CTNF 18/466,515 CTNF 83032 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 1. The present application 18/466,515, filed on 09/13/2023, is being examined under the first inventor to file provisions of the AIA. Drawings 2. The drawings received on 09/13/2023 are accepted by the Examiner. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. 3. Claims 1-20 are rejected under 35 U.S.C 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more. Claim 1 is directed to an abstract idea of modifying inputs for an artificial intelligence model, as explained in detail below. The claim does not include elements that are sufficient to amount to significantly more than the judicial exception because the elements can be concepts performed in the human mind which do not add meaningful limits to practicing the abstract idea. Claim 1 recites a system for modifying inputs for an artificial intelligence model comprising at least in part: receive an input requesting a visual media output ( e.g., observing an input can be performed in the human mind) ; determine one or more keywords included in the input ( e.g., observing and evaluating one or more keywords in the input can be performed in the human mind) ; identify, for at least one keyword for the one or more keywords, an association between the at least one keyword and an entity parameter, wherein the entity parameter is indicative of an entity ( e.g., observing and evaluating one or more keywords in the input can be performed in the human mind); modify, based on the association, the input to create a modified input, wherein the modified input includes a modification to the at least one keyword to cause the at least one keyword to be indicative of the entity; ( e.g., changing the input to a modified input with respect to the evaluated keywords can be performed in the human mind including observation, evaluation and judgement) ; provide, to the artificial intelligence model, the modified input ( e.g., providing the modified input to the AI model can be achieved using a computer as a tool) ; obtain, via the artificial intelligence model and based on providing the modified input (e .g., obtaining results can be performed in the human mind including observation, evaluation and judgment ); Claim 1, as it is recited, falls within one of the groupings of abstract ideas [e.g., mental process] enumerated in the 2019 PEG. The recited concept can be performed in the human mind, including observation, evaluation, judgement, and opinion. That is, other than reciting a system comprising one or more memories; and one or more processors to modify input for an artificial intelligence model, nothing in the claim precludes the step from practically being performed in the mind. The formulating and the learning features in the claim are recited at a high level of generality and add no more to the claimed invention than a computer to perform an abstract idea. The additional feature merely uses a computer/device as a tool to display result after a series of data gathering step is insignificant extra-solution activity, thus, the judicial exception is not integrated into a practical application. The additional feature does not appear to be improvements to the functioning of a computer or to any other technology or technical field. The additional feature does not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitation as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Therefore, claim 1 is not patent eligible. Claims 2-7 recite similar features as claim 1, are fall within one of the groupings of abstract ideas [e.g., mental process] enumerated in the 2019 PEG. The recited concept can be performed in human mind including observation, evaluation, judgement, opinion. Claims 2-7 recite searching an entity database to identify the entity parameter using at least a keyword, determining semantic meaning associated with the input using a natural language processing operation and modifying the input based on an entity modifier in the input to output at least one visual element. There are no additional features turning the judicial exception into a practical application. The claimed features do not appear to be improvements to the functioning of a computer or to any other technology or technical field. Therefore, claims 2-7 are not patent eligible. Claim 8 recites a method of modifying inputs for an artificial intelligence model comprising at least in part: receive an input for an artificial intelligence model, wherein the input describes an output of the artificial intelligence model ( e.g., observing an input and producing an output using an AI model can be performed in the human mind) ; generating an output based on a modified input that is based on modifying one or more keywords included in the input to be indicative of respective entities based on the one or more keywords being associated with respective entity parameters ( e.g., observing and evaluating one or more keywords in the input and generating an output based on modifying the observed and evaluated keywords can be performed in the human mind) ; providing the output, wherein the output includes visual elements associated with respective keyword of the one or more keywords that indicative the respective entities, an association between the at least one keyword and an entity parameter ( e.g., providing a visual result can be achieved in the human mind including observation, evaluation and judgment) ; Claim 8, as it is recited, falls within one of the groupings of abstract ideas [e.g., mental process] enumerated in the 2019 PEG. The recited concept can be performed in the human mind, including observation, evaluation, judgement, and opinion. That is, other than reciting a device comprising an artificial intelligence model, nothing in the claim precludes the step from practically being performed in the mind. The formulating and the learning features in the claim are recited at a high level of generality and add no more to the claimed invention than a computer to perform an abstract idea. The additional feature merely uses a computer/device as a tool to display result after a series of data gathering step is insignificant extra-solution activity, thus, the judicial exception is not integrated into a practical application. The additional feature does not appear to be improvements to the functioning of a computer or to any other technology or technical field. The additional feature does not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitation as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Therefore, claim 8 is not patent eligible. Claims 9-15 recite similar features as claim 8, are fall within one of the groupings of abstract ideas [e.g., mental process] enumerated in the 2019 PEG. The recited concept can be performed in human mind including observation, evaluation, judgement, opinion. Claims 9-15 recite providing an indication of associations between keywords and entity parameters to train the artificial intelligence model to modify inputs; parsing the input using a natural language processing operation and determining entity scores for respective entity parameters. There are no additional features turning the judicial exception into a practical application. The claimed features do not appear to be improvements to the functioning of a computer or to any other technology or technical field. Therefore, claims 9-15 are not patent eligible. Claim 16 recites a system for modifying inputs for an artificial intelligence model comprising at least in part: receive an input requesting a visual media output ( e.g., observing an input can be performed in the human mind) ; determine one or more keywords included in the input ( e.g., observing and evaluating one or more keywords in the input can be performed in the human mind) ; identify, for at least one keyword for the one or more keywords, an association between the at least one keyword and an entity parameter, wherein the entity parameter is indicative of an entity ( e.g., observing and evaluating one or more keywords in the input can be performed in the human mind); modify, based on the association, the input to create a modified input, wherein the modified input includes a modification to the at least one keyword to cause the at least one keyword to be indicative of the entity; ( e.g., changing the input to a modified input with respect to the evaluated keywords can be performed in the human mind including observation, evaluation and judgement) ; provide, to the artificial intelligence model, the modified input ( e.g., providing the modified input to the AI model can be achieved using a computer as a tool) ; obtain, via the artificial intelligence model and based on providing the modified input (e .g., obtaining results can be performed in the human mind including observation, evaluation and judgment ); Claim 16, as it is recited, falls within one of the groupings of abstract ideas [e.g., mental process] enumerated in the 2019 PEG. The recited concept can be performed in the human mind, including observation, evaluation, judgement, and opinion. That is, other than reciting a system comprising one or more memories; and one or more processors to modify input for an artificial intelligence model, nothing in the claim precludes the step from practically being performed in the mind. The formulating and the learning features in the claim are recited at a high level of generality and add no more to the claimed invention than a computer to perform an abstract idea. The additional feature merely uses a computer/device as a tool to display result after a series of data gathering step is insignificant extra-solution activity, thus, the judicial exception is not integrated into a practical application. The additional feature does not appear to be improvements to the functioning of a computer or to any other technology or technical field. The additional feature does not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitation as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Therefore, claim 1 is not patent eligible. Claims 17-20 recite similar features as claim 1, are fall within one of the groupings of abstract ideas [e.g., mental process] enumerated in the 2019 PEG. The recited concept can be performed in human mind including observation, evaluation, judgement, opinion. Claims 17-20 recite searching an entity database to identify the entity parameter using at least a keyword, determining semantic meaning associated with the input using a natural language processing operation and modifying the input based on an entity modifier in the input to output at least one visual element. There are no additional features turning the judicial exception into a practical application. The claimed features do not appear to be improvements to the functioning of a computer or to any other technology or technical field. Therefore, claims 17-20 are not patent eligible. C laim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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. 07-07-aia AIA 07-07 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 – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-15 AIA Claim s 1, 2, 6, 8-12, 14, 16, 17 and 20 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Ganapathy et al. (US 2022/0004571 A)., hereinafter Ganapathy . Referring to claims 1 and 16, Ganapathy discloses a system for modifying inputs for an artificial intelligence model ( See para. [0022], a system for extracting words, phrases and/or values for refining inputs for machine learning model(s) ), the system comprising: one or more memories ( See para. [0103], para. [0112] and Figure 15, one or more memory devices ); and one or more processors ( See para. [0024], para. [0026] and Figures 2, one or more processor(s) perform information extraction in input ) communicatively coupled to the one or more memories ( See para. [0103], para. [0112] and Figure 15, one or more memory devices ), configured to: receive, via a user device, an input ( See para. [0011], para. [0025] and Figures 1 and 10, receiving an input ) requesting a visual media output ( See para. [0011] and Figure 10, generating a report based on the received input, note in para. [0046], the report includes tables, graphs, charts and/or other appropriate representations of data, arranged into a particular layout ); determine one or more keywords included in the input ( See para. [0026], para. [0027] and Figures 1 and 2, the system performs information extraction 202 on input 201 and determines or identifies elements or language features including keywords [e.g., keywords 203 to 201 ); identify, for at least one keyword of the one or more keywords, an association between the at least one keyword and an entity parameter ( See para. [0027] and para. [0028, the system compares portions of the input [e.g. keywords 203-204] to a listing of keywords and idenfies matches or partial matches ), wherein the entity parameter is indicative of an entity ( See para. [0027], numerical elements or specified phrases [e.g. last year] are identified as values 205 or 207 by comparing portions of input to a listing of system entities ); modify, based on the association, the input to create a modified input, wherein the modified input includes a modification to the at least one keyword to cause the at least one keyword to be indicative of the entity ( See para. [0022], para. [0031], para. [0041] and Figure 3; in response to the received input 201, a different intent may be selected or intent 309 may be modified to reflect an input-specific value rather than the default value. For instance, if an input 201 for “top 10 customers last year by total sales” was received, a selected intent may refer to a database element such as “sales” or “total sales”, rather than the default column “orders” 314 ) . provide, to the artificial intelligence model, the modified input ( See para. [0022], para. [0066] and claim 7, providing the identified/selected intent associated with the input to one or more machine learning model, note the machine learning model (s) are refined on an ongoing based on search intent, words or phrases ); obtain, via the artificial intelligence model and based on providing the modified input, the visual media output ( See para. [0066], para. [0077], para. [0078] and Figure 10 obtaining a report via the machine learning model by analyzing similarity of inputs 107, where similar inputs [e.g., inputs with matching keywords] may indicate satisfaction with a provided report) and provide, to the user device, the visual media output for display ( See para. [0077], para. [0078] and Figure 10, providing a chart included in the report to the user ). As to claims 2 and 17 , Ganapathy discloses wherein the one or more processors ( See para. [0024] and Figure 1, one or more processor 102 ), to identify the association ( See para. [0022], para. [0051], association between intents and various entities ), are configured to: search, using the at least on keyword, an entity database to identify the entity parameter ( See para. [0027] and para. [0028], the system compares portions of the input [e.g. keywords 203-204] to a listing of keywords and idenfies matches or partial matches ). As to claims 6 and 20 , Ganapathi discloses wherein, based on the modified input, the visual media output includes at least one visual element that visually indicates that the at least one visual element is associated with the entity, wherein the at least one keyword indicates the at least one visual element ( See para. [0066], para. [0077], para. [0078] and Figure 10 obtaining a report via the machine learning model by analyzing similarity of inputs 107, where similar inputs [e.g., inputs with matching keywords] may indicate satisfaction with a provided report, note in para. [0077], para. [0078] and Figure 10, providing a chart included in the report to the user) . Referring to claim 8, Ganapathi discloses a method of modifying inputs for an artificial intelligence model ( See para. [0022], a system for extracting words, phrases and/or values for refining inputs for machine learning model(s) ), comprising: receiving, by a device, an input for the artificial intelligence model ( See para. [0011], para. [0025] and Figures 1 and 10, receiving an input ), wherein the input describes an output of the artificial intelligence model ( See para. [0011] and Figure 10, generating a report based on the received input, note in para. [0046], the report includes tables, graphs, charts and/or other appropriate representations of data, arranged into a particular layout ); generating, by the device and via the artificial intelligence model, the output based on a modified input that is based on modifying one or more keywords included in the input to be indicative of respective entities based on the one or more keywords being associated with respective entity parameters ( See para. [0022], para. [0031], para. [0041] and Figure 3; in response to the received input 201, a different intent may be selected or intent 309 may be modified to reflect an input-specific value rather than the default value. For instance, if an input 201 for “top 10 customers last year by total sales” was received, a selected intent may refer to a database element such as “sales” or “total sales”, rather than the default column “orders” 314 ) ; and providing, by the device and based on receiving the input, the output for display ( See para. [0022], para. [0066] and claim 7, providing the identified/selected intent associated with the input to one or more machine learning model, note the machine learning model (s) are refined on an ongoing based on search intent, words or phrases ), wherein the output includes visual elements, associated with respective keywords of the one or more keywords, that indicate the respective entities ( See para. [0066], para. [0077], para. [0078] and Figure 10 obtaining a report via the machine learning model by analyzing similarity of inputs 107, where similar inputs [e.g., inputs with matching keywords] may indicate satisfaction with a provided report) . As to claim 9 , Ganapathy discloses wherein generating the output comprises: modifying the input to the modified input based on modifying the one or more keywords to be indicative of the respective entities ( See para. [0022], para. [0031], para. [0041] and Figure 3; in response to the received input 201, a different intent may be selected or intent 309 may be modified to reflect an input-specific value rather than the default value. For instance, if an input 201 for “top 10 customers last year by total sales” was received, a selected intent may refer to a database element such as “sales” or “total sales”, rather than the default column “orders” 314 ) ; providing, to the artificial intelligence model, the modified input; and obtaining, from the artificial intelligence model, the output ( See para. [0022], para. [0066] and claim 7, providing the identified/selected intent associated with the input to one or more machine learning model, note the machine learning model (s) are refined on an ongoing based on search intent, words or phrases ). As to claim 10 , Ganapathy discloses wherein generating the output comprises: providing, to the artificial intelligence model, the input; and obtaining, from the artificial intelligence model, the output that is based on the modified input ( See para. [0022], para. [0031], para. [0041] and Figure 3; in response to the received input 201, a different intent may be selected or intent 309 may be modified to reflect an input-specific value rather than the default value. For instance, if an input 201 for “top 10 customers last year by total sales” was received, a selected intent may refer to a database element such as “sales” or “total sales”, rather than the default column “orders” 314 ) . As to claim 11 , Ganapathy discloses providing, to the artificial intelligence model, an indication of associations between keywords and entity parameters to train the artificial intelligence model to modify inputs based on the associations ( See para. [0044], a user enters text characters into a query field, a typeahead process may attempt to identify one or more user nodes 202, concept nodes 204, or edges 206 that match the string of characters entered into the query field as the user is entering the characters. As the typeahead process receives requests or calls including a string or n-gram from the text query, the typeahead process may perform or cause to be performed a search to identify existing social-graph elements [i.e., user nodes 202, concept nodes 204, edges 206] having respective names, types, categories, or other identifiers matching the entered text ). Therefore, it 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 was made to modify the association of Ganapathi to determine a category associated with at least one keyword, as taught by Wang. Skilled artisan would have been motivated to return fewer search results that are relevant to a query. This would improve the overall efficiency of the search engine by reducing the amount of computing resources consumed (See Wang, para. [0075]). In addition, both references (Wang and Ganapathi) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as providing relevant results based on intended words of an inputted query. This close relation between both references highly suggests an expectation of success As to claim 12 , Ganapathi discloses parsing, using a natural language processing operation, the input to identify a set of keywords; and identifying the one or more keywords from the set of keywords based on the one or more keywords being associated with the respective entity parameters ( See para. [0031] and Figure 3, map one or more natural language elements [e.g., a keyword] to one or more use cases, such as a set of associated database elements [e.g., one or more columns of a table]. Search intents may be associated with default or system-level entities or keywords [e.g., “top”], entities or keywords specific to live dashboard system 100 and/or database 106 [e.g., “customer”], and/or entities or keywords associated with usage sessions of live dashboard system 100. Intent selection 308 may identify multiple relevant intents including a “top customers” intent 309 and a default intent 310, and may select among the identified intents based on various relevant criteria, such as keyword matching, phrase matching, previous selection history, default rankings, etc. Each relevant intent may include a unique intent name or other identifier [e.g., intent “top customers” 309]. Each intent may include (or refer to) various synonyms or other associated extracted elements or element classifications. For instance, inputs with elements such as “top clients”, “top accounts”, etc. may be mapped to intent “top customers” 309 via synonym lookup or other classification features ). As to claim 14 , Ganapathi discloses wherein the visual elements are visually indicative of the respective entities ( See para. [0066], para. [0077], para. [0078] and Figure 10 obtaining a report via the machine learning model by analyzing similarity of inputs 107, where similar inputs [e.g., inputs with matching keywords] may indicate satisfaction with a provided report) . Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim s 3-5, 7, 13, 15, 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ganapathy and in view of Wang et al. (US 2018/0150552 A1), hereinafter Wang . As to claims 3 and 18 , Ganapathy discloses wherein the one or more processors ( See para. [0026] and para. [0027], one or more professors ) are further configured to: determine, via a natural language processing operation ( See para. [0031] and Figure 3, each search intent maps one or more natural language element ), a first semantic meaning associated with the input, and wherein the one or more processors, to modify the input ( See para. [0031] and Figure 3, map one or more natural language elements [e.g., a keyword] to one or more use cases, such as a set of associated database elements [e.g., one or more columns of a table]. Search intents may be associated with default or system-level entities or keywords [e.g., “top”], entities or keywords specific to live dashboard system 100 and/or database 106 [e.g., “customer”], and/or entities or keywords associated with usage sessions of live dashboard system 100. Intent selection 308 may identify multiple relevant intents including a “top customers” intent 309 and a default intent 310, and may select among the identified intents based on various relevant criteria, such as keyword matching, phrase matching, previous selection history, default rankings, etc. Each relevant intent may include a unique intent name or other identifier [e.g., intent “top customers” 309]. Each intent may include (or refer to) various synonyms or other associated extracted elements or element classifications. For instance, inputs with elements such as “top clients”, “top accounts”, etc. may be mapped to intent “top customers” 309 via synonym lookup or other classification features ), are configured to: determine, via the natural language processing operation, a second semantic meaning associated with the modified input ( See para. [0026], para. [0031] and Figure 3 , Intent selection 308 may identify multiple relevant intents including a “top customers” intent 309 and a default intent 310, and may select among the identified intents based on various relevant criteria, such as keyword matching, phrase matching, previous selection history, default rankings, etc. Each relevant intent may include a unique intent name or other identifier [e.g., intent “top customers” 309]. Each intent may include (or refer to) various synonyms or other associated extracted elements or element classifications. For instance, inputs with elements such as “top clients”, “top accounts”, etc. may be mapped to intent “top customers” 309 via synonym lookup or other classification features ); and determine a similarity metric indicating a similarity between the first semantic meaning and the second semantic meaning ( See para. [0030] keywords 203-204 may include or may be associated with various synonyms, misspellings, grammatical variations, and/or other associated terms. For instance, “client”, “clients”, “clientele”, “account”, “accounts”, and/or other similar terms may be included as keywords based on identification of keyword “customers” 203. As another example, keywords such as “best”, “upper”, etc. may be included as keywords based on identification of keyword “top ”), wherein providing the modified input is based on the similarity metric […] ( See para. [0066], the machine learning 515 analyzes similarity of inputs 107, where similar inputs [e.g., inputs with matching keywords] ). Ganapathi does not explicitly disclose providing the modified input is based on the similarity metric satisfying a similarity threshold. Wang discloses providing the modified input is based on the similarity metric satisfying a similarity threshold ( See para. [0066], one or more-word senses may comprise each word sense having a similarity metric greater than or equal to a threshold similarity metric of the embedding of the n-gram and the embedding of the word sense. As an example, and not by way of limitation, for the n-gram “bass”, a cosine similarity of the embedding of “bass” the embeddings of the word senses “fish”, “low pitch”, and “instrument” may be 0.8, 0.76, and 0.62, respectively. The word senses with at least a threshold cosine similarity of 0.75 may be selected, which may correspond to the word senses “fish” and “low pitch ”). Therefore, it 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 was made to modify the modified input of Ganapathi to satisfy a similarity threshold, as taught by Wang. Skilled artisan would have been motivated to return search results that are relevant to a query when there are hundreds of millions, or possibly billions, of objects to search through (See Wang, para. [0005]). In addition, both references (Wang and Ganapathi) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as providing relevant results based on intended words of an inputted query. This close relation between both references highly suggests an expectation of success As to claim 4 , Ganapathi does not explicitly disclose identifying the association, are configured to: determine a category associated with the at least one keyword, wherein the association is between the category and the entity parameter. Wang discloses identifying the association, are configured to: determine a category associated with the at least one keyword, wherein the association is between the category and the entity parameter ( See para. [0044], a user enters text characters into a query field, a typeahead process may attempt to identify one or more user nodes 202, concept nodes 204, or edges 206 that match the string of characters entered into the query field as the user is entering the characters. As the typeahead process receives requests or calls including a string or n-gram from the text query, the typeahead process may perform or cause to be performed a search to identify existing social-graph elements [i.e., user nodes 202, concept nodes 204, edges 206] having respective names, types, categories, or other identifiers matching the entered text ). Therefore, it 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 was made to modify the association of Ganapathi to determine a category associated with at least one keyword, as taught by Wang. Skilled artisan would have been motivated to return fewer search results that are relevant to a query. This would improve the overall efficiency of the search engine by reducing the amount of computing resources consumed (See Wang, para. [0075]). In addition, both references (Wang and Ganapathi) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as providing relevant results based on intended words of an inputted query. This close relation between both references highly suggests an expectation of success As to claims 5 and 19 , Ganapathi does not explicitly determine, based on the input, whether the at least one keyword is modified by an entity modifier in the input, wherein modifying the input is based on the at least one keyword not being modified by the entity modifier in the input. Wang discloses determine, based on the input, whether the at least one keyword is modified by an entity modifier in the input, wherein modifying the input is based on the at least one keyword not being modified by the entity modifier in the input ( See para. [0077] and Figure 8, the function may be based on a data structure mapping head-terms to modifier-terms, such as the table 800 illustrated in FIG. 8. As an example and not by way of limitation, referencing FIG. 5, the inputted query may be the query 510 “kids toys”, and the identified n-grams of the query may be <“kids”, “toys”>. A function ƒ(w.sub.1, w.sub.2) may return a value indicating whether w.sub.1 is a head-term or a modifier-term, and whether w.sub.2 is a head-term or a modifier-term based on a data structure [e.g., the function may return 1 if w.sub.1 is a head-term and 0 otherwise, the function may return 1 if w.sub.2 is a head-term and 0 otherwise, etc .) Therefore, it 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 was made to modify the Ganapathi system to determine, based on the input, whether the at least one keyword is modified by an entity modifier in the input, as taught by Wang. Skilled artisan would have been motivated to return fewer search results that are relevant to a query. This would improve the overall efficiency of the search engine by reducing the amount of computing resources consumed (See Wang, para. [0075]). In addition, both references (Wang and Ganapathi) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as providing relevant results based on intended words of an inputted query. This close relation between both references highly suggests an expectation of success As to claims 7 and 15 , Ganapathi discloses wherein the one or more processors, to identify the association, are configured to: determine, for the at least one keyword, a set of entity parameters associated with the at least one keyword ( See para. [0027] and para. [0028, the system compares portions of the input [e.g. keywords 203-204] to a listing of keywords and idenfies matches or partial matches, note the numerical elements or specified phrases [e.g. last year] are identified as values 205 or 207 by comparing portions of input to a listing of system entities ). Ganapathi does not explicitly disclose determine entity scores for respective entity parameters from the set of entity parameters; and determine that the at least one keyword is associated with the entity parameter based on an entity score associated with the entity parameter being a highest entity score among the entity scores. Wang discloses determine entity scores for respective entity parameters from the set of entity parameters; and determine that the at least one keyword is associated with the entity parameter based on an entity score associated with the entity parameter being a highest entity score among the entity scores ( See para. [0063] and Figure 6, the candidate subsets. Because there is a plurality of identified candidate subsets of n-grams, the social-networking system 160 may calculate, for each identified candidate subset of n-grams, a relatedness-score. If, for example, the similarity score is a cosine similarity, a relatedness-score for the identified subset of n-grams “mad dog”, “trainer “may be calculated as and a relatedness-score for the identified subset of n-grams “mad”, “dog trainer ). Therefore, it 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 was made to modify the Ganapathi system to determine entity scores, as taught by Wang. Skilled artisan would have been motivated to return fewer search results that are relevant to a query. This would improve the overall efficiency of the search engine by reducing the amount of computing resources consumed (See Wang, para. [0075]). In addition, both references (Wang and Ganapathi) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as providing relevant results based on intended words of an inputted query. This close relation between both references highly suggests an expectation of success. As to claim 13 , Ganapathy does not explicitly disclose identifying the one or more keywords is based on the one or more keywords not being modified by entity modifiers in the input. Wang discloses identifying the one or more keywords is based on the one or more keywords not being modified by entity modifiers in the input ( See para. [0077] and Figure 8, the function may be based on a data structure mapping head-terms to modifier-terms, such as the table 800 illustrated in FIG. 8. As an example and not by way of limitation, referencing FIG. 5, the inputted query may be the query 510 “kids toys”, and the identified n-grams of the query may be <“kids”, “toys”>. A function ƒ(w.sub.1, w.sub.2) may return a value indicating whether w.sub.1 is a head-term or a modifier-term, and whether w.sub.2 is a head-term or a modifier-term based on a data structure [e.g., the function may return 1 if w.sub.1 is a head-term and 0 otherwise, the function may return 1 if w.sub.2 is a head-term and 0 otherwise, etc .) Therefore, it 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 was made to modify the Ganapathi system to determine, based on the input, whether the at least one keyword is modified by an entity modifier in the input, as taught by Wang. Skilled artisan would have been motivated to return fewer search results that are relevant to a query. This would improve the overall efficiency of the search engine by reducing the amount of computing resources consumed (See Wang, para. [0075]). In addition, both references (Wang and Ganapathi) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as providing relevant results based on intended words of an inputted query. This close relation between both references highly suggests an expectation of success Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Bender et al. (US 2021/0294829 A1) discloses a method including obtaining parameters and a document, determining a domain based on the parameters, where the domain maps to a first ontology, and where ontologies map n-grams onto a set of concepts. The method includes scoring a first set of n-grams of the document using a scoring model based on relations between members of the first set of n-grams, selecting sections of the text based on n-gram scores provided by the scoring model, and determining an initial n-gram set, where each respective n-gram of the initial n-gram set maps to a respective concept of the set of concepts, and where each respective n-gram is identified by an ontology other than the first ontology. The method includes determining related n-grams mapped to the set of concepts associated with the domain and generating a text summary for the document based on the sections and the related n-grams. Wang et al. (US 2018/0150552 A1) discloses a method includes receiving, from a client system of a user of an online social network, a query inputted by the user, wherein the query comprises multiple n-grams; determining one or more head-terms and one or more modifier-terms of the n-grams based on a syntactic model; identifying one or more objects matching at least a portion of the query; ranking each identified object based on a quality of matching of the object to the determined head-terms and modifier-terms; and sending, to the client system in response to the query, a search-results interface for display, wherein the search-results interface includes one or more search results corresponding to one or more of the identified objects, respectively, each identified object corresponding to a search result having a rank greater than a threshold rank. Any inquiry concerning this communication or earlier communications from the examiner should be directed to YUK TING CHOI whose telephone number is (571) 270-1637. The examiner can normally be reached Monday-Friday 9am-6pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, AMY NG can be reached at 5712701698. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /YUK TING CHOI/Primary Examiner, Art Unit 2164 Application/Control Number: 18/466,515 Page 2 Art Unit: 2164 Application/Control Number: 18/466,515 Page 3 Art Unit: 2164 Application/Control Number: 18/466,515 Page 4 Art Unit: 2164 Application/Control Number: 18/466,515 Page 5 Art Unit: 2164 Application/Control Number: 18/466,515 Page 6 Art Unit: 2164 Application/Control Number: 18/466,515 Page 7 Art Unit: 2164 Application/Control Number: 18/466,515 Page 8 Art Unit: 2164 Application/Control Number: 18/466,515 Page 9 Art Unit: 2164 Application/Control Number: 18/466,515 Page 10 Art Unit: 2164 Application/Control Number: 18/466,515 Page 11 Art Unit: 2164 Application/Control Number: 18/466,515 Page 12 Art Unit: 2164 Application/Control Number: 18/466,515 Page 13 Art Unit: 2164 Application/Control Number: 18/466,515 Page 14 Art Unit: 2164 Application/Control Number: 18/466,515 Page 15 Art Unit: 2164 Application/Control Number: 18/466,515 Page 16 Art Unit: 2164 Application/Control Number: 18/466,515 Page 17 Art Unit: 2164 Application/Control Number: 18/466,515 Page 18 Art Unit: 2164 Application/Control Number: 18/466,515 Page 19 Art Unit: 2164 Application/Control Number: 18/466,515 Page 20 Art Unit: 2164 Application/Control Number: 18/466,515 Page 21 Art Unit: 2164 Application/Control Number: 18/466,515 Page 22 Art Unit: 2164