based on the second query, and based on the second score for the second query, and providing, for display in the user interface and based on based on the third specificity score, results using the third query.
Bennett discloses:
setting a second specificity score, for a specificity of the second query, based on the first specificity score and based on the first query and the second query being sibling queries [Paragraph 0050 teaches query category distributions may be utilized to enhance query output from a search of a set of documents. In one embodiment, query ambiguity may be determined for a query by using an ambiguity function that accounts for a spread of a distribution of categories for the query, for example, to generate an ambiguity score for the query. Paragraph 0052 teaches a resulting ambiguity score may be determined by applying an ambiguity function to a desired number of categories in the distribution. Paragraph 0058 teaches a query refinement score can be determined for an alternate query (e.g., a refinement of a first query that is composed after the first query does not return desired results) that can be used to measure whether the alternate query is a more specific refinement of one or more portions of the query, a more general refinement of one or more portions of the query, or an orthogonal concept to one or more portions of the query (e.g., neither more general nor more specific). For example, where a first query may be for "football," and alternate query for "professional football" would be a more specific refinement, an alternate query for "sports" would be a more general refinement, and an alternate query for "telephones" would be orthogonal. Note: Determining a query refinement score for an alternate query (second query) based on the alternate query being an alternate to a first query with an ambiguity score (first specificity score) reads on the claims.] that share at least two types of explicit attributes [Paragraph 0053 teaches a query similarity between the query and an alternate query may be determined by comparing the distribution of the categories associated with the query and the distribution of the categories associated with the alternate query. For example, a first query for "bad credit mortgage" may result in a similar topic distribution as a second query for "refinance." Paragraph 0054 teaches the distribution for the query "bad credit mortgage" can be shown in the chart 700, and the distribution for the query "refinance" can be shown in the chart 750. In the exemplary distributions of topics 702 and 752, similar topics have been identified for the queries. Paragraph 0055 teaches a query similarity score for these two queries, for example, can be determined by calculating a similarity function using the distribution of the categories associated with the first query and the distribution of the categories associated with the second query. Note: Queries that share similar topics and topic distributions, wherein topics and topic distributions are interpreted to be a plurality of attribute types (at least two) or topics reads on the claims.]
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 teaching of the cited references and modify the invention as taught by Fusco, by incorporating determining a query refinement score for an alternate query (second query) based on the alternate query being an alternate to a first query with an ambiguity score (first specificity score) that are similar, as taught by Bennett (see Paragraph 0050, 0052, 0053, and 0058), because the two publications are directed to query processing; incorporating determining a query refinement score for an alternate query (second query) based on the alternate query being an alternate to a first query with an ambiguity score (first specificity score) that are similar facilitates query classification by utilizing existing topic distributions for the query, or having to begin a new query classification (see Bennett Paragraph 0058).
Fusco and Bennett discloses some of the limitations as set forth in claim 1 but does not appear to expressly disclose training a machine-learning classifier by adjusting parameter weights of the machine-learning classifier based on the first query and the second query being known queries, based on the first score and based on setting the second specificity score, and based on the second score for the second query, wherein the machine-learning classifier includes a logistic regression machine learning model, a k--2- nearest neighbors machine learning model, a convolutional neural network machine learning model, a trees machine learning model, or a random forest machine learning model.
Na discloses:
training a machine-learning classifier by adjusting parameter weights of the machine-learning classifier based on the first query and the second query being known queries, based on the first score and based on setting the second specificity score, and based on the second score for the second query, wherein the machine-learning classifier includes a logistic regression machine learning model, a k--2- nearest neighbors machine learning model, a convolutional neural network machine learning model, [Paragraph 0052 teaches may use machine learning models to perform functionalities described herein. Example machine learning models include regression models, support vector machines, naïve bayes, decision trees, k nearest neighbors, random forest, boosting algorithms, k-means, and hierarchical clustering. The machine learning models may also include neural networks, such as perceptrons, multilayer perceptrons, convolutional neural networks. Paragraph 0053 teaches each machine learning model includes a set of parameters. A set of parameters for a machine learning model are parameters that the machine learning model uses to process an input… the set of parameters for a neural network may include weights and biases that are applied at each neuron in the neural network. Paragraph 0055 teaches updates the set of parameters for the machine learning model based on the score generated by the loss function. For example, the machine learning training module 230 may apply gradient descent to update the set of parameters. Paragraph 0069 teaches the online concierge system 140 measures the specificity of a search query with a machine learning model trained on a set of example queries with manually labeled specificity values. Note: Training a convolutional neural network based on a first second query and their specificity scores using adjustable weights that are updated reads on the claims.] a trees machine learning model, or a random forest machine learning model
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 teaching of the cited references and modify the invention as taught by Fusco and Bennett, by incorporating training a convolutional neural network based on a first second query and their specificity scores using adjustable weights that are updated, as taught by Na (see Paragraph 0052, 0053, 0055 and 0069), because the three publications are directed to query processing; incorporating training a convolutional neural network based on a first second query and their specificity scores using adjustable weights that are updated provides an improvement directed to personalizing results to a user (see Na Paragraph 0009).
Claim(s) 11 and 21 are similarly rejected because they are similar in scope.
As to claim 24:
Fusco, Bennett, and Na discloses all of the limitations as set forth in claim 1.
Bennett also discloses:
The computer-implemented method of claim 11, wherein the sibling queries are different [Paragraph 0053 teaches a query similarity between the query and an alternate query may be determined by comparing the distribution of the categories associated with the query and the distribution of the categories associated with the alternate query. For example, a first query for "bad credit mortgage" may result in a similar topic distribution as a second query for "refinance." Paragraph 0054 teaches the distribution for the query "bad credit mortgage" can be shown in the chart 700, and the distribution for the query "refinance" can be shown in the chart 750. In the exemplary distributions of topics 702 and 752, similar topics have been identified for the queries.]
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 teaching of the cited references and modify the invention as taught by Fusco, by incorporating determining a query refinement score for an alternate query (second query) based on the alternate query being an alternate to a first query with an ambiguity score (first specificity score) that are similar, as taught by Bennett (see Paragraph 0050, 0052, 0053, and 0058), because the two publications are directed to query processing; incorporating determining a query refinement score for an alternate query (second query) based on the alternate query being an alternate to a first query with an ambiguity score (first specificity score) that are similar facilitates query classification by utilizing existing topic distributions for the query, or having to begin a new query classification (see Bennett Paragraph 0058).
As to claim 25:
Fusco, Bennett, and Na discloses all of the limitations as set forth in claim 1.
Na also discloses:
The computer-implemented method of claim 11, wherein the sibling queries include one or more common terms [Paragraph 0080 teaches the search query 500a, “bread,” is a less specific search query and thus has a low query specificity score. Because the search query has a low query specificity score. Paragraph 0081 teaches in FIG. 5B, the search query 500b, “rye bread” is a more specific search query and thus has a high query specificity score. Also see Figures 5A and 5B.]
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 teaching of the cited references and modify the invention as taught by Fusco and Bennett, by incorporating training a convolutional neural network based on a first second query and their specificity scores using adjustable weights that are updated, as taught by Na (see Paragraph 0052, 0053, 0055 and 0069), because the three publications are directed to query processing; incorporating training a convolutional neural network based on a first second query and their specificity scores using adjustable weights that are updated provides an improvement directed to personalizing results to a user (see Na Paragraph 0009).
Claim(s) 2 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fusco et al. (U.S. Patent No.: 11361571 B1) hereinafter Fusco, in view of Bennett et al. (U.S. Publication No.: US 20100293174 A1) hereinafter Bennett, in view of Na et al. (U.S. Publication No.: US 20240249335 A1) hereinafter Na, in view of Winters et al. (U.S. Publication No.: US 20150058108 A1) hereinafter Winters, and in view of Nipko et al. (U.S. Patent No.: US 8775231 B1) hereinafter Nipko.
As to claim 2:
Fusco, Bennett, and Na discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose wherein generating the first specificity score for the first query further comprises: determining a specificity count of unique items purchased based on the first query, determining a co-purchase probability for the first query, and generating the first specificity score for the first query based on the specificity count for the first query and the co-purchase probability for the first query.
Winters discloses:
The system of claim 1, wherein generating the first specificity score is based on a specificity count of unique items purchased based on the first query [Paragraph 0322 teaches the transaction records (301) are aggregated to generate aggregated measurements (e.g., variable values (321)) that are not specific to a particular transaction, such as frequencies of purchases made with different merchants or different groups of merchants, the amounts spent with different merchants or different groups of merchants, and the number of unique purchases across different merchants or different groups of merchants, etc.. Note: Aggregating (a first query) data that includes a count of unique purchases reads on the claims.];
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 teaching of the cited references and modify the invention as taught by Fusco, Bennett, and Na, by incorporating aggregating (a first query) data that includes a count of unique purchases, as taught by Winters (see Paragraph 0322), because the four publications are directed to query processing; incorporating aggregating (a first query) data that includes a count of unique purchases improves the capabilities of the aggregated measurements in indicating certain aspects of the spending behavior of the customers (see Winters Paragraph 0364).
Fusco, Bennett, Na, and Winters discloses all of the limitations as set forth in claim 1 and some of claim 2 but does not appear to expressly disclose determining a co-purchase probability for the first query, and generating the first specificity score for the first query based on the specificity count for the first query and the co-purchase probability for the first query.
Nipko discloses:
a co-purchase probability for the first query [Column 8 Lines 34-38 teach the affinity relationship among product that provides the probability of products being purchased together for the product groups 930 is calculated using a technique such as affinity analysis.];
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 teaching of the cited references and modify the invention as taught by Fusco, Bennett, Na, and Winters, by incorporating the probability of products being purchased together, as taught by Nipko (see Column 8 Lines 34-38), because the five publications are directed to query processing; incorporating the probability of products being purchased together provides efficient identifying reliable purchase pattern profiles through scientific analysis of customer data (see Nipko Abstract).
Claim 12 is similarly rejected because it is similar in scope.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fusco et al. (U.S. Patent No.: 11361571 B1) hereinafter Fusco, in view of Bennett et al. (U.S. Publication No.: US 20100293174 A1) hereinafter Bennett, in view of Na et al. (U.S. Publication No.: US 20240249335 A1) hereinafter Na, and further Gabbai et al. (U.S. Publication No.: US 20170011136 A1) hereinafter Gabbai.
As to claim 4:
Fusco, Bennett, and Na discloses some of the limitations as set forth in claim 1 but does not appear to expressly disclose wherein setting the second specificity score for the second query further comprises: setting the second specificity score for the second query to represent a lower specificity than the first specificity score for the first query.
Gabbai discloses:
The system of claim 1, wherein setting the second specificity score for the second query further comprises: setting the second specificity score for the second query to represent a lower specificity than the first specificity score for the first query [Paragraph 0012 teaches obtaining a first search query and comparing a level of specificity of the first search query to a search threshold. When the level of specificity is below the search threshold, the method may include providing visually guided search refinement to construct a second search query. Paragraph 0048 teaches first, second, third, fourth, and fifth search refinement options 210a, 210b, 210c, 210d, and 210e, referred to herein collectively as the search refinement options 210. Paragraph 0067 teaches the level of specificity of the first search query may be determined based on a number of words in the first search query. More words in the first search query may indicate that the first search query includes a higher level of specificity. Note: A second query that has fewer words than a first query resulting in the second query having a lower specificity than a first query that has higher specificity due to having a higher number of words reads on the claims. For example, a first query could be, Curved LCD television of greater than 60 inches by Sony or Samsung or LG or Sharp but not Phillips – in the context of the cited portion of Gabbai, the specificity score could be 19 since it has 19 words. A second query that is a subquery that is smaller (see Vanderberg) could be, Curved LCD television of greater than 60 inches by Sony or Samsung or LG or Sharp – which, in the context of the cited portion of Gabbai, the specificity score would be set to (setting) lower value since it has fewer words.]
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 teaching of the cited references and modify the invention as taught by Fusco, Bennett, and Na, by incorporating a second query that has fewer words than a first query resulting in the second query having a lower specificity than a first query that has higher specificity due to having a higher number of words, as taught by Gabbai (see Paragraph 0012, 0048, and 0067), because the four publications are directed to query processing; incorporating a second query that has fewer words than a first query resulting in the second query having a lower specificity than a first query that has higher specificity due to having a higher number of words provide adaptive search refinement to assist a user to identify material of interest (see Gabbai Paragraph 0011).
Claim(s) 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fusco et al. (U.S. Patent No.: 11361571 B1) hereinafter Fusco, in view of Bennett et al. (U.S. Publication No.: US 20100293174 A1) hereinafter Bennett, in view of Na et al. (U.S. Publication No.: US 20240249335 A1) hereinafter Na, in view of Gabbai et al. (U.S. Publication No.: US 20170011136 A1) hereinafter Gabbai, and further in view of Lee et al. (U.S. Publication No.: US 20200117760 A1) hereinafter Lee.
As to claim 5:
Fusco, Bennett, and Na discloses all of the limitations as set forth in claim 1 but does not appear expressly disclose wherein setting the second specificity score for the second query comprises: setting the second specificity score for the second query and the first specificity score for the first query to be equivalent to a specificity of queries that are siblings to the first query and the second query.
Gabbai discloses:
The system of claim 1, wherein setting the second specificity score for the second query comprises: setting the second specificity score for the second query and the first specificity score for the first query to be equivalent to a specificity of queries that are siblings to the first query and the second query [Paragraph 0012 teaches for each iteration of providing the multiple search refinement options, at least some of the multiple search refinement options are different. Paragraph 0048 teaches first, second, third, fourth, and fifth search refinement options 210a, 210b, 210c, 210d, and 210e, referred to herein collectively as the search refinement options 210. Paragraph 0067 teaches the level of specificity of the first search query may be determined based on a number of words in the first search query. More words in the first search query may indicate that the first search query includes a higher level of specificity. Note: Determining queries (third or fourth query) that have the same word count as a first query and second query (siblings to the first query and the second query) and determining (setting) the specificity for the first and second query to the same maximum specificity third and fourth query. For example, a first query could be, Curved LCD television of greater than 60 inches by Sony or Samsung or LG or Sharp but not Phillips – in the context of the cited portion of Gabbai, the specificity score could be 19 since it has 19 words, a second query that is a sibling based on a determined same number of words (see Lee and Xu) could be, Curved LCD television of greater than 60 inches by Sony or Samsung or LG or Sharp but not Panasonic – in the context of the cited portion of Gabbai, the specificity score could be 19 since it has 19 words, a third query that is a sibling based on a determined same number of words (see Lee and Xu) could be, Curved LCD television of greater than 60 inches by Sony or Samsung or LG or Sharp but not Asus – in the context of the cited portion of Gabbai, the specificity score could be 19 since it has 19 words, and a fourth query that is a sibling based on a determined same number of words (see Lee and Xu) could be, Curved LCD television of greater than 60 inches by Sony or Samsung or LG or Sharp but not Lenovo – in the context of the cited portion of Gabbai, the specificity score could be 19 since it has 19 words. These example queries represent determining (setting) the specificity of a first and second query is equivalent to other sibling queries that have the same number of words and therefore has the same level of specificity.]
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 teaching of the cited references and modify the invention as taught by Fusco, Bennett, and Na, by incorporating a second query that has fewer words than a first query resulting in the second query having a lower specificity than a first query that has higher specificity due to having a higher number of words, as taught by Gabbai (see Paragraph 0012, 0048, and 0067), because the three publications are directed to query processing; incorporating a second query that has fewer words than a first query resulting in the second query having a lower specificity than a first query that has higher specificity due to having a higher number of words provide adaptive search refinement to assist a user to identify material of interest (see Gabbai Paragraph 0011).
Fusco, Bennett, Na, and Gabbai discloses all of the limitations as set forth in claim 1 and some of 5 but does not appear to expressly disclose a maximum.
Lee discloses:
a maximum [Paragraph 0077 teaches responsive to a determination that the number of words of the query is higher than a maximum number of words (e.g., 10 words, 15 words, etc.), the query and/or the representation of the query may be discarded and/or may not be stored in the search history profile.]
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 teaching of the cited references and modify the invention as taught by Fusco, Bennett, Na, and Gabbai, by incorporating storing queries that do not surpass a maximum number of words, as taught by Lee (see Paragraphs 0077), because the four publications are directed to query processing; incorporating storing queries that do not surpass a maximum number of words provides improvement the functionality of a computer-implemented search engine (see Lee Paragraph 0132).
Claim 15 is similarly rejected because it is similar in scope.
Claim(s) 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fusco et al. (U.S. Patent No.: 11361571 B1) hereinafter Fusco, in view of Bennett et al. (U.S. Publication No.: US 20100293174 A1) hereinafter Bennett, in view of Na et al. (U.S. Publication No.: US 20240249335 A1) hereinafter Na, and further in view of Eberlein et al. (European Patent Application No.: EP 2682877 A1) hereinafter Eberlein.
As to claim 6:
Fusco, Bennett, Na discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose wherein the operations further comprise: excluding attributes of product type, product type descriptor, brand, product line, or miscellaneous in determining that first query and the second query are sibling queries.
Eberlein discloses:
The system of claim 1 wherein the operations further comprise: excluding attributes of product type, product type descriptor, brand, product line, or miscellaneous in determining that first query and the second query are sibling queries [Paragraph 0037 teaches with respect to the product sales data example of FIG 2, mobile analytics engine 110 may dynamically modify query 230 by excluding the "Product" attribute, which has an aggregation grade.]
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 teaching of the cited references and modify the invention as taught by Fusco, Bennett, Na, by incorporating excluding the "Product" attribute, as taught by Eberlein (see Paragraph 0037), because the seven publications are directed to query processing; incorporating excluding the "Product" attribute provides prioritizing the transfer of desirable attributes (see Eberlein Paragraph 0024).
Claim 16 is similarly rejected because it is similar in scope, however, it is unpatentable over Fusco, Bennett, Na, Gabbai, Lee, and Eberlein.
Claim(s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fusco et al. (U.S. Patent No.: 11361571 B1) hereinafter Fusco, in view of Bennett et al. (U.S. Publication No.: US 20100293174 A1) hereinafter Bennett, in view of Na et al. (U.S. Publication No.: US 20240249335 A1) hereinafter Na, and further in view of Maheshwari et al. (U.S. Publication No.: US 20220197900 A1) hereinafter Maheshwari.
As to claim 7:
Fusco, Bennett, Na discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose wherein setting the second specificity score comprises: applying token-level comparison attributes across identical attributes of the first query and the second query.
Maheshwari discloses:
The system of claim 1, wherein setting the second specificity score further comprises:
applying token-level comparison attributes across identical attributes of the first query and the second query [Paragraph 0030 teaches one or more of the similar queries may have identical tokens. Paragraph 0094 teaches each query token may be determined based on structural and/or relationship attributes between different query expressions, as previously described. Note: Determining that a first and second query have identical tokens wherein tokens are associated attributes based on a comparison reads on the claims.]
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 teaching of the cited references and modify the invention as taught by Fusco, Bennett, Na, by incorporating determining that a first and second query have identical tokens wherein tokens are associated attributes based on a comparison, as taught by Maheshwari (see Paragraph 0030 and 0094), because the four publications are directed to query processing; by incorporating determining that a first and second query have identical tokens wherein tokens are associated attributes based on a comparison provides optimizing query performance (see Eberlein Paragraph 0002).
Claim 17 is similarly rejected because it is similar in scope.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fusco et al. (U.S. Patent No.: 11361571 B1) hereinafter Fusco, in view of Bennett et al. (U.S. Publication No.: US 20100293174 A1) hereinafter Bennett, in view of Na et al. (U.S. Publication No.: US 20240249335 A1) hereinafter Na, and further in view of Botros (U.S. Patent No.: US 8498986 B1) hereinafter Botros.
As to claim 8:
Fusco, Bennett, Na discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose wherein the machine-learning classifier is a binary classifier.
Botros discloses:
The system of claim 1, wherein the machine-learning classifier is a binary classifier [Column 4 Lines 3-4 teaches the adaptive learning machine 102 as an SVM may be a classifier that provides a binary output.]
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 teaching of the cited references and modify the invention as taught by Fusco, Biadsy and Gabbai , by incorporating adaptive learning machine that is a classifier with binary output, as taught by Botros (see Column 4 Lines 3-4), because the four publications are directed to query processing; by incorporating adaptive learning machine that is a classifier with binary output provides an advantage in classifying data (see Botros Column 2 Lines 47-65).
Claim(s) 9 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fusco et al. (U.S. Patent No.: 11361571 B1) hereinafter Fusco, in view of Bennett et al. (U.S. Publication No.: US 20100293174 A1) hereinafter Bennett, in view of Na et al. (U.S. Publication No.: US 20240249335 A1) hereinafter Na, and further in view of Laban et a. (U.S. Publication No.: US 20230419048 A1) hereinafter Laban.
As to claim 9:
Fusco, Bennett, and Na discloses all of the limitations as set forth in claim 1.
Na also discloses:
The system of claim 1, wherein the operations further comprise: generating, using the machine learning classifier, a third specificity score for a third query received via a user interface [Paragraph 0069 teaches the online concierge system 140 measures the specificity of a search query with a machine learning model trained on a set of example queries with manually labeled specificity values. Note: Training a convolutional neural network based on a first, second, and third query and their specificity scores using adjustable weights that are updated reads on the claims.]
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 teaching of the cited references and modify the invention as taught by Fusco and Bennett, by incorporating training a convolutional neural network based on a first second query and their specificity scores using adjustable weights that are updated, as taught by Na (see Paragraph 0052, 0053, 0055 and 0069), because the three publications are directed to query processing; incorporating training a convolutional neural network based on a first second query and their specificity scores using adjustable weights that are updated provides an improvement directed to personalizing results to a user (see Na Paragraph 0009).
Fusco, Bennett, and Na discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose wherein the operations further comprise: determining whether the third specificity score for the third query meets a predetermined threshold.
Laban discloses:
determining whether the specificity score for the query meets a predetermined threshold [Paragraph 0058 teaches answer consolidation submodule 133 may compute the specificity score of the candidate question and compare the specificity score with a threshold value. If the specificity score is less than or equal to the threshold value, the answer consolidation model may determine the candidate question to be a vague question.]
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 teaching of the cited references and modify the invention as taught Fusco, Bennett, and Na, by incorporating specificity score is less than or equal to the threshold value, as taught by Laban (see Paragraph 0058), because the four publications are directed to query processing; incorporating specificity score is less than or equal to the threshold value improves the user experience(see Laban Paragraph 0025).
Claim 19 is similarly rejected because it is similar in scope.
Claim(s) 10, 19, and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fusco et al. (U.S. Patent No.: 11361571 B1) hereinafter Fusco, in view of Bennett et al. (U.S. Publication No.: US 20100293174 A1) hereinafter Bennett, in view of Na et al. (U.S. Publication No.: US 20240249335 A1) hereinafter Na, in view of Laban et al. (U.S. Publication No.: US 20230419048 A1) hereinafter Laban, and further in view of Zhang (U.S. Publication No.: US 20210027485 A1) hereinafter Zhang.
As to claim 10:
Fusco, Bennett, Na, and Laban discloses all of the limitations as set forth in claim 1 and 9.
Na also discloses:
The system of claim 9, further comprising third specificity score and third query [Paragraph 0069 teaches the online concierge system 140 measures the specificity of a search query with a machine learning model trained on a set of example queries with manually labeled specificity values. Note: Training a convolutional neural network based on a first and second and third query and their specificity scores using adjustable weights that are updated reads on the claims.]
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 teaching of the cited references and modify the invention as taught by Fusco and Bennett, by incorporating training a convolutional neural network based on a first second query and their specificity scores using adjustable weights that are updated, as taught by Na (see Paragraph 0052, 0053, 0055 and 0069), because the three publications are directed to query processing; incorporating training a convolutional neural network based on a first second query and their specificity scores using adjustable weights that are updated provides an improvement directed to personalizing results to a user (see Na Paragraph 0009).
Fusco, Bennett, Na, and Laban discloses all of the limitations as set forth in claim 1, 9, and some of claim 10 but does not appear to expressly disclose when the score for the query meets the predetermined threshold providing for display information regarding out-of-stock items in response to a search using the query.
Zhang discloses:
when the score for the query meets the predetermined threshold providing for display information regarding out-of-stock items in response to a search using the query [Paragraph 0047 teaches applying the post-processing rules removes, from the list of the identified objects, (i) objects associated with confidence scores that are below a threshold. Paragraph 0124 teaches the neural network used to detect objects and predict their status is trained to detect different breads, pastries, and other bakery items and the display areas that contain them. The annotations for the image 400 include a bounding box for each distinct display region identified by the model, along with a classification of the display region as in-stock, low-stock, or out-of-stock, accompanied by a confidence score for the prediction. Note: Displaying out-of-stock data associated with an item in response to a score that meets a threshold requirement of bellowing a threshold reads on the claims.]
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 teaching of the cited references and modify the invention as taught by Fusco, Bennett, Na, and Laban, by incorporating displaying out-of-stock data associated with an item in response to a score that meets a threshold requirement of bellowing a threshold, as taught by Zhang (see Paragraph 0047 and 0124), because the five applications are directed to query processing; incorporating displaying out-of-stock data associated with an item in response to a score that meets a threshold requirement of bellowing a threshold improves the efficiency of operations (see Zhang Paragraph 0068).
Claim(s) 19 and 22 are similarly rejected because they are similar in scope. Examiner notes claim 19 has similar claim limitations therefore it is included in the claim 10 analysis.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fusco et al. (U.S. Patent No.: 11361571 B1) hereinafter Fusco, in view of Bennett et al. (U.S. Publication No.: US 20100293174 A1) hereinafter Bennett, in view of Na et al. (U.S. Publication No.: US 20240249335 A1) hereinafter Na, and further in view of Fujiki et al. (U.S. Patent No.: US 10691702 B1) hereinafter Fujiki.
As to claim 13:
Fusco, Bennett, and Na discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose wherein propagating the first specificity score for the first query to generate the second specificity score for the second query further comprises: determining that the second query is equivalent to the first query and setting the second specificity score for the second query to be equivalent to the first specificity score for the first query.
Fujiki discloses:
The system of claim 11, wherein setting the second specificity score for the second query comprises setting the second specificity score for the second query to be equivalent to the first specificity score for the first query [Column 17 Lines 40-48 teach server 320 may assign the same score to a particular category for different queries that include different quantities of terms associated with the particular category. For example, assume that a first query includes the terms “film” and “movie,” while a second query includes the term “movie.” In such an implementation, server 320 may assign a score, such as 1, 100, etc., to the particular category for the first query, while assigning the same score to the particular category for the second query. Note: Based on determining the queries are equivalent based on a particular category, assigning the same score to the category of the query.]
based on determining that the second query is equivalent to the first query [Column 17 Lines 40-48 teach server 320 may assign the same score to a particular category for different queries that include different quantities of terms associated with the particular category. For example, assume that a first query includes the terms “film” and “movie,” while a second query includes the term “movie.” In such an implementation, server 320 may assign a score, such as 1, 100, etc., to the particular category for the first query, while assigning the same score to the particular category for the second query. Note: Determining that a first query and second query are equivalent based on a particular category of the query reads on the claims.];
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 teaching of the cited references and modify the invention as taught by Fusco, Bennett, and Na, by incorporating determining a first query and second query are equivalent based on a particular category and assigning the same score to the category that is included in the query, as taught by Fujiki (see Column 17 Lines 40-48), because the four publications are directed to query processing; incorporating determining a first query and second query are equivalent based on a particular category and assigning the same score to the category that is included in the query efficiently provides relevant information to a user (see Fujiki Column 6 Line 56).
Response to Arguments
Applicant’s arguments directed to the 103 rejections with respect to amended claim(s) 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
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/EARL LEVI ELIAS/Examiner, Art Unit 2169
/SHERIEF BADAWI/Supervisory Patent Examiner, Art Unit 2169
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