DETAILED ACTION
This action is in response to the application filed on January 30th, 2025. Claims 1-20 are pending and have been examined.
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 .
Specification
The disclosure is objected to because of the following informalities:
Line 4 of [0078] of the specification reads “query breath score” but should read “query breadth score”.
Line 2 of [0088] of the specification references Fig. 4 but appears that it should be referencing Fig. 3.
Appropriate correction is required.
Claim Objections
Claims 2 and 12 objected to because of the following informalities: Each of the claims read “query specific score”, but should read “query specificity score” in line 2. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "a prompt" twice, in lines 20 and 34, and “the prompt” in various other lines. There is insufficient antecedent basis for this limitation in the claim, due to the two separately introduced instances of “a prompt”.
Claim 1 recites the limitation “a user" twice, in lines 22 and 36, and “the user” in various other lines. There is insufficient antecedent basis for this limitation in the claim.
Claims 11 and 20 each contain antecedent basis issues comparable to those described above regarding claim 1.
Claims 2-10 and 12-19 each depend on claim 1 or 11 either directly or indirectly. As such, claims 2-10 and 12-19 each incorporate the antecedent basis issues of claims 1 and 11, and do not remedy them. As such, claims 2-10 and 12-19 are rejected for the same reasons as stated above regarding claims 1 and 11.
Claim 3 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential steps, such omission amounting to a gap between the steps. See MPEP § 2172.01. The method of calculating the specificity score is unclear, as the calculation depends on calculating the breadth score, which in turn depends on calculating the entropy score, which in turn appears to depend on calculating the specificity score.
Claim rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential steps, such omission amounting to a gap between the steps. See MPEP § 2172.01. The method of calculating the specificity score is unclear, as the calculation depends on calculating the breadth score, which in turn depends on calculating the entropy score, which in turn appears to depend on calculating the specificity score.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefore, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1, the claim recites elements (a) “storing, by “identifying an item grouping of a plurality of item groupings associated with the search query”, (h) “generating a prompt for the specification, the elements encompass mental processes and organizing human activity. Accordingly, the claim recites an abstract idea (Step 2A, Prong one).
The judicial exception is not integrated into a practical application. The claim recites additional elements (w) “an online system”, (x) “a model serving system”, and (y) “a client device”. Here, elements (w)-(y) account for generic computing components recited at a high level of generality (MPEP 2106.04(a)(2)(III)(C)). Even when viewed in combination, the claim elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception (Step 2A: YES).
The claim does not include any other additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, elements (a)-(v) amount to no more than a mental process and organizing human activity, and elements (w)-(y) amount to no more than generic computing components. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, and do not provide an inventive concept (step 2B).
Claim 2 depends on claim 1, and thus recites the limitations of claim 1, with the additional element (z) “wherein computing the query breadth score for a search query comprises: computing an entropy score, wherein the entropy score is an inverse measure of a specificity of the search query.”
For the reasons discussed above for claim 1, the claim 1 limitations recite abstract ideas. The additional element of claim 2 does not preclude the steps of claim 1 from being practically performed in the human mind. Element (z) further modifies the abstract idea by disclosing computing a breadth score. Here, element (z) falls under the mental process of collecting data, evaluating it, and outputting the results of the evaluation (MPEP 2106.04(a)(2)(III)(A)).
Claim 3 depends on claim 2, and thus recites the limitations of claim 2, with the additional element (aa) “wherein computing the query breadth score for a search query comprises: computing an entropy score, wherein the entropy score is an inverse measure of a specificity of the search query.”
For the reasons discussed above for claim 2, the claim 2 limitations recite abstract ideas. The additional element of claim 3 does not preclude the steps of claim 2 from being practically performed in the human mind. Element (aa) further modifies the abstract idea by disclosing computing an entropy score. Here, element (aa) falls under the mental process of collecting data, evaluating it, and outputting the results of the evaluation (MPEP 2106.04(a)(2)(III)(A)).
Claim 4 depends on claim 1, and thus recites the limitations of claim 1, with the additional element (ab) “wherein computing the query specificity score for a search query comprises: computing a frequency score based on the search query, wherein the frequency score represents a number of times that the search query is received over a time period.”
For the reasons discussed above for claim 1, the claim 1 limitations recite abstract ideas. The additional element of claim 4 does not preclude the steps of claim 1 from being practically performed in the human mind. Element (ab) further modifies the abstract idea by disclosing computing a frequency score. Here, element (ab) falls under the mental process of collecting data, evaluating it, and outputting the results of the evaluation (MPEP 2106.04(a)(2)(III)(A)).
Claim 5 depends on claim 4, and thus recites the limitations of claim 4, with the additional element (ac) “wherein computing the frequency score for a search query comprises: computing a percentage of a total number of queries received over the time period were the search query.”
For the reasons discussed above for claim 4, the claim 4 limitations recite abstract ideas. The additional element of claim 5 does not preclude the steps of claim 4 from being practically performed in the human mind. Element (ac) further modifies the abstract idea by disclosing computing a percentage of queries in a time period. Here, element (ac) falls under the mental process of collecting data, evaluating it, and outputting the results of the evaluation (MPEP 2106.04(a)(2)(III)(A)).
Claim 6 depends on claim 1, and thus recites the limitations of claim 1, with the additional element (ad) “wherein computing the query specificity score comprises computing a weighted combination of a set of sub-scores.”
For the reasons discussed above for claim 1, the claim 1 limitations recite abstract ideas. The additional element of claim 6 does not preclude the steps of claim 1 from being practically performed in the human mind. Element (ad) further modifies the abstract idea by disclosing computing a weighted combination of sub-scores. Here, element (ad) falls under the mental process of collecting data, evaluating it, and outputting the results of the evaluation (MPEP 2106.04(a)(2)(III)(A)).
Claim 7 depends on claim 1, and thus recites the limitations of claim 1, with the additional element (ae) “wherein the user data describing the user associated with a search query in the first subset or second subset of search queries comprises user interaction data describing an interaction of the user with an item of the online system, user location data describing a location of the user, or user order information describing an order associated with the user.”
For the reasons discussed above for claim 1, the claim 1 limitations recite abstract ideas. The additional element of claim 7 does not preclude the steps of claim 1 from being practically performed in the human mind. Element (ae) further modifies the abstract idea by disclosing various user data that can be collected. Here, element (ae) falls under the mental process of collecting data, evaluating it, and outputting the results of the evaluation (MPEP 2106.04(a)(2)(III)(A)).
Claim 8 depends on claim 1, and thus recites the limitations of claim 1, with the additional element (af) “wherein each item grouping in the stored plurality of item groupings comprises an indication of a theme associated with the item grouping.”
For the reasons discussed above for claim 1, the claim 1 limitations recite abstract ideas. The additional element of claim 8 does not preclude the steps of claim 1 from being practically performed in the human mind. Element (af) further modifies the abstract idea by disclosing the indication of a theme for each item grouping. Here, element (af) falls under the mental process of collecting data, evaluating it, and outputting the results of the evaluation (MPEP 2106.04(a)(2)(III)(A)).
Claim 9 depends on claim 8, and thus recites the limitations of claim 8, with the additional element (ag) “wherein the instructions to generate the dynamic description of an item grouping for a search query in the first subset of search queries comprise: instructions to generate the description based on the theme associated with the item grouping.”
For the reasons discussed above for claim 8, the claim 8 limitations recite abstract ideas. The additional element of claim 9 does not preclude the steps of claim 8 from being practically performed in the human mind. Element (ag) further modifies the abstract idea by disclosing using the theme to generate the description. Here, element (ag) falls under the mental process of collecting data, evaluating it, and outputting the results of the evaluation (MPEP 2106.04(a)(2)(III)(A)).
Claim 10 depends on claim 1, and thus recites the limitations of claim 1, with the additional element (ah) “wherein the instructions to generate a dynamic description of an item grouping for a search query in the first subset or second subset of search queries comprises instructions to generate a title or paragraph describing the item grouping.”
For the reasons discussed above for claim 1, the claim 1 limitations recite abstract ideas. The additional element of claim 10 does not preclude the steps of claim 1 from being practically performed in the human mind. Element (ah) further modifies the abstract idea by disclosing generating a dynamic description that is a title or paragraph. Here, element (ah) falls under the mental process of collecting data, evaluating it, and outputting the results of the evaluation (MPEP 2106.04(a)(2)(III)(A)).
Claims 2-10 do not recite any additional elements and therefore, the claims are not practically integrated into a practical application and do not amount to significantly more than a judicial exception (Step 2A Prong two and Step 2B).
Regarding claim 11, the claim recites elements (a) “storing, by human mind]”, (m) “receiving a response from the model serving system, wherein the response comprises the dynamic description for the item grouping”, (n) “transmitting the identified item grouping and the dynamic description of the item grouping to
The judicial exception is not integrated into a practical application. The claim recites additional elements (w) “a non-transitory computer-readable medium storing instructions that, when executed by a computer system, cause the computer system to perform operations”, (x) “an online system”, (y) “a model serving system”, and (z) “a client device”. Here, elements (w)-(z) account for generic computing components recited at a high level of generality (MPEP 2106.04(a)(2)(III)(C)). Even when viewed in combination, the claim elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception (Step 2A: YES).
The claim does not include any other additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, elements (a)-(v) amount to no more than a mental process and organizing human activity, and elements (w)-(z) amount to no more than generic computing components. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, and do not provide an inventive concept (step 2B).
Claim 12 depends on claim 11, and thus recites the limitations of claim 11, with the additional element (aa) “wherein computing the query breadth score for a search query comprises: computing an entropy score, wherein the entropy score is an inverse measure of a specificity of the search query.”
For the reasons discussed above for claim 11, the claim 11 limitations recite abstract ideas. The additional element of claim 12 does not preclude the steps of claim 11 from being practically performed in the human mind. Element (aa) further modifies the abstract idea by disclosing computing a breadth score. Here, element (aa) falls under the mental process of collecting data, evaluating it, and outputting the results of the evaluation (MPEP 2106.04(a)(2)(III)(A)).
Claim 13 depends on claim 12, and thus recites the limitations of claim 12, with the additional element (ab) “wherein computing the query breadth score for a search query comprises: computing an entropy score, wherein the entropy score is an inverse measure of a specificity of the search query.”
For the reasons discussed above for claim 12, the claim 12 limitations recite abstract ideas. The additional element of claim 13 does not preclude the steps of claim 12 from being practically performed in the human mind. Element (ab) further modifies the abstract idea by disclosing computing an entropy score. Here, element (ab) falls under the mental process of collecting data, evaluating it, and outputting the results of the evaluation (MPEP 2106.04(a)(2)(III)(A)).
Claim 14 depends on claim 11, and thus recites the limitations of claim 11, with the additional element (ac) “wherein computing the query specificity score for a search query comprises: computing a frequency score based on the search query, wherein the frequency score represents a number of times that the search query is received over a time period.”
For the reasons discussed above for claim 11, the claim 11 limitations recite abstract ideas. The additional element of claim 14 does not preclude the steps of claim 11 from being practically performed in the human mind. Element (ac) further modifies the abstract idea by disclosing computing a frequency score. Here, element (ac) falls under the mental process of collecting data, evaluating it, and outputting the results of the evaluation (MPEP 2106.04(a)(2)(III)(A)).
Claim 15 depends on claim 14, and thus recites the limitations of claim 14, with the additional element (ad) “wherein computing the frequency score for a search query comprises: computing a percentage of a total number of queries received over the time period were the search query.”
For the reasons discussed above for claim 14, the claim 14 limitations recite abstract ideas. The additional element of claim 15 does not preclude the steps of claim 14 from being practically performed in the human mind. Element (ad) further modifies the abstract idea by disclosing computing a percentage of queries in a time period. Here, element (ad) falls under the mental process of collecting data, evaluating it, and outputting the results of the evaluation (MPEP 2106.04(a)(2)(III)(A)).
Claim 16 depends on claim 11, and thus recites the limitations of claim 11, with the additional element (ae) “wherein computing the query specificity score comprises computing a weighted combination of a set of sub-scores.”
For the reasons discussed above for claim 11, the claim 11 limitations recite abstract ideas. The additional element of claim 16 does not preclude the steps of claim 11 from being practically performed in the human mind. Element (ae) further modifies the abstract idea by disclosing computing a weighted combination of sub-scores. Here, element (ae) falls under the mental process of collecting data, evaluating it, and outputting the results of the evaluation (MPEP 2106.04(a)(2)(III)(A)).
Claim 17 depends on claim 11, and thus recites the limitations of claim 11, with the additional element (af) “wherein the user data describing the user associated with a search query in the first subset or second subset of search queries comprises user interaction data describing an interaction of the user with an item of the online system, user location data describing a location of the user, or user order information describing an order associated with the user.”
For the reasons discussed above for claim 11, the claim 11 limitations recite abstract ideas. The additional element of claim 17 does not preclude the steps of claim 11 from being practically performed in the human mind. Element (af) further modifies the abstract idea by disclosing various user data that can be collected. Here, element (af) falls under the mental process of collecting data, evaluating it, and outputting the results of the evaluation (MPEP 2106.04(a)(2)(III)(A)).
Claim 18 depends on claim 11, and thus recites the limitations of claim 11, with the additional element (ag) “wherein each item grouping in the stored plurality of item groupings comprises an indication of a theme associated with the item grouping.”
For the reasons discussed above for claim 11, the claim 11 limitations recite abstract ideas. The additional element of claim 18 does not preclude the steps of claim 11 from being practically performed in the human mind. Element (ag) further modifies the abstract idea by disclosing the indication of a theme for each item grouping. Here, element (ag) falls under the mental process of collecting data, evaluating it, and outputting the results of the evaluation (MPEP 2106.04(a)(2)(III)(A)).
Claim 19 depends on claim 18, and thus recites the limitations of claim 18, with the additional element (ah) “wherein the instructions to generate the dynamic description of an item grouping for a search query in the first subset of search queries comprise: instructions to generate the description based on the theme associated with the item grouping.”
For the reasons discussed above for claim 18, the claim 18 limitations recite abstract ideas. The additional element of claim 19 does not preclude the steps of claim 18 from being practically performed in the human mind. Element (ah) further modifies the abstract idea by disclosing using the theme to generate the description. Here, element (ah) falls under the mental process of collecting data, evaluating it, and outputting the results of the evaluation (MPEP 2106.04(a)(2)(III)(A)).
Claims 12-19 do not recite any additional elements and therefore, the claims are not practically integrated into a practical application and do not amount to significantly more than a judicial exception (Step 2A Prong two and Step 2B).
Regarding claim 20, the claim recites elements (a) “storing, by an item database”, (c) “computing a query specificity score for each search query of the plurality of search queries based on the corresponding free text of the corresponding search query”, (d) “determining, for a first subset of the plurality of search queries, that the corresponding query specificity scores are above a threshold value”, (e) “determining, for a second subset of the plurality of search queries, that the corresponding query specificity scores are below a threshold value”, (f) “presenting, for each search query in the first subset of search queries, a dynamic description for an item grouping to a corresponding user, wherein presenting the dynamic description comprises:”, (g) “identifying an item grouping of a plurality of item groupings associated with the search query”, (h) “generating a prompt for
The judicial exception is not integrated into a practical application. The claim recites additional elements (w) “a processor”, (x) “a non-transitory computer-readable medium storing instructions that, when executed by a computer system, cause the computer system to perform operations”, (y) “an online system”, (z) “a model serving system”, and (aa) “a client device”. Here, elements (w)-(aa) account for generic computing components recited at a high level of generality (MPEP 2106.04(a)(2)(III)(C)). Even when viewed in combination, the claim elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception (Step 2A: YES).
The claim does not include any other additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, elements (a)-(v) amount to no more than a mental process and organizing human activity, and elements (w)-(aa) amount to no more than generic computing components. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, and do not provide an inventive concept (step 2B).
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, 2, 7-12, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lightbody et al. (US Pat. No. 11,386,456 B1 hereinafter Lightbody), in view of Guriel et al. (US Pat. No. 12,259,921 B1 hereinafter Guriel).
Regarding claim 1, Lightbody discloses a computer-implemented method, comprising: storing, by an online system, a plurality of item groupings, wherein each item grouping comprises a set of items and wherein each item grouping is associated with a set of search queries (Lightbody, Col. 8, lines 13-22: "as shown in FIGS. 3 and 4, different categories of results are shown for the different stages. For example, in the illustrated embodiments, while in an earlier stage, categories such as top picks from a review site and/or from the online merchant, customer recommended products, and customer considerations are presented. Conversely, while in a later stage, categories such as “Customers ultimately bought,” “Related to items you've viewed,” and “Frequently bought together” are presented."); receiving a plurality of search queries from users of client devices, wherein each search query comprises free text describing items to retrieve from an item database (Lightbody, Col. 4, lines 33-39: "The search query engine 206 accepts search queries from a user via an application running on a user interface 210 and processes the search queries to generate data (e.g., a current query) useable by the machine learning model 202 to identify a stage of a shopping mission (e.g., from the stages shown at 204) in which the user is positioned within the shopping mission."); computing a query specificity score for each search query of the plurality of search queries based on the corresponding free text of the corresponding search query (Lightbody, Col. 7, lines 10-16: "As shown, the results for a query that is made while a user is in an early stage of an online shopping mission (e.g., where there is little or no historical data for the user relating to the mission and/or where the user has not provided a targeted search query with a specificity that is above at least one threshold) include generalized information for products relating to the search query."); determining, for a first subset of the plurality of search queries, that the corresponding query specificity scores are above a threshold value (Lightbody, Col. 9, lines 7-12: "Alternatively, a search query that includes more targeted parameters (e.g., more parameters than a threshold or otherwise a query having more specificity than a threshold) is indicative of a user being in a later stage of a shopping mission (e.g., closer to an end than a beginning of the shopping mission)."); and determining, for a second subset of the plurality of search queries, that the corresponding query specificity scores are below a threshold value (Lightbody, Col. 7, lines 10-16: "As shown, the results for a query that is made while a user is in an early stage of an online shopping mission (e.g., where there is little or no historical data for the user relating to the mission and/or where the user has not provided a targeted search query with a specificity that is above at least one threshold) include generalized information for products relating to the search query."). However, Lightbody fails to expressly recite presenting, for each search query in the first subset of search queries, a dynamic description for an item grouping to a corresponding user, wherein presenting the dynamic description comprises: identifying an item grouping of a plurality of item groupings associated with the search query; generating a prompt for a model serving system, wherein the prompt comprises: user data describing a user associated with the search query; the identified item grouping; and instructions to generate a dynamic description for the item grouping based on the user data and the item grouping; transmitting the prompt to the model serving system; receiving a response from the model serving system, wherein the response comprises the dynamic description for the item grouping; and transmitting the identified item grouping and the dynamic description of the item grouping to a client device of the user for presentation to the user; and presenting, for each search query in the second subset of search queries, a dynamic item grouping by: generating a prompt for a model serving system, wherein the prompt comprises: user data describing a user associated with the search query; item data describing a plurality of candidate items; instructions to generate an item grouping to display to the user based on the user data and the item data; and instructions to generate a description for the item grouping based on the user data and the item data; transmitting the prompt to the model serving system; receiving a response from the model serving system, wherein the response comprises the generated item grouping and the generated description for the item grouping; and transmitting the item grouping and the description of the item grouping to a client device of the user for presentation to the user.
Guriel teaches presenting, for each search query in the first subset of search queries, a dynamic description for an item grouping to a corresponding user, wherein presenting the dynamic description comprises: identifying an item grouping of a plurality of item groupings associated with the search query; generating a prompt for a model serving system (Guriel, Col. 15, lines 31-41: " the description manager 118 may generate an input (e.g., a second input) or prompt for the LLM to generate the cluster title 148 and cluster description 150. The prompt may include the associated titles of the interactive multimedia content items 124 of the cluster 142, as well as the common tags 146 associated with each cluster 142. As discussed above, the prompt may be generated for any manner of input to the LLM, and may indicate one or more parameters for directing the LLM to generate the cluster title 148 and cluster description 150 output."), wherein the prompt comprises: user data describing a user associated with the search query (Guriel, Col. 11, lines 61-65: "the embedding engine 114 receives usage data 136 for the collection of interactive multimedia content items 124. As discussed herein, the usage data 136 may indicate the usage and behavior of users with respect to the interactive multimedia content items 124."); the identified item grouping (Guriel, Col. 12, lines 48-57: "The embedding engine 114 may generate embeddings 138 in this way for each of the interactive multimedia content items 124 in order that the interactive multimedia content items 124 may be compared and similarities between the interactive multimedia content items 124 may be learned. This may facilitate identifying clusters of interactive multimedia content items 124 that are similar in one or more aspects, as described herein. The embedding engine 114 may store the embeddings 138 to the data storage 122 as cluster data 130."); and instructions to generate a dynamic description for the item grouping based on the user data and the item grouping; transmitting the prompt to the model serving system; receiving a response from the model serving system, wherein the response comprises the dynamic description for the item grouping (Guriel, Col. 15, lines 31-41: " the description manager 118 may generate an input (e.g., a second input) or prompt for the LLM to generate the cluster title 148 and cluster description 150. The prompt may include the associated titles of the interactive multimedia content items 124 of the cluster 142, as well as the common tags 146 associated with each cluster 142. As discussed above, the prompt may be generated for any manner of input to the LLM, and may indicate one or more parameters for directing the LLM to generate the cluster title 148 and cluster description 150 output."); and transmitting the identified item grouping and the dynamic description of the item grouping to a client device of the user for presentation to the user (Guriel, Col. 15 line 64- Col. 16 line 5: "As mentioned above, the content cluster system 110 includes a taste cluster manager 120. FIG. 7 illustrates example features and functionalities of the taste cluster manager 120 as described herein, according to at least one embodiment of the present disclosure. The taste cluster manager 120 may identify one or more preferences of a user for interactive multimedia content items 124 and may facilitate identifying one or more of the clusters 142 that may appeal to the user's preferences."; Col. 17, lines 21-23: "In some embodiments, the taste cluster manager 120 may generate a report of the taste clusters 152 and may present the report via a graphic user interface of a user device."); and presenting, for each search query in the second subset of search queries, a dynamic item grouping by: generating a prompt for a model serving system, wherein the prompt comprises: user data describing a user associated with the search query (Guriel, Col. 11, lines 61-65: "the embedding engine 114 receives usage data 136 for the collection of interactive multimedia content items 124. As discussed herein, the usage data 136 may indicate the usage and behavior of users with respect to the interactive multimedia content items 124."); item data describing a plurality of candidate items; instructions to generate an item grouping to display to the user based on the user data and the item data; and instructions to generate a description for the item grouping based on the user data and the item data; transmitting the prompt to the model serving system; receiving a response from the model serving system, wherein the response comprises the generated item grouping and the generated description for the item grouping (Guriel, Col. 15, lines 31-41: " the description manager 118 may generate an input (e.g., a second input) or prompt for the LLM to generate the cluster title 148 and cluster description 150. The prompt may include the associated titles of the interactive multimedia content items 124 of the cluster 142, as well as the common tags 146 associated with each cluster 142. As discussed above, the prompt may be generated for any manner of input to the LLM, and may indicate one or more parameters for directing the LLM to generate the cluster title 148 and cluster description 150 output."); and transmitting the item grouping and the description of the item grouping to a client device of the user for presentation to the user (Guriel, Col. 15 line 64- Col. 16 line 5: "As mentioned above, the content cluster system 110 includes a taste cluster manager 120. FIG. 7 illustrates example features and functionalities of the taste cluster manager 120 as described herein, according to at least one embodiment of the present disclosure. The taste cluster manager 120 may identify one or more preferences of a user for interactive multimedia content items 124 and may facilitate identifying one or more of the clusters 142 that may appeal to the user's preferences."; Col. 17, lines 21-23: "In some embodiments, the taste cluster manager 120 may generate a report of the taste clusters 152 and may present the report via a graphic user interface of a user device.").
Lightbody and Guriel are analogous arts because they each belong to the same field of content searching systems. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the shopping system of Lightbody to incorporate the teachings of Guriel to generate item groupings, titles, and descriptions based on various factors. This helps accommodate a large catalogue of items for any number of users (Guriel, Col. 4, paragraph 3). As such, the system is able to describe and relate many items for many users, thus improving the user experience.
Regarding claim 2, the rejection of claim 1 is incorporated. Lightbody, in view of Guriel, discloses all of the elements of the current invention as stated above. Lightbody further discloses wherein computing the query specific score for a search query comprises: computing a query breadth score based on the search query that represents a breadth of the free text in the search query (Lightbody, Col. 7, lines 10-16: "As shown, the results for a query that is made while a user is in an early stage of an online shopping mission (e.g., where there is little or no historical data for the user relating to the mission and/or where the user has not provided a targeted search query with a specificity that is above at least one threshold) include generalized information for products relating to the search query."; Col. 9, lines 7-12: "Alternatively, a search query that includes more targeted parameters (e.g., more parameters than a threshold or otherwise a query having more specificity than a threshold) is indicative of a user being in a later stage of a shopping mission (e.g., closer to an end than a beginning of the shopping mission)."; Col. 4, lines 11-15: "Example modeled estimations of intent-based stages of a customer in the shopping mission are shown at 204, including six stages ordered by breadth of intent and ranging from F5—e.g., “I'm looking for inspiration,” to F0—e.g., “I know the product I want and I just need to find it.”"; Here, the specificity is seen as being based on the breadth of the query.).
Regarding claim 7, the rejection of claim 1 is incorporated. Lightbody, in view of Guriel, discloses all of the elements of the current invention as stated above. Lightbody further discloses wherein the user data describing the user associated with a search query in the first subset or second subset of search queries comprises user interaction data describing an interaction of the user with an item of the online system, user location data describing a location of the user, or user order information describing an order associated with the user (Lightbody, Col. 4, lines 40-48: "The model 202 also accepts as inputs, in some examples, historical mission data from the historical database 208. The historical mission data may include a listing of timestamped events, as described in more detail below, indicating interactions of the user with the user interface and/or other events relevant to the shopping mission, which include search queries, past purchases, and other user interactions (e.g., user selections/views of products and other information, etc.) provided via the user interface 210 as shown in FIG. 2.").
Regarding claim 8, the rejection of claim 1 is incorporated. Lightbody, in view of Guriel, discloses all of the elements of the current invention as stated above. Guriel further teaches wherein each item grouping in the stored plurality of item groupings comprises an indication of a theme associated with the item grouping (Guriel, Col. 3, lines 27-34: "the present disclosure describes a content cluster system that can analyze, characterize, and describe a catalogue of interactive multimedia content items. The content cluster system can determine, at a detailed and nuanced level, a number of qualities, attributes, themes, meanings, and other aspects of each multimedia content items and can represent these attributes based on assigning semantic attribute tags to the multimedia content items."). The same motivation for claim 1 applies equally to claim 8.
Regarding claim 9, the rejection of claim 8 is incorporated. Lightbody, in view of Guriel, discloses all of the elements of the current invention as stated above. Guriel further teaches wherein the instructions to generate the dynamic description of an item grouping for a search query in the first subset of search queries comprise: instructions to generate the description based on the theme associated with the item grouping (Guriel, Col. 14, lines 58-65: "The description manager 118 may generate the cluster title 148 and cluster description 150 based on the attribute tags 132, identified in the common tags 146 for each cluster 142. For example, the description manager 118 may identify, from the common tags 146, one or more aspects, themes, qualities, attributes, etc., that describe the interactive multimedia content items 124 in the cluster 142 generally."). The same motivation for claim 1 applies equally to claim 9.
Regarding claim 10, the rejection of claim 1 is incorporated. Lightbody, in view of Guriel, discloses all of the elements of the current invention as stated above. Guriel further teaches wherein the instructions to generate a dynamic description of an item grouping for a search query in the first subset or second subset of search queries comprises instructions to generate a title or paragraph describing the item grouping (Guriel, Col. 15, lines 31-41: " the description manager 118 may generate an input (e.g., a second input) or prompt for the LLM to generate the cluster title 148 and cluster description 150. The prompt may include the associated titles of the interactive multimedia content items 124 of the cluster 142, as well as the common tags 146 associated with each cluster 142. As discussed above, the prompt may be generated for any manner of input to the LLM, and may indicate one or more parameters for directing the LLM to generate the cluster title 148 and cluster description 150 output."). The same motivation for claim 1 applies equally to claim 10.
Regarding claim 11, Lightbody discloses a non-transitory computer-readable medium storing instructions that, when executed by a computer system, cause the computer system to perform operations comprising (Lightbody, Col. 19, lines 47-53: “The tangible storage 640 may be removable or non-removable, and includes magnetic disks, magnetic tapes or cassettes, CD-ROMs, DVDs, or any other medium which can be used to store information in a non-transitory way and which can be accessed within the computing environment 600. The storage 640 stores instructions for the software 680 implementing one or more innovations described herein.”): storing, by an online system, a plurality of item groupings, wherein each item grouping comprises a set of items and wherein each item grouping is associated with a set of search queries (Lightbody, Col. 8, lines 13-22: "as shown in FIGS. 3 and 4, different categories of results are shown for the different stages. For example, in the illustrated embodiments, while in an earlier stage, categories such as top picks from a review site and/or from the online merchant, customer recommended products, and customer considerations are presented. Conversely, while in a later stage, categories such as “Customers ultimately bought,” “Related to items you've viewed,” and “Frequently bought together” are presented."); receiving a plurality of search queries from users of client devices, wherein each search query comprises free text describing items to retrieve from an item database (Lightbody, Col. 4, lines 33-39: "The search query engine 206 accepts search queries from a user via an application running on a user interface 210 and processes the search queries to generate data (e.g., a current query) useable by the machine learning model 202 to identify a stage of a shopping mission (e.g., from the stages shown at 204) in which the user is positioned within the shopping mission."); computing a query specificity score for each search query of the plurality of search queries based on the corresponding free text of the corresponding search query (Lightbody, Col. 7, lines 10-16: "As shown, the results for a query that is made while a user is in an early stage of an online shopping mission (e.g., where there is little or no historical data for the user relating to the mission and/or where the user has not provided a targeted search query with a specificity that is above at least one threshold) include generalized information for products relating to the search query."); determining, for a first subset of the plurality of search queries, that the corresponding query specificity scores are above a threshold value (Lightbody, Col. 9, lines 7-12: "Alternatively, a search query that includes more targeted parameters (e.g., more parameters than a threshold or otherwise a query having more specificity than a threshold) is indicative of a user being in a later stage of a shopping mission (e.g., closer to an end than a beginning of the shopping mission)."); and determining, for a second subset of the plurality of search queries, that the corresponding query specificity scores are below the threshold value (Lightbody, Col. 7, lines 10-16: "As shown, the results for a query that is made while a user is in an early stage of an online shopping mission (e.g., where there is little or no historical data for the user relating to the mission and/or where the user has not provided a targeted search query with a specificity that is above at least one threshold) include generalized information for products relating to the search query."). However, Lightbody fails to expressly recite presenting, for each search query in the first subset of search queries, a dynamic description for an item grouping to a corresponding user, wherein presenting the dynamic description comprises: identifying an item grouping of the plurality of item groupings associated with the search query; generating a prompt for a model serving system, wherein the prompt comprises: user data describing a user associated with the search query; the identified item grouping; and instructions to generate a dynamic description for the item grouping based on the user data and the item grouping; transmitting the prompt to the model serving system; receiving a response from the model serving system, wherein the response comprises the dynamic description for the item grouping; and transmitting the identified item grouping and the dynamic description of the item grouping to a client device of the user for presentation to the user; and presenting, for each search query in the second subset of search queries, a dynamic item grouping by: generating a prompt for the model serving system, wherein the prompt comprises: user data describing a user associated with the search query; item data describing a plurality of candidate items; instructions to generate an item grouping to display to the user based on the user data and the item data; and instructions to generate a description for the item grouping based on the user data and the item data; transmitting the prompt to the model serving system; receiving a response from the model serving system, wherein the response comprises the generated item grouping and the generated description for the item grouping; and transmitting the item grouping and the description of the item grouping to a client device of the user for presentation to the user.
Guriel teaches presenting, for each search query in the first subset of search queries, a dynamic description for an item grouping to a corresponding user, wherein presenting the dynamic description comprises: identifying an item grouping of the plurality of item groupings associated with the search query; generating a prompt for a model serving system (Guriel, Col. 15, lines 31-41: " the description manager 118 may generate an input (e.g., a second input) or prompt for the LLM to generate the cluster title 148 and cluster description 150. The prompt may include the associated titles of the interactive multimedia content items 124 of the cluster 142, as well as the common tags 146 associated with each cluster 142. As discussed above, the prompt may be generated for any manner of input to the LLM, and may indicate one or more parameters for directing the LLM to generate the cluster title 148 and cluster description 150 output."), wherein the prompt comprises: user data describing a user associated with the search query (Guriel, Col. 11, lines 61-65: "the embedding engine 114 receives usage data 136 for the collection of interactive multimedia content items 124. As discussed herein, the usage data 136 may indicate the usage and behavior of users with respect to the interactive multimedia content items 124."); the identified item grouping (Guriel, Col. 12, lines 48-57: "The embedding engine 114 may generate embeddings 138 in this way for each of the interactive multimedia content items 124 in order that the interactive multimedia content items 124 may be compared and similarities between the interactive multimedia content items 124 may be learned. This may facilitate identifying clusters of interactive multimedia content items 124 that are similar in one or more aspects, as described herein. The embedding engine 114 may store the embeddings 138 to the data storage 122 as cluster data 130."); and instructions to generate a dynamic description for the item grouping based on the user data and the item grouping; transmitting the prompt to the model serving system; receiving a response from the model serving system, wherein the response comprises the dynamic description for the item grouping (Guriel, Col. 15, lines 31-41: " the description manager 118 may generate an input (e.g., a second input) or prompt for the LLM to generate the cluster title 148 and cluster description 150. The prompt may include the associated titles of the interactive multimedia content items 124 of the cluster 142, as well as the common tags 146 associated with each cluster 142. As discussed above, the prompt may be generated for any manner of input to the LLM, and may indicate one or more parameters for directing the LLM to generate the cluster title 148 and cluster description 150 output."); and transmitting the identified item grouping and the dynamic description of the item grouping to a client device of the user for presentation to the user (Guriel, Col. 15 line 64- Col. 16 line 5: "As mentioned above, the content cluster system 110 includes a taste cluster manager 120. FIG. 7 illustrates example features and functionalities of the taste cluster manager 120 as described herein, according to at least one embodiment of the present disclosure. The taste cluster manager 120 may identify one or more preferences of a user for interactive multimedia content items 124 and may facilitate identifying one or more of the clusters 142 that may appeal to the user's preferences."; Col. 17, lines 21-23: "In some embodiments, the taste cluster manager 120 may generate a report of the taste clusters 152 and may present the report via a graphic user interface of a user device."); and presenting, for each search query in the second subset of search queries, a dynamic item grouping by: generating a prompt for the model serving system, wherein the prompt comprises: user data describing a user associated with the search query (Guriel, Col. 11, lines 61-65: "the embedding engine 114 receives usage data 136 for the collection of interactive multimedia content items 124. As discussed herein, the usage data 136 may indicate the usage and behavior of users with respect to the interactive multimedia content items 124."); item data describing a plurality of candidate items; instructions to generate an item grouping to display to the user based on the user data and the item data; and instructions to generate a description for the item grouping based on the user data and the item data; transmitting the prompt to the model serving system; receiving a response from the model serving system, wherein the response comprises the generated item grouping and the generated description for the item grouping (Guriel, Col. 15, lines 31-41: " the description manager 118 may generate an input (e.g., a second input) or prompt for the LLM to generate the cluster title 148 and cluster description 150. The prompt may include the associated titles of the interactive multimedia content items 124 of the cluster 142, as well as the common tags 146 associated with each cluster 142. As discussed above, the prompt may be generated for any manner of input to the LLM, and may indicate one or more parameters for directing the LLM to generate the cluster title 148 and cluster description 150 output."); and transmitting the item grouping and the description of the item grouping to a client device of the user for presentation to the user (Guriel, Col. 15 line 64- Col. 16 line 5: "As mentioned above, the content cluster system 110 includes a taste cluster manager 120. FIG. 7 illustrates example features and functionalities of the taste cluster manager 120 as described herein, according to at least one embodiment of the present disclosure. The taste cluster manager 120 may identify one or more preferences of a user for interactive multimedia content items 124 and may facilitate identifying one or more of the clusters 142 that may appeal to the user's preferences."; Col. 17, lines 21-23: "In some embodiments, the taste cluster manager 120 may generate a report of the taste clusters 152 and may present the report via a graphic user interface of a user device.").
Lightbody and Guriel are analogous arts because they each belong to the same field of content searching systems. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the shopping system of Lightbody to incorporate the teachings of Guriel to generate item groupings, titles, and descriptions based on various factors. This helps accommodate a large catalogue of items for any number of users (Guriel, Col. 4, paragraph 3). As such, the system is able to describe and relate many items for many users, thus improving the user experience.
Regarding claim 12, the rejection of claim 11 is incorporated. Lightbody, in view of Guriel, discloses all of the elements of the current invention as stated above. Lightbody further discloses wherein computing the query specific score for a search query comprises: computing a query breadth score based on the search query that represents a breadth of the free text in the search query (Lightbody, Col. 7, lines 10-16: "As shown, the results for a query that is made while a user is in an early stage of an online shopping mission (e.g., where there is little or no historical data for the user relating to the mission and/or where the user has not provided a targeted search query with a specificity that is above at least one threshold) include generalized information for products relating to the search query."; Col. 9, lines 7-12: "Alternatively, a search query that includes more targeted parameters (e.g., more parameters than a threshold or otherwise a query having more specificity than a threshold) is indicative of a user being in a later stage of a shopping mission (e.g., closer to an end than a beginning of the shopping mission)."; Col. 4, lines 11-15: "Example modeled estimations of intent-based stages of a customer in the shopping mission are shown at 204, including six stages ordered by breadth of intent and ranging from F5—e.g., “I'm looking for inspiration,” to F0—e.g., “I know the product I want and I just need to find it.”"; Here, the specificity is seen as being based on the breadth of the query.).
Regarding claim 17, the rejection of claim 11 is incorporated. Lightbody, in view of Guriel, discloses all of the elements of the current invention as stated above. Lightbody further discloses wherein the user data describing the user associated with a search query in the first subset or second subset of search queries comprises user interaction data describing an interaction of the user with an item of the online system, user location data describing a location of the user, or user order information describing an order associated with the user (Lightbody, Col. 4, lines 40-48: "The model 202 also accepts as inputs, in some examples, historical mission data from the historical database 208. The historical mission data may include a listing of timestamped events, as described in more detail below, indicating interactions of the user with the user interface and/or other events relevant to the shopping mission, which include search queries, past purchases, and other user interactions (e.g., user selections/views of products and other information, etc.) provided via the user interface 210 as shown in FIG. 2.").
Regarding claim 18, the rejection of claim 11 is incorporated. Lightbody, in view of Guriel, discloses all of the elements of the current invention as stated above. Guriel further teaches wherein each item grouping in the stored plurality of item groupings comprises an indication of a theme associated with the item grouping (Guriel, Col. 3, lines 27-34: "the present disclosure describes a content cluster system that can analyze, characterize, and describe a catalogue of interactive multimedia content items. The content cluster system can determine, at a detailed and nuanced level, a number of qualities, attributes, themes, meanings, and other aspects of each multimedia content items and can represent these attributes based on assigning semantic attribute tags to the multimedia content items."). The same motivation for claim 11 applies equally to claim 18.
Regarding claim 19, the rejection of claim 18 is incorporated. Lightbody, in view of Guriel, discloses all of the elements of the current invention as stated above. Guriel further teaches wherein the instructions to generate the dynamic description of an item grouping for a search query in the first subset of search queries comprise: instructions to generate the description based on the theme associated with the item grouping (Guriel, Col. 14, lines 58-65: "The description manager 118 may generate the cluster title 148 and cluster description 150 based on the attribute tags 132, identified in the common tags 146 for each cluster 142. For example, the description manager 118 may identify, from the common tags 146, one or more aspects, themes, qualities, attributes, etc., that describe the interactive multimedia content items 124 in the cluster 142 generally."). The same motivation for claim 11 applies equally to claim 19.
Regarding claim 20, Lightbody discloses a computer system comprising: a processor (Lightbody, Col. 19, lines 14-15: “the computing environment 600 includes one or more processing units 610, 615 and memory 620, 625.”); and non-transitory computer-readable medium storing instructions that, when executed by the computer system, cause the computer system to perform operations comprising (Lightbody, Col. 19, lines 47-53: “The tangible storage 640 may be removable or non-removable, and includes magnetic disks, magnetic tapes or cassettes, CD-ROMs, DVDs, or any other medium which can be used to store information in a non-transitory way and which can be accessed within the computing environment 600. The storage 640 stores instructions for the software 680 implementing one or more innovations described herein.”): storing, by an online system, a plurality of item groupings, wherein each item grouping comprises a set of items and wherein each item grouping is associated with a set of search queries (Lightbody, Col. 8, lines 13-22: "as shown in FIGS. 3 and 4, different categories of results are shown for the different stages. For example, in the illustrated embodiments, while in an earlier stage, categories such as top picks from a review site and/or from the online merchant, customer recommended products, and customer considerations are presented. Conversely, while in a later stage, categories such as “Customers ultimately bought,” “Related to items you've viewed,” and “Frequently bought together” are presented."); receiving a plurality of search queries from users of client devices, wherein each search query comprises free text describing items to retrieve from an item database (Lightbody, Col. 4, lines 33-39: "The search query engine 206 accepts search queries from a user via an application running on a user interface 210 and processes the search queries to generate data (e.g., a current query) useable by the machine learning model 202 to identify a stage of a shopping mission (e.g., from the stages shown at 204) in which the user is positioned within the shopping mission."); computing a query specificity score for each search query of the plurality of search queries based on the corresponding free text of the corresponding search query (Lightbody, Col. 7, lines 10-16: "As shown, the results for a query that is made while a user is in an early stage of an online shopping mission (e.g., where there is little or no historical data for the user relating to the mission and/or where the user has not provided a targeted search query with a specificity that is above at least one threshold) include generalized information for products relating to the search query."); determining, for a first subset of the plurality of search queries, that the corresponding query specificity scores are above a threshold value (Lightbody, Col. 9, lines 7-12: "Alternatively, a search query that includes more targeted parameters (e.g., more parameters than a threshold or otherwise a query having more specificity than a threshold) is indicative of a user being in a later stage of a shopping mission (e.g., closer to an end than a beginning of the shopping mission)."); and determining, for a second subset of the plurality of search queries, that the corresponding query specificity scores are below the threshold value (Lightbody, Col. 7, lines 10-16: "As shown, the results for a query that is made while a user is in an early stage of an online shopping mission (e.g., where there is little or no historical data for the user relating to the mission and/or where the user has not provided a targeted search query with a specificity that is above at least one threshold) include generalized information for products relating to the search query."). However, Lightbody fails to expressly recite presenting, for each search query in the first subset of search queries, a dynamic description for an item grouping to a corresponding user, wherein presenting the dynamic description comprises: identifying an item grouping of the plurality of item groupings associated with the search query; generating a prompt for a model serving system, wherein the prompt comprises: user data describing a user associated with the search query; the identified item grouping; and instructions to generate a dynamic description for the item grouping based on the user data and the item grouping; transmitting the prompt to the model serving system; receiving a response from the model serving system, wherein the response comprises the dynamic description for the item grouping; and transmitting the identified item grouping and the dynamic description of the item grouping to a client device of the user for presentation to the user; and presenting, for each search query in the second subset of search queries, a dynamic item grouping by: generating a prompt for the model serving system, wherein the prompt comprises: user data describing a user associated with the search query; item data describing a plurality of candidate items; instructions to generate an item grouping to display to the user based on the user data and the item data; and instructions to generate a description for the item grouping based on the user data and the item data; transmitting the prompt to the model serving system; receiving a response from the model serving system, wherein the response comprises the generated item grouping and the generated description for the item grouping; and transmitting the item grouping and the description of the item grouping to a client device of the user for presentation to the user.
Guriel teaches presenting, for each search query in the first subset of search queries, a dynamic description for an item grouping to a corresponding user, wherein presenting the dynamic description comprises: identifying an item grouping of the plurality of item groupings associated with the search query; generating a prompt for a model serving system (Guriel, Col. 15, lines 31-41: " the description manager 118 may generate an input (e.g., a second input) or prompt for the LLM to generate the cluster title 148 and cluster description 150. The prompt may include the associated titles of the interactive multimedia content items 124 of the cluster 142, as well as the common tags 146 associated with each cluster 142. As discussed above, the prompt may be generated for any manner of input to the LLM, and may indicate one or more parameters for directing the LLM to generate the cluster title 148 and cluster description 150 output."), wherein the prompt comprises: user data describing a user associated with the search query (Guriel, Col. 11, lines 61-65: "the embedding engine 114 receives usage data 136 for the collection of interactive multimedia content items 124. As discussed herein, the usage data 136 may indicate the usage and behavior of users with respect to the interactive multimedia content items 124."); the identified item grouping (Guriel, Col. 12, lines 48-57: "The embedding engine 114 may generate embeddings 138 in this way for each of the interactive multimedia content items 124 in order that the interactive multimedia content items 124 may be compared and similarities between the interactive multimedia content items 124 may be learned. This may facilitate identifying clusters of interactive multimedia content items 124 that are similar in one or more aspects, as described herein. The embedding engine 114 may store the embeddings 138 to the data storage 122 as cluster data 130."); and instructions to generate a dynamic description for the item grouping based on the user data and the item grouping; transmitting the prompt to the model serving system; receiving a response from the model serving system, wherein the response comprises the dynamic description for the item grouping (Guriel, Col. 15, lines 31-41: " the description manager 118 may generate an input (e.g., a second input) or prompt for the LLM to generate the cluster title 148 and cluster description 150. The prompt may include the associated titles of the interactive multimedia content items 124 of the cluster 142, as well as the common tags 146 associated with each cluster 142. As discussed above, the prompt may be generated for any manner of input to the LLM, and may indicate one or more parameters for directing the LLM to generate the cluster title 148 and cluster description 150 output."); and transmitting the identified item grouping and the dynamic description of the item grouping to a client device of the user for presentation to the user (Guriel, Col. 15 line 64- Col. 16 line 5: "As mentioned above, the content cluster system 110 includes a taste cluster manager 120. FIG. 7 illustrates example features and functionalities of the taste cluster manager 120 as described herein, according to at least one embodiment of the present disclosure. The taste cluster manager 120 may identify one or more preferences of a user for interactive multimedia content items 124 and may facilitate identifying one or more of the clusters 142 that may appeal to the user's preferences."; Col. 17, lines 21-23: "In some embodiments, the taste cluster manager 120 may generate a report of the taste clusters 152 and may present the report via a graphic user interface of a user device."); and presenting, for each search query in the second subset of search queries, a dynamic item grouping by: generating a prompt for the model serving system, wherein the prompt comprises: user data describing a user associated with the search query (Guriel, Col. 11, lines 61-65: "the embedding engine 114 receives usage data 136 for the collection of interactive multimedia content items 124. As discussed herein, the usage data 136 may indicate the usage and behavior of users with respect to the interactive multimedia content items 124."); item data describing a plurality of candidate items; instructions to generate an item grouping to display to the user based on the user data and the item data; and instructions to generate a description for the item grouping based on the user data and the item data; transmitting the prompt to the model serving system; receiving a response from the model serving system, wherein the response comprises the generated item grouping and the generated description for the item grouping (Guriel, Col. 15, lines 31-41: " the description manager 118 may generate an input (e.g., a second input) or prompt for the LLM to generate the cluster title 148 and cluster description 150. The prompt may include the associated titles of the interactive multimedia content items 124 of the cluster 142, as well as the common tags 146 associated with each cluster 142. As discussed above, the prompt may be generated for any manner of input to the LLM, and may indicate one or more parameters for directing the LLM to generate the cluster title 148 and cluster description 150 output."); and transmitting the item grouping and the description of the item grouping to a client device of the user for presentation to the user (Guriel, Col. 15 line 64- Col. 16 line 5: "As mentioned above, the content cluster system 110 includes a taste cluster manager 120. FIG. 7 illustrates example features and functionalities of the taste cluster manager 120 as described herein, according to at least one embodiment of the present disclosure. The taste cluster manager 120 may identify one or more preferences of a user for interactive multimedia content items 124 and may facilitate identifying one or more of the clusters 142 that may appeal to the user's preferences."; Col. 17, lines 21-23: "In some embodiments, the taste cluster manager 120 may generate a report of the taste clusters 152 and may present the report via a graphic user interface of a user device.").
Lightbody and Guriel are analogous arts because they each belong to the same field of content searching systems. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the shopping system of Lightbody to incorporate the teachings of Guriel to generate item groupings, titles, and descriptions based on various factors. This helps accommodate a large catalogue of items for any number of users (Guriel, Col. 4, paragraph 3). As such, the system is able to describe and relate many items for many users, thus improving the user experience.
Claim(s) 3 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lightbody, in view of Guriel, as applied to claims 1, 2, 7-12, and 17-20 above, and further in view of Di Fabbrizio et al. (US Pat. Pub. No. 2023/0106590 A1).
Regarding claim 3, the rejection of claim 2 is incorporated. Lightbody, in view of Guriel, discloses all of the elements of the current invention as stated above. However, Lightbody, in view of Guriel, fails to expressly recite wherein computing the query breadth score for a search query comprises: computing an entropy score, wherein the entropy score is an inverse measure of a specificity of the search query.
Di Fabbrizio teaches wherein computing the query breadth score for a search query comprises: computing an entropy score, wherein the entropy score is an inverse measure of a specificity of the search query (Di Fabbrizio, [0072]: "The specificity measure can measure the information needs of users for provide questions. For example, the specificity measure can be determined as the inverse entropy of a topic profile computed for a given term “t”. ").
Lightbody, Guriel, and Di Fabbrizio are analogous arts because they each belong to the same field of data processing systems. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the shopping system of Lightbody, as modified by the content clustering system of Guriel, to incorporate the teachings of Di Fabbrizio to use an entropy measure that is the inverse of a specificity measure. This serves another way of computing the specificity of a topic (Di Fabbrizio, [0072]). This helps further quantify how specific a query is and generate the best response to it.
Regarding claim 13, the rejection of claim 12 is incorporated. Lightbody, in view of Guriel, discloses all of the elements of the current invention as stated above. However, Lightbody, in view of Guriel, fails to expressly recite wherein computing the query breadth score for a search query comprises: computing an entropy score, wherein the entropy score is an inverse measure of a specificity of the search query.
Di Fabbrizio teaches wherein computing the query breadth score for a search query comprises: computing an entropy score, wherein the entropy score is an inverse measure of a specificity of the search query (Di Fabbrizio, [0072]: "The specificity measure can measure the information needs of users for provide questions. For example, the specificity measure can be determined as the inverse entropy of a topic profile computed for a given term “t”. ").
Lightbody, Guriel, and Di Fabbrizio are analogous arts because they each belong to the same field of data processing systems. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the shopping system of Lightbody, as modified by the content clustering system of Guriel, to incorporate the teachings of Di Fabbrizio to use an entropy measure that is the inverse of a specificity measure. This serves another way of computing the specificity of a topic (Di Fabbrizio, [0072]). This helps further quantify how specific a query is and generate the best response to it.
Claim(s) 4, 5, 14, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lightbody, in view of Guriel, as applied to claims 1, 2, 7-12, 17-20 above, and further in view of Yang et al. (US Pat. No. 12,210,576 B1 hereinafter Yang).
Regarding claim 4, the rejection of claim 1 is incorporated. Lightbody, in view of Guriel, discloses all of the elements of the current invention as stated above. However, Lightbody, in view of Guriel, fails to expressly recite wherein computing the query specificity score for a search query comprises: computing a frequency score based on the search query, wherein the frequency score represents a number of times that the search query is received over a time period.
Yang teaches wherein computing the query specificity score for a search query comprises: computing a frequency score based on the search query, wherein the frequency score represents a number of times that the search query is received over a time period (Yang, Col. 6, lines 28-37: "As described in further detail below, for item text, the periodic sales concentration of the item over historical time periods may be provided. For example, a vector having an element corresponding to each month of the year, with a normalized per-month percentage of sales may be the periodic sales concentration data for an item. In the query context, a periodic query concentration (e.g., a number of times a particular query was received (or a percentage representing the proportional incidence of the query) during a particular time period may be represented using a vector.").
Lightbody, Guriel, and Yang are analogous arts because they each belong to the same field of content searching systems. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the shopping system of Lightbody, as modified by the content clustering system of Guriel, to incorporate the teachings of Yang to determine a frequency score for a query. Search frequency information can help identify when certain items are searched for the most, such as in particular seasons or near certain holidays (Yang, Col. 5, paragraph 2). This helps the system provide more relevant search results to a user.
Regarding claim 5, the rejection of claim 4 is incorporated. Lightbody, in view of Guriel and Yang, discloses all of the elements of the current invention as stated above. Yang further teaches wherein computing the frequency score for a search query comprises: computing a percentage of a total number of queries received over the time period were the search query (Yang, Col. 6, lines 28-37: "As described in further detail below, for item text, the periodic sales concentration of the item over historical time periods may be provided. For example, a vector having an element corresponding to each month of the year, with a normalized per-month percentage of sales may be the periodic sales concentration data for an item. In the query context, a periodic query concentration (e.g., a number of times a particular query was received (or a percentage representing the proportional incidence of the query) during a particular time period may be represented using a vector."). The same motivation for claim 4 applies equally to claim 5.
Regarding claim 14, the rejection of claim 11 is incorporated. Lightbody, in view of Guriel, discloses all of the elements of the current invention as stated above. However, Lightbody, in view of Guriel, fails to expressly recite wherein computing the query specificity score for a search query comprises: computing a frequency score based on the search query, wherein the frequency score represents a number of times that the search query is received over a time period.
Yang teaches wherein computing the query specificity score for a search query comprises: computing a frequency score based on the search query, wherein the frequency score represents a number of times that the search query is received over a time period (Yang, Col. 6, lines 28-37: "As described in further detail below, for item text, the periodic sales concentration of the item over historical time periods may be provided. For example, a vector having an element corresponding to each month of the year, with a normalized per-month percentage of sales may be the periodic sales concentration data for an item. In the query context, a periodic query concentration (e.g., a number of times a particular query was received (or a percentage representing the proportional incidence of the query) during a particular time period may be represented using a vector.").
Lightbody, Guriel, and Yang are analogous arts because they each belong to the same field of content searching systems. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the shopping system of Lightbody, as modified by the content clustering system of Guriel, to incorporate the teachings of Yang to determine a frequency score for a query. Search frequency information can help identify when certain items are searched for the most, such as in particular seasons or near certain holidays (Yang, Col. 5, paragraph 2). This helps the system provide more relevant search results to a user.
Regarding claim 15, the rejection of claim 14 is incorporated. Lightbody, in view of Guriel and Yang, discloses all of the elements of the current invention as stated above. Yang further teaches wherein computing the frequency score for a search query comprises: computing a percentage of a total number of queries received over the time period were the search query (Yang, Col. 6, lines 28-37: "As described in further detail below, for item text, the periodic sales concentration of the item over historical time periods may be provided. For example, a vector having an element corresponding to each month of the year, with a normalized per-month percentage of sales may be the periodic sales concentration data for an item. In the query context, a periodic query concentration (e.g., a number of times a particular query was received (or a percentage representing the proportional incidence of the query) during a particular time period may be represented using a vector."). The same motivation for claim 14 applies equally to claim 15.
Claim(s) 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lightbody, in view of Guriel, as applied to claims 1, 2, 7-12, and 17-20 above, and further in view of Bhagat et al. (US Pat. No. 10,304,082 B1 hereinafter Bhagat).
Regarding claim 6, the rejection of claim 1 is incorporated. Lightbody, in view of Guriel, discloses all of the elements of the current invention as stated above. However, Lightbody, in view of Guriel, fails to expressly recite wherein computing the query specificity score comprises computing a weighted combination of a set of sub-scores.
Bhagat teaches wherein computing the query specificity score comprises computing a weighted combination of a set of sub-scores (Bhagat, Col. 17, lines 1-19: "In some embodiments, the search application 224 may be able to predict the particular result (e.g., item 106) that the entity 103 is interested in based at least in part on the search query and/or a variety of other factors. The factors may include, for example, repeated behavior by the entity 103 with respect to an item 106, preference data 245, interaction history 242, specificity of the search query (e.g., brand name of item included), relationship to prior purchases, and so on. The factors may be evaluated in order to determine a level of confidence regarding whether a particular item 106 is the desired result of the entity 103 requesting search results. For example, the factors may be assigned a score based in part on a sum of weighted values. If the score meets or exceeds a predefined threshold, the search application 224 may determine to redirect the entity 103 to alternative network associated with the particular item 106 rather than the originally requested search results associated with the search query 406.").
Lightbody, Guriel, and Bhagat are analogous arts because they each belong to the same field of content searching systems. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the shopping system of Lightbody, as modified by the content clustering system of Guriel, to incorporate the teachings of Bhagat to use a weighted combination of sub-scores for computing specificity. This allows for numerous factors to influence the overall specificity score, and certain factors can be more influential than others (Bhagat, Col. 2, paragraph 2). As such, the system can better determine the specificity of the query, and provide better results for the user.
Regarding claim 16, the rejection of claim 11 is incorporated. Lightbody, in view of Guriel, discloses all of the elements of the current invention as stated above. However, Lightbody, in view of Guriel, fails to expressly recite wherein computing the query specificity score comprises computing a weighted combination of a set of sub-scores.
Bhagat teaches wherein computing the query specificity score comprises computing a weighted combination of a set of sub-scores (Bhagat, Col. 17, lines 1-19: "In some embodiments, the search application 224 may be able to predict the particular result (e.g., item 106) that the entity 103 is interested in based at least in part on the search query and/or a variety of other factors. The factors may include, for example, repeated behavior by the entity 103 with respect to an item 106, preference data 245, interaction history 242, specificity of the search query (e.g., brand name of item included), relationship to prior purchases, and so on. The factors may be evaluated in order to determine a level of confidence regarding whether a particular item 106 is the desired result of the entity 103 requesting search results. For example, the factors may be assigned a score based in part on a sum of weighted values. If the score meets or exceeds a predefined threshold, the search application 224 may determine to redirect the entity 103 to alternative network associated with the particular item 106 rather than the originally requested search results associated with the search query 406.").
Lightbody, Guriel, and Bhagat are analogous arts because they each belong to the same field of content searching systems. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the shopping system of Lightbody, as modified by the content clustering system of Guriel, to incorporate the teachings of Bhagat to use a weighted combination of sub-scores for computing specificity. This allows for numerous factors to influence the overall specificity score, and certain factors can be more influential than others (Bhagat, Col. 2, paragraph 2). As such, the system can better determine the specificity of the query, and provide better results for the user.
Conclusion
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/TYLER BECKER/ Examiner, Art Unit 2657
/DANIEL C WASHBURN/ Supervisory Patent Examiner, Art Unit 2657
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