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 .
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 therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under U.S.C 101 as being directed to an abstract idea without adding significantly more
Regarding Claim 1
Step 1: “A method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: creating a prompt using a search query and a content recommendation output by a machine learning model in response to the search query is a mental process that can be done with the aid of pen and paper, a person can create a prompt based off of a search query and an output by a machine learning model.
Step 2A Prong 2: The additional limitations causing a large language model (LLM) to generate an evaluation of the content recommendation and the search query using the prompt, wherein the evaluation comprises a relevance score of the content recommendation and the search query are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of evaluating a recommendation from a LLM using a relevance score
and training the machine learning model to generate an updated content recommendation in response to the search query, wherein the training comprises using the relevance score of the content recommendation and the search query. are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of evaluating training a machine learning model to generate recommendations
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation causing a large language model (LLM) to generate an evaluation of the content recommendation and the search query using the prompt, wherein the evaluation comprises a relevance score of the content recommendation and the search query are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of evaluating a recommendation from a LLM using a relevance score
and training the machine learning model to generate an updated content recommendation in response to the search query, wherein the training comprises using the relevance score of the content recommendation and the search query. are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of evaluating training a machine learning model to generate recommendations
Regarding Claim 2
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: wherein the prompt further comprises user information of a user associated with the search query is an abstract idea that can be done with the aid of pen and paper, a person can create a prompt that has user information to it
Step 2A Prong 2 and Step 2B: There are no additional elements recited so the claim does not provide a practical application and is not considered to be significantly more. As such, the claim is patent ineligible.
Regarding Claim 3
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: wherein the evaluation comprises a relevance score of the content recommendation and the search query based on the user information is a mental process that can be done with the aid of pen and paper, a person can using a relevance score to evaluate recommendations and the query from the user information
Step 2A Prong 2 and Step 2B: There are no additional elements recited so the claim does not provide a practical application and is not considered to be significantly more. As such, the claim is patent ineligible.
Regarding Claim 4:
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: wherein the search query is selected from a stable set of search queries, and the stable set of search queries is updated at a first frequency is a mental process that can be done with the aid of pen and paper, a person can observe a set of queries and choose one.
Step 2A Prong 2 and Step 2B: There are no additional elements recited so the claim does not provide a practical application and is not considered to be significantly more. As such, the claim is patent ineligible.
Regarding Claim 5
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: recites the abstract ideas of claim 4
Step 2A Prong 2: The additional limitations wherein the content recommendation is a first content recommendation, the search query is a first search query, the machine learning model is a first machine learning model, and the evaluation is a first evaluation, further comprising: creating the prompt using a second search query and a second content recommendation output by the first machine learning model in response to the second search query, wherein the second search query is selected from a dynamic set of search queries, and the dynamic set of search queries is updated at a second frequency, the second frequency being higher than the first frequency. are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of creating a prompt for a machine learning model from a set of queries that are updated at a faster rate than the queries from another prompt
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation wherein the content recommendation is a first content recommendation, the search query is a first search query, the machine learning model is a first machine learning model, and the evaluation is a first evaluation, further comprising: creating the prompt using a second search query and a second content recommendation output by the first machine learning model in response to the second search query, wherein the second search query is selected from a dynamic set of search queries, and the dynamic set of search queries is updated at a second frequency, the second frequency being higher than the first frequency. are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of creating a prompt for a machine learning model from a set of queries that are updated at a faster rate than the queries from another prompt
Regarding Claim 6
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: Recites the abstract ideas of Claim 1
Step 2A Prong 2: The additional limitations modifying a parameter of the machine learning model in response to the relevance score are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of modifying a parameter in a machine learning model
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation modifying a parameter of the machine learning model in response to the relevance score are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of modifying a parameter in a machine learning model
Regarding Claim 7
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: wherein the content recommendation is a first content recommendation, the evaluation is a first evaluation, and the relevance score is a first relevance score, further comprising: creating a second prompt using the search query and a second content recommendation output by a second machine learning model in response to the search query is an abstract idea that can be done with the aid of pen and paper, a person can create a prompt using a search query and recommendation output from a model.
Step 2A Prong 2: the additional limitation causing the LLM to generate a second evaluation of the second content recommendation and the search query using the second prompt, wherein the evaluation comprises a second relevance score of the second content recommendation and the search query; and providing, to a computing device, a comparison of the first relevance score and the second relevance score are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of using a LLM to evaluate a recommendation
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation causing the LLM to generate a second evaluation of the second content recommendation and the search query using the second prompt, wherein the evaluation comprises a second relevance score of the second content recommendation and the search query; and providing, to a computing device, a comparison of the first relevance score and the second relevance score are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of using a LLM to evaluate a recommendation
Regarding Claim 8
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: wherein the evaluation comprises a reasoning for the relevance score is an abstract idea that can be done with the aid of pen and paper, a person can create a reason for a relevance score
Step 2A Prong 2 and Step 2B: There are no additional elements recited so the claim does not provide a practical application and is not considered to be significantly more. As such, the claim is patent ineligible.
Regarding Claim 9
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: recites the abstract ideas of claim 1
Step 2A Prong 2: the additional limitation generating, by the machine learning model, a ranking score associated with the content recommendation using the search query are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of generating a ranking using a machine learning model
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation generating, by the machine learning model, a ranking score associated with the content recommendation using the search query are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of generating a ranking using a machine learning model
Regarding Claim 10
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: Recites the abstract ideas of claim 9
Step 2A Prong 2: The additional limitation wherein training the machine learning model to generate the updated content recommendation further comprises: combining the ranking score with the relevance score to generate the updated content recommendation are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of combining ranking and relevance scores to generate recommendations and train the model
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation wherein training the machine learning model to generate the updated content recommendation further comprises: combining the ranking score with the relevance score to generate the updated content recommendation are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of combining ranking and relevance scores to generate recommendations and train the model
Regarding Claim 11
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: Recites the abstract ideas of claim 1
Step 2A Prong 2: The additional limitations wherein training the machine learning model to generate the updated content recommendation further comprises: determining, by the machine learning model, the updated content recommendation using the search query and a feature, wherein the feature is based on the relevance score are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of determining recommendations using the search query and feature
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation wherein training the machine learning model to generate the updated content recommendation further comprises: determining, by the machine learning model, the updated content recommendation using the search query and a feature, wherein the feature is based on the relevance score are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of determining recommendations using the search query and feature
Regarding Claim 12
Step 1: “A system” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of 1
Step 2A Prong 2: See the analysis of 1
Step 2B: See the analysis of 1
Regarding Claim 13
Step 1: “The system” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of 4
Step 2A Prong 2: See the analysis of 4
Step 2B: See the analysis of 4
Regarding Claim 14
Step 1: “The system” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of 5
Step 2A Prong 2: See the analysis of 5
Step 2B: See the analysis of 5
Regarding Claim 15
Step 1: “The system” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of 6
Step 2A Prong 2: See the analysis of 6
Step 2B: See the analysis of 6
Regarding Claim 16
Step 1: “A non-transitory machine-readable storage medium” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of 1
Step 2A Prong 2: See the analysis of 1
Step 2B: See the analysis of 1
Regarding Claim 17
Step 1: “The non-transitory machine-readable storage medium” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of 4
Step 2A Prong 2: See the analysis of 4
Step 2B: See the analysis of 4
Regarding Claim 18
Step 1: “The non-transitory machine-readable storage medium” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of 5
Step 2A Prong 2: See the analysis of 5
Step 2B: See the analysis of 5
Regarding Claim 19
Step 1: “The non-transitory machine-readable storage medium” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of 6
Step 2A Prong 2: See the analysis of 6
Step 2B: See the analysis of 6
Regarding Claim 20
Step 1: “The non-transitory machine-readable storage medium” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of 8
Step 2A Prong 2: See the analysis of 8
Step 2B: See the analysis of 8
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 6, 9, 11-12, 15-16, 19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Wang et al (US12287819B2) (“Wang”)
Regarding Claim 1, Wang teaches A method comprising: creating a prompt using a search query and a content recommendation output by a machine learning model in response to the search query ([Column 13: Lines 50-53] teaches a LLM taking a search query and outputting relevant items in response to the query)
causing a large language model (LLM) to generate an evaluation of the content recommendation and the search query using the prompt, wherein the evaluation comprises a relevance score of the content recommendation and the search query ([Column 24: Lines 29-36] teaches a sample training example comprising a query and the recommended items with a relevance score to assess the relevance to the query)
and training the machine learning model to generate an updated content recommendation in response to the search query, wherein the training comprises using the relevance score of the content recommendation and the search query ([Column 24: Lines 29-36] teaches a training example that comprises a query and a relevance score of the relevance to the query. The training example is used to train the LLM [Abstract] teaching this limitation)
Regarding Claim 6, Wang teaches all the limitations of Claim 1
Wang also teaches modifying a parameter of the machine learning model in response to the relevance score ([Column 19: Lines 18-37] Teaches updating a machine learning module’s parameters based off of the loss function. The loss function score is coming from the output of the machine learning model which is using the training example as input data. The training example contains a relevance score teaching the response to the relevance score part of the limitation which would teach this limitation)
Regarding Claim 9, Wang teaches all the limitations of Claim 1
Wang also teaches generating, by the machine learning model, a ranking score associated with the content recommendation using the search query ([Column 14: Lines 13 to 21] teaches ranking prediction items from the search query and using that ranking to select the recommended items teaching this limitation.)
Regarding Claim 11, Wang teaches all the limitations of Claim 1
Wang also teaches wherein training the machine learning model to generate the updated content recommendation further comprises: determining, by the machine learning model, the updated content recommendation using the search query and a feature, wherein the feature is based on the relevance score ([Column 20: Lines 48-50] teaches the online concierge system is handling search queries. The online concierge system is taking in item predictions and using those predictions to select one or more items to recommend to the client with one of the ways the item can be ranked being the relevance score [Column 21: Lines 10-32]. This process of processing predictions and selecting the appropriate recommendations is being interpreted as updated the content recommendation. The data associated with the item includes the different item features [Column 12: Lines 4-9] and with the relevance score being used to select the appropriate items that is being interpreted as a feature based on the relevance score which would teach this limitation)
Regarding Claim 12, See the analysis of Claim 1
Regarding Claim 15, See the analysis of Claim 6
Regarding Claim 16, See the analysis of Claim 1
Regarding Claim 19, See the analysis of Claim 6
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.
Claims 2-3, 8, and 20 are rejected over Wang et al (US12287819B2) (“Wang”) in view of Wang Peng (CN113434763B) (“Peng”)
Regarding Claim 2, Wang teaches all the limitations of Claim 1
Wang does not teach wherein the prompt further comprises user information of a user associated with the search query
However, Peng does teach wherein the prompt further comprises user information of a user associated with the search query. ([Abstract] teaches acquiring search content input by an initial user, this is being interpreted as associating user information with a search query with the search content being the prompt)
Peng and Wang are analogous art because they both deal with recommendations for the user
It would have been obvious to combine Wang with the user information association of Peng. Doing so would provide more effective search results for the user ([Peng-0003]).
Regarding Claim 3, Wang and Peng teaches all the limitations of Claim 2
Peng also teaches wherein the evaluation comprises a relevance score of the content recommendation and the search query based on the user information ([0009] Teaches a recommendation reason for the search results based on the initial user’s search content according to the relevance score. The recommendation reason is being interpreted as an evaluation, with the initial user being interpreted as user information which would teach this limitation)
Regarding Claim 8, Wang teaches all the limitations of Claim 1
Wang does not teach wherein the evaluation comprises a reasoning for the relevance score.
However, Peng does teach wherein the evaluation comprises a reasoning for the relevance score ([teaches generating a recommendation reason according to the relevance score])
Regarding Claim 20, See the analysis of Claim 8
Claims 4-5, 13-14, and 17-18 are rejected over Wang et al (US12287819B2) (“Wang”) in view of Jain et al (US20240256582A1) (“Jain”)
Regarding Claim 4, Wang teaches all the limitations of Claim 1
Wang does not teach wherein the search query is selected from a stable set of search queries, and the stable set of search queries is updated at a first frequency.
However, Jain does teach ([0036] teaches crawling through different web sources for searchable content to use with their search and knowledge management system. The searchable content being extracted for the system is interpreted as a stable set of search queries. The content being extracted at a first update frequence which teaches the first frequency limitation)
Wang and Jain are analogous art because they both deal with using AI with search queries
It would have been obvious to combine Wang with the search query frequencies of Jain. Doing so would increase the quality of the search results ([Jain-0004])
Regarding Claim 5, Wang and Jain teaches all the limitations of Claim 4
Wang also teaches wherein the content recommendation is a first content recommendation, the search query is a first search query, the machine learning model is a first machine learning model, and the evaluation is a first evaluation, further comprising ([Column 24: Lines 29-36] teaches a training example that comprises a query and a relevance score of the relevance to the query. The training example is used to train the LLM [Abstract] teaching this limitation. The training example is being interpreted as content recommendation with the relevance score being the evaluation, teaching this limitation)
Wang does not teach, creating the prompt using a second search query and a second content recommendation output by the first machine learning model in response to the second search query, wherein the second search query is selected from a dynamic set of search queries, and the dynamic set of search queries is updated at a second frequency, the second frequency being higher than the first frequency.
However, Jain does teach creating the prompt using a second search query and a second content recommendation output by the first machine learning model in response to the second search query, wherein the second search query is selected from a dynamic set of search queries, and the dynamic set of search queries is updated at a second frequency, the second frequency being higher than the first frequency ([0037] teaches a search and management system that takes in queries that are ranked and displayed by the user with the ranking here functioning as an evaluation. The system is leveraging generative ai teaching the machine learning model part of the limitation [0013]. [0080] teaches a set of search results and that a summary of search results would be generated if it is detected that a threshold number of users submitted similar queries for a similar thing. It can be reasonably interpreted that if those search queries are being tracked that those search queries can be seen as a dynamic set of search queries teaching this limitation. [0036] teaches a first and second update frequency for crawling different data sources to use in search queries to generate personalized recommendations with the second frequency looking for webpages updated at a shorter time frame than the first frequency)
Regarding Claim 13, See the analysis of Claim 4
Regarding Claim 14, See the analysis of Claim 5
Regarding Claim 17, See the analysis of Claim 4
Regarding Claim 18, See the analysis of Claim 5
Claim 7 is rejected over Wang et al (US12287819B2) (“Wang”) in view of Jain et al (US20240256582A1) (“Jain”), and Baek et al (US20240037154A1) (“Baek”)
Regarding Claim 7, Wang teaches all the limitations of Claim 1
Wang does teach wherein the content recommendation is a first content recommendation, the evaluation is a first evaluation, and the relevance score is a first relevance score, further comprising ([Column 24: Lines 29-36] teaches a training example that comprises a query and a relevance score of the relevance to the query. The training example is used to train the LLM [Abstract] teaching this limitation. The training example is being interpreted as content recommendation with the relevance score being the evaluation, teaching this limitation)
Wang does not teach creating a second prompt using the search query and a second content recommendation output by a second machine learning model in response to the search query;
causing the LLM to generate a second evaluation of the second content recommendation and the search query using the second prompt, wherein the evaluation comprises a second relevance score of the second content recommendation and the search query;
and providing, to a computing device, a comparison of the first relevance score and the second relevance score.
However, Jain does teach creating a second prompt using the search query and a second content recommendation output by a second machine learning model in response to the search query ([0069] teaches a second search result using one or more generative AI model, teaching a second prompt using a second search query and getting results with one of those results being personalized recommendations)
causing the LLM to generate a second evaluation of the second content recommendation and the search query using the second prompt, wherein the evaluation comprises a second relevance score of the second content recommendation and the search query ([0029] teaches the search and knowledge management system being used to rank search results and [0072] teaches a different input prompt being used in the search bar which is being interpreted as a second prompt for the search query. [0078] teaches relevance of the search results being ranked based on its relevance to the search query which is interpreted as an evaluation. The documents being evaluated on relevance is interpreted as a relevance score teaching this limitation)
Wang and Jain are analogous art because they both deal with using AI with search queries
It would have been obvious to combine Wang with the search query frequencies of Jain. Doing so would increase the quality of the search results ([Jain-0004])
Wang and Jain do not teach and providing, to a computing device, a comparison of the first relevance score and the second relevance score.
However, Baek does teach and providing, to a computing device, a comparison of the first relevance score and the second relevance score ([0050] teaches comparing relevance scores to select the most relevant topics)
Wang, Jain and Baek are analogous art because they all deal with using AI and suggesting user content
It would have been obvious to combine Wang with the search query frequencies of Jain and the relevance score comparisons of Baek. Doing so would lead to more efficient systems when it comes to suggesting content ([Baek:0003-0005])
Claim 10 is rejected over Wang et al (US12287819B2) (“Wang”) in view of Christophe et al (US11687968B1) (“Christophe”)
Regarding Claim 10, Wang teaches all the limitations of Claim 9. Wang does not teach wherein training the machine learning model to generate the updated content recommendation further comprises: combining the ranking score with the relevance score to generate the updated content recommendation.
However, Christophe does teach wherein training the machine learning model to generate the updated content recommendation further comprises: combining the ranking score with the relevance score to generate the updated content recommendation ([Column 12: Lines 39-46] Teaches ordering search results according to their ranking. The ranking can be done by the relevance score to the query suggestion teaching the limitation)
Wang and Christophe are analogous art because they both deal with search queries
It would have been obvious to combine Wang with the search query rankings of Christophe. Doing so would lead to more accurate query suggestions ([Christophe Column 1: Lines 65-67 and Column 2: Lines 1-6)
Conclusion
The prior arts are made of record and relied upon is considered to applicant’s disclosure
Guangda et al WO2016137389A1 (2016-09-01) ([Abstract] “Media content relevant to an input video is identified by analyzing video frames in the input video to detect if any of the video frames contain a target product. One or more video frames found to be containing a detected target product is selected and a product thumbnail is generated for each of selected the video frames. At least one product thumbnails is selected and a video product visual index is generated for each of the selected product thumbnail. Relevant media content is then identified for each of the product thumbnails by comparing the video product visual index of each of the selected product thumbnail with a plurality of media content visual index in a media content database. Each media content visual index is associated with one media content in the media content product database”)
Wang et al US20240241897A1 (2024-01-17) ([Abstract] “A system may generate a prompt based in part on a search query from a customer client device. The prompt instructs a machine learned model to provide item predictions. And the model was trained by: converting structured data describing items of an online catalog to annotated text data (unstructured data), generating training examples based in part on the annotated text data, and training the model using the training examples. The system may receive item predictions generated by the prompt being applied to the machine learned model, the item predictions may have corresponding item identifiers. The item predictions are processed to identify a recommended item from the item predictions. The processing includes determining item information for the recommended item using an item identifier associated with the recommended item. The item information is provided to the customer client device.”)
Long et al US20160246791A1 (2016-08-25) ([Abstract] “Methods, systems, and media for presenting search results are provided. In accordance with some embodiments, the method comprises: receiving text corresponding to a search query; determining whether a content rating score associated with the search query is below a predetermined threshold, wherein the score is calculated by: identifying a first plurality of search results retrieved using the search query, wherein each search result is associated with one of a plurality of content ratings classes; and calculating the content rating score that is a proportion of search results associated with at least one of the content ratings classes among the first plurality of search results; in response to determining that the content rating score is below the predetermined threshold, identifying a second plurality of search results to be presented based on the search query; and causing the second plurality of search results to be presented.”)
Lee at al US20140288970A1 (2014-09-25) ([Abstract] “A method for identifying relevant follow-up recommendations from medical reports includes identifying with a processor follow-up recommendations in electronically formatted prior medical reports, and visually presenting, via a display monitor, the identified follow-up recommendations. A computing apparatus (102) including a processor that obtains, in electronic format, an imaging examination order for a follow-up imaging examination of a patient, wherein the imaging examination order at least includes a unique identification of the patient, retrieves electronically formatted prior medical reports of the patient from a data repository based on the patient or the unique identification of the patient, identifies follow-up imaging recommendations in the retrieved electronically formatted prior medical reports, and visually presents the identified follow-up imaging recommendations.”)
Shailen V. Banker US20120011425A1 (2021-01-12) ([Abstract] “A method for graphically linking articles may include the steps of forming a first link between a selected article and a first linked article; forming a second link between the selected article and a second linked article; and interactively displaying the first link and the second link to the user.”)
Shailen V. Banker US20040133342A1 (2004-07-08) ([Abstract] “A linked information system employing evolutionary media content links is provided. In another aspect of the present invention, a media content distribution system includes selected media content accessible to remotely located users in an electronic format over a computer network. A further aspect of the present invention selects media content during the course of a human analysis based on relevance to tracked topics.”)
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/URIAH VENDELL MOORE/Examiner, Art Unit 2142
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
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