DETAILED ACTION
This is in response to the application filed on 11/07/2024.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-16, 18-19, 21-25, 27-29, 31-32, 34-36, 38, and 51-55 have been examined and are pending. Claims 17, 20, 26, 30, 33, 37, and 39-50 were previously canceled.
Priority
Priority claims as 371 of application PCT/US2023/081355 filed on 11/28/2023 is acknowledged.
Specification
The abstract is objected to because the text includes minor inconsistencies.
It appears that “The technology is generally directed identifying content responsive to a search query having a format corresponding to a determined query intent…” includes grammatical typos, and should read “The technology is generally directed to identifying content responsive to a search query and having a format corresponding to a determined query intent…”.
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Claims 1-16, 18-19, 21-25, 27-29, 31-32, 34-36, 38 and 51-55 are objected to because of the following informalities:
In independent claims 1, 14 and 27, it appears the limitation ‘identifying … based the intent index value, content responsive to the search query;’ includes a typo, and should instead read ‘identifying … based on the intent index value, content responsive to the search query;’. Dependent claims 2-13, 15-16, 18-19, 21-25, 28-29, 31-32, 34-36, 38, and 51-55 are also objected to for the above reasons.
In dependent claims 5, 18 and 31: it appears that the claim element ‘input into artificial intelligence ("AI") model’ within the limitation “…providing, by the one or more processors, the search query as input into artificial intelligence ("AI") model; and…” should read as ‘an artificial intelligence ("AI") model’ to make the scope clear. Dependent claims 6-7, 19, and 32 are also objected to for the above reasons.
In dependent claim 7, it appears the preamble ‘The method of claim 5-, ...’ includes a typo, and should read ‘The method of claim 5, …’.
In dependent claims 8, 21, and 34, the scope of the claim element ‘all formats’ within limitation ‘determining a ratio of a number of search queries for the first format to a total number of search queries for all formats.’ is unclear. The specification (in paras 02,24,117 and others) refers to formats and search filters interchangeably, and further indicates that format may include a combination of various formats. It isn’t clear what such a combination would entail. The limitation is not given patentable at this time given the indefinite scope. The applicant is requested to clarify and/or amend claim language as necessary to clarify scope.
In dependent claims 51, 53 and 55: the scope and meaning of the claim element ‘content in a respective format’ within the limitation “…identifying…based on the search query, a discrete cluster of a plurality of clusters, wherein each cluster of the plurality of clusters includes content in a respective format, …” is unclear.
The examiner notes that the corresponding independent claims 1, 14 and 27 recite ‘content in a first format, the identified content in a second format
corresponding to the first format’, but that doesn’t help clarify how the claim element ‘a respective format’ in the dependent claims is related to the first or second format. Examiner requests applicant to clarify the scope of the claim elements, and/or amend the claim language as needed. Dependent claims 2-3, 10-13, 15-16, 23-25, 28-29, 36-38, 52, and 54 are also objected to for the above reasons.
In dependent claims 2, 14 and 28: the meaning of claim element ‘discrete cluster having one or more historical search queries’, and antecedent basis for claim element ‘the at least one term’ within the limitation “…identifying the discrete cluster having one or more historical search queries including the at least one term…” is not apparent. The examiner suggests amending the claim language as needed to clarify scope. Dependent claims 2-3, 10-13, 15-16, 23-25, 28-29, 36-38, 52, and 54 are also objected to for the above reasons.
In dependent claim 10, 23 and 36: the antecedent basis for claim element ‘the respective cluster’ within the limitation “…providing as input…the respective cluster to an artificial intelligence (AI) model…” is unclear, as the corresponding parent claims 2, 14 and 28 describe ‘the discrete cluster’ but do not describe ‘respective cluster’. The examiner suggests amending the claim language as needed to clarify scope. Dependent claims 11-13, 24-25, and 38 are also objected to for the above reasons.
Appropriate correction is required.
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 1-16, 18-19, 21-25, 27-29, 31-32, 34-36, 38 and 51-55 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding independent claims 1, 14 and 27,
Step 2A, Prong 1: The claims are directed to an abstract idea.
The limitations of “determining …an intent index value, wherein the intent index value provides an indication that the search query is for content in a first format; identifying …based the intent index value, content responsive to the search query; and providing for output …the identified content in a second format corresponding to the first format. ”, as drafted, is a process that under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “processors”, nothing in the claim elements precludes the steps from practically being performed in the human mind. For example, but for the “processors” language, the claim encompasses the user thinking about a value representing a search query’s intention to be a first type of data, and identifying the corresponding data based on that value. Furthermore, but for the “processors” language, the claim encompasses the user thinking about representing the data in a format corresponding to the first type for output.
Thus, the claims recite abstract ideas of a mental process and are not patent eligible.
Step 2A, Prong 2: This judicial exception is not integrated into a practical application. The claim recites the additional element of: receiving…search query. The receiving step is recited at a high level of generality and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The combination of this additional element is no more than mere instructions to apply the exception using a generic computer component (“computer”). Accordingly, even in combination, i.e., considering the additional element(s) as an ordered combination with the claims as a whole and the abstract idea recited, the additional element(s) do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, even in combination, i.e., considering the additional element(s) as an ordered combination with the claims as a whole and the abstract idea recited. As discussed above with respect to integration of the abstract idea into a practical application, the additional element in the claim: receiving…search query, is recited at a high level of generality and amount to mere data gathering, which is a form of insignificant extra-solution activity, and being computer-implemented, amount to no more than mere instructions to apply the exception using a generic computer component. The specification does not provide any indication that such data collection or manipulation is performed by anything other than a generic, off-the-shelf computer component, and the Symantec, TLI, and OIP Techs. court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the receiving step is well-understood, routine, conventional activity is supported under Berkheimer memo. Thus, the claims are not patent eligible.
Regarding dependent claims 2-13, 15-16, 18-19, 21-25, 28-29, 31-32, 34-36, 38, and 51-55,
Claims 4-5, 8-9, and 51-52 are dependent on claim 1 and include all the limitations of claim 1. Similarly, claims 18, 21-22 and 53-54 are dependent on claim 14 and include all the limitations of claim 14. Similarly, claims 31, 34-35, and 55 are dependent on claim 27 and include all the limitations of claim 27. Therefore, claims 4-5, 8-9, 18, 21-22, 31, 34-35, and 51-55 also recite the same abstract ideas of a mental process. Claims 4-5, 8, 18, 21, 31, 34, and 51-55 further recite additional limitations which simply elaborate in the abstract ideas of a mental process, and therefore, does not amount to significantly more than the abstract idea. Claims 9, 22, and 35 further recite additional limitations regarding different types of formats, which is directed to selecting a particular data source or type of data to be manipulated, and considered to be an extra-solution activity that does not meaningfully limit the claim. Further, this additional limitation amounts to no more than mere instructions to apply the exception using a generic computer component. The specification does not provide any indication that such data collection or manipulation is performed by anything other than a generic, off-the-shelf computer component, and the Symantec, TLI, and OIP Techs. Court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the selecting a data type step is well-understood, routine, conventional activity is supported under Berkheimer memo. Thus, the claims are not patent eligible.
Claims 2-3 are dependent on claim 51 and include all the limitations of claims 51 and 1. Therefore, claims 2-3 also recite the same abstract ideas of a mental process. Claims 2-3 further recite additional limitations which simply elaborates in the abstract ideas of a mental process, and therefore, does not amount to significantly more than the abstract idea. Thus, the claims are not patent eligible.
Claims 6-7 are dependent on claim 5, and include all the limitations of claims 5 and 1. Therefore, claims 6-7 also recite the same abstract ideas of a mental process. Claims 6-7 further recite additional limitations which simply elaborates in the abstract ideas of a mental process, and therefore, does not amount to significantly more than the abstract idea. Thus, the claims are not patent eligible.
Claim 10 is dependent on claim 2, and include all the limitations of claims 2, 51, and 1. Claims 11-12 are dependent on claim 10, and include all the limitations of claims 10, 2, 51, and 1. Therefore, claims 10, and 11-12 also recite the same abstract ideas of a mental process. Claims 10, and 11-12 further recite additional limitations which simply elaborates in the abstract ideas of a mental process, and therefore, does not amount to significantly more than the abstract idea. Thus, the claims are not patent eligible.
Claim 13 is dependent on claim 12, and include all the limitations of claims 12, 10, 2, 51, and 1. Therefore, claim 13 also recites the same abstract ideas of a mental process. Claim 13 further recites additional limitations which simply elaborates in the abstract ideas of a mental process, and therefore, does not amount to significantly more than the abstract idea. Thus, the claims are not patent eligible.
Claims 15-16 are dependent on claim 53 and include all the limitations of claims 53 and 14. Therefore, claims 15-16 also recite the same abstract ideas of a mental process. Claims 15-16 further recite additional limitations which simply elaborates in the abstract ideas of a mental process, and therefore, does not amount to significantly more than the abstract idea. Thus, the claims are not patent eligible.
Claims 19 is dependent on claim 18 and include all the limitations of claim 18 and 14. Therefore, claim 19 also recites the same abstract ideas of a mental process. Claim 19 further recites additional limitations which simply elaborates in the abstract ideas of a mental process, and therefore, does not amount to significantly more than the abstract idea. Thus, the claims are not patent eligible.
Claim 23 is dependent on claim 53, and include all the limitations of claims 53, and 14. Claim 24 is dependent on claim 23, and include all the limitations of claims 23, 53, and 14. Therefore, claims 23-24 also recite the same abstract ideas of a mental process. Claims 23-24 further recite additional limitations which simply elaborates in the abstract ideas of a mental process, and therefore, does not amount to significantly more than the abstract idea. Thus, the claims are not patent eligible.
Claim 25 is dependent on claim 23, and include all the limitations of claims 23, 53, and 14. Therefore, claim 25 also recites the same abstract ideas of a mental process. Claim 25 further recites additional limitations which simply elaborates in the abstract ideas of a mental process, and therefore, does not amount to significantly more than the abstract idea. Thus, the claims are not patent eligible.
Claims 28-29 are dependent on claim 55 and include all the limitations of claims 55 and 27. Therefore, claims 28-29 also recite the same abstract ideas of a mental process. Claims 28-29 further recite additional limitations which simply elaborates in the abstract ideas of a mental process, and therefore, does not amount to significantly more than the abstract idea. Thus, the claims are not patent eligible.
Claim 32 is dependent on claim 31 and include all the limitations of claim 31 and 27. Therefore, claim 32 also recites the same abstract ideas of a mental process. Claim 32 further recites additional limitations which simply elaborates in the abstract ideas of a mental process, and therefore, does not amount to significantly more than the abstract idea. Thus, the claims are not patent eligible.
Claim 36 is dependent on claim 29, and include all the limitations of claims 29, 55, and 27. Therefore, claim 36 also recites the same abstract ideas of a mental process. Claims 10, 11-12, 23-24, and 36 further recite additional limitations which simply elaborates in the abstract ideas of a mental process, and therefore, does not amount to significantly more than the abstract idea. Thus, the claims are not patent eligible.
Claim 38 is dependent on claim 36, and include all the limitations of claims 36, 29, 55, and 27. Therefore, claim 38 also recites the same abstract ideas of a mental process. Claim 38 further recites additional limitations which simply elaborates in the abstract ideas of a mental process, and therefore, does not amount to significantly more than the abstract idea. Thus, the claims are not patent eligible.
Claim Rejections - 35 USC § 102
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)(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.
(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, 4-6, 9, 14, 18-19, 22, 27, 31-32, 35, 52 and 54 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Batraski (US 2012/0246165 A1).
Regarding claim 1,
Batraski teaches A method, comprising: receiving, by one or more processors, a search query; determining, by the one or more processors, an intent index value, wherein the intent index value provides an indication that the search query is for content in a first format; *see FIGS.1A-10, paras35-38(“ FIG. 2…FIG. 3…110 receives, from the user computer, a portion of a search query 130...search suggestion module 305 determines search suggestions 140 that are related to the portion of the search query 130 and/or the location of the user, and/or the intent of the user determined by personal preferences saved in the user's search history or interests…determines the related search suggestions 140 based on the user's previous search history…if a user has searched for sports-related subject matter within a predetermined time period…, and the portion of the search query 130 is “ko”, the search suggestion module 305 determines, in one embodiment, that the search suggestions 140 should relate to “Kobe Bryant” and not “kobe beef” [teaches determining intent index value, under broadest reasonable interpretation of claim elements (BRI)]…search suggestion from the plurality of search suggestions 140 is selected...110 then transmits the selected search suggestion 310 to a rich content module…rich content module 320 determines rich content associated with the search suggestion 310. For example, the rich content can include a photograph of Kobe Bryant when the search suggestion 310 is “Kobe Bryant” [intent index value provides indication that search query is for content in first format, under BRI]...server computer 110 then transmits the rich content 150 to the user computer 105 for display…”), para40(“FIG. 4…110 attempts to detect user intent from the portion of the search query as the user is typing and transmits one or more search suggestions to the user computer for display…then attempts to detect search suggestion intent [intent index value] from the selected search suggestion…110 receives rich content 150 from the rich content module…transmits the rich content to the user computer for display…”), paras41-48(“FIG. 5… search suggestion 310 and the portion of the search query are transmitted to a query classification module 505…505 classifies the search suggestion 310 into categories [also intent index value, under BRI], such as “Movie”, “Music Artist”,… [0046] In order to provide such content for these kinds of queries, the system 500 can mine the search engine's query logs (via data mining module 560) and generate query/query relationship or query/search result relationship in many ways…in one embodiment data is organized in a key/value pair fashion, where the key is the query itself and the value is the rich content to provide for the query. This database can be served with a key/value lookup service 570 [intent index]…”), paras52-53, paras28-33(“…response to receiving the portion of the search query 130, the server computer 110 generates and transmits one or more search suggestions 140 to the user computer 105 for display…if a user types “kob” into the search query entry area 125, the search suggestions displayed by the user computer 105 may be “Kobe Bryant” and “kobe beef”…110 also transmits rich content 150 to the user… FIG. 1B, the rich content 150 is structured information related to a search suggestion ...if the mouse hovers over the search suggestion 140 “Kobe Bryant”, then the rich content 150 may include a picture of Kobe Bryant, his position, his team…rich content 150 can include text, one or more graphics, one or more web links, one or more icons, one or more pictures, one or more videos…audio tracks [determining intent index value…indication query is for content in first format, under BRI]…”)
identifying, by the one or more processors based the intent index value, content responsive to the search query; and providing for output, by the one or more processors, the identified content in a second format corresponding to the first format. *see paras28-33,38, paras40-48(“FIG. 5… search suggestion 310 and the portion of the search query are transmitted to a query classification module 505…505 classifies the search suggestion 310 into categories [intent index value, under BRI], such as “Movie”, “Music Artist”,…Based on the suggestion category (or categories), different data sources are needed to fetch the raw data associated with this suggestion/category combination 510. For example, for “Chicago/movie”, movie data, such as year, rating, director, casting, etc. are needed [identifying based on intent index value, content responsive to search query, under BRI], but for “Chicago/city”, the city's points of interest, current events, and weather report are needed…data federation module 515 determines the importance of multiple data sources and fetches the data from corresponding sources …data federation module 515 can select a single category using a fixed priority order, a query classification confidence level, the user's search session context, and/or the user's search history profile to determine what would be more interesting to the user. For example, if “Movie” is determined to be more important [intent index value] by the data federation module 515, then, based on a fixed priority order technique, the movie details of “Chicago” would be chosen [providing for output…identified content in second format corresponding to first format, under BRI]…rich content of the search suggestion can be obtained by using the vertical search engines or web services…rich content can also be fetched/scraped from the web directly. For example, movie data can be fetched from the Internet Movie Database (IMDb) website automatically…in one embodiment a predefined list of queries that can trigger rich content of the corresponding category can be used, and then the vertical web services can be scraped with these queries in an offline fashion…scraped content is then served with a key/value lookup server…in one embodiment data is organized in a key/value pair fashion, where the key is the query itself and the value is the rich content to provide for the query. This database can be served with a key/value lookup service 570 [intent index]…after the federation module 515 finishes fetching data from the web services, and determines which data is to be used, the data federation module 515 sends the data to the presentation rendering module 580, which transmits the rich content to the user's browser for viewing in the search suggestion region…rich content can be based on templates… each category can have its own template, and the server computer 110 can fill in the template with the rich content obtained from the data federation module …template can be dynamically changing, to allow more relevant and dynamic presentation layout for better user engagement” teaches providing output…identified content in second format corresponding to first format, under BRI), paras52-53
Regarding claim 4,
Batraski teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Batraski further teaches The method of claim 1, wherein the content includes at least one digital component. *see paras28-33(“…rich content 150 is information that is presented next to…search suggestions 140 in the search suggestion region 145. The rich content 150 can include text, one or more graphics, one or more web links, one or more icons, one or more pictures, one or more videos (e.g., a video is played when the user clicks on a file or link associated with the video), one or more audio tracks (e.g., a song is played when the user clicks on a file or link associated with the song), one or more answers to a question, etc.” teaches content includes at least one digital component, under broadest reasonable interpretation of claim elements (BRI)), paras38-48(“ FIG. 5 …search suggestion 310 and the portion of the search query are transmitted to a query classification module 505…505 classifies the search suggestion 310 into categories, such as “Movie”, “Music Artist”,…Based on the suggestion category (or categories), different data sources are needed to fetch the raw data associated with this suggestion/ category combination 510. For example, for “Chicago/movie”, movie data, such as year, rating, director, casting, etc. are needed [content includes digital component, under BRI], but for “Chicago/city”, the city's points of interest, current events, and weather report are needed…if “Movie” is determined to be more important by the data federation module 515, then, based on a fixed priority order technique, the movie details of “Chicago” would be chosen…rich content of the search suggestion can be obtained by using the vertical search engines or web services…rich content can also be fetched/scraped from the web directly. For example, movie data can be fetched from the Internet Movie Database (IMDb) website automatically…a predefined list of queries that can trigger rich content of the corresponding category can be used, and then the vertical web services can be scraped with these queries in an offline fashion…scraped content is then served with a key/value lookup server…in one embodiment data is organized in a key/value pair fashion, where the key is the query itself and the value is the rich content to provide for the query. This database can be served with a key/value lookup service…after the federation module 515 finishes fetching data from the web services, and determines which data is to be used, the data federation module 515 sends the data to the presentation rendering module 580, which transmits the rich content to the user's browser for viewing in the search suggestion region 145…rich content can be based on templates…each category can have its own template, and the server computer 110 can fill in the template with the rich content obtained from the data federation module…template can be dynamically changing, to allow more relevant and dynamic presentation layout for better user engagement” teaches content includes at least one digital component, under BRI), paras52-53
Regarding claim 5,
Batraski teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Batraski further teaches The method of claim 1, wherein when determining the intent index value, the method further comprises: providing, by the one or more processors, the search query as input into artificial intelligence ("AI") model; and determining, by the one or more processors executing the AI model, the intent index value. *see paras49-51(“FIG. 6…user types in a portion of a search query…105 transmits this portion 130 to a front end (FE) 605…605 transmits the portion of the search query 130 to query suggestion databases 610…610 return to the FE 605 search suggestions 140 related to the portion of the query 130 and metadata 615 associated with the search suggestions 140. The metadata 615 may include, as described above, the category or categories associated with each search suggestion, the frequency of search suggestions… geographic metadata of the suggestion (e.g., Pizza Hut Florham Park has a city name), geographic distribution, etc. The FE 605 transmits one or more selected search suggestion 310 of the search suggestions 140 and the metadata 620 associated with the one or more selected search suggestion 310 to [determining… executing AI model…intent index value, under its broadest reasonable interpretation; 610 is understood to apply some kind of AI model to determine suggestions/intent index value, under BRI] a near real-time query intelligence (NRTI) module 625…625 performs content federation…data stored in this database 640 (…) or cloud system is aggregated (arrow 645) and published as rich content 150 to the NRTI…query/suggestion feedback 665 is transmitted to the query suggestion databases…science modeling (arrow 655) is also performed on the data stored in the database 640. Science modeling can include query classification, frequency analysis, trending detection, user interest analysis or detection, geographic aggregation [655/modeling also understood to apply some kind of AI model, under BRI]…FIG. 7…700 used to determine search suggestions… 605 transmits the portion of the search query 130 to a query suggestion meta service 705...705 transmits data feedback 735 to a user database 740, and the user database 740 transmits aggregated data and results of science modeling (e.g., the user interest analysis and detection (e.g., 30 days ago, 7 days ago, right now, etc.), geographic interest, user group interest… and trending detection, etc.) [determining …executing AI model…intent index value, under BRI]…”), paras41-48, paras04-07(“… transmitting of the search suggestion to a rich content module further includes classifying, by the rich content module, the search suggestion into a category [determining… executing AI model…intent index value, under BRI]…classifying is based on a classification technique, such as fixed priority order, query classification confidence level, using search session context of the user, and using a search history profile of the user…search engine query logs are mined by classifying queries in the logs into one or more categories [determining…executing AI model…intent index value, under BRI]…a relationship is generated, such as a query to query relationship and/or a query to search result relationship....”)
Regarding claim 6,
Batraski teaches all the claimed limitations as set forth in the rejection of claim 5 above.
Batraski further teaches The method of claim 5, further comprising training the AI model, wherein training the AI model comprises: associating labels with search queries, wherein the labels indicate a search filter associated with the search queries; and providing, as training data, one or more query level features, the query level features comprising one or more of historical properties of the search queries, embeddings associated with the search queries, query string features, geographical features associated with the search queries, or language of the search queries. *see paras04-07(“… transmitting of the search suggestion to a rich content module further includes classifying, by the rich content module, the search suggestion into a category… classifying is based on a classification technique, such as fixed priority order, query classification confidence level, using search session context of the user, and using a search history profile of the user…search engine query logs are mined by classifying queries in the logs into one or more categories [training AI model…associating labels with queries…labels indicate search filter associated with queries, under BRI]…relationship is generated, such as a query to query relationship and/or a query to search result relationship [training data]...”),paras35-38(“FIG. 2…110 receives… portion of a search query...user's geographic information can be used and is passed to the server computer 110…110 transmits the portion of the search query 130 to a search suggestion module 305…305 determines search suggestions 140 that are related to the portion of the search query 130 and/or the location of the user, and/or the intent of the user determined by personal preferences saved in the user's search history or interests [providing training data…query level features comprising historical properties… geographical features…, under their broadest reasonable interpretation (BRI)]…305 determines the related search suggestions 140 based on the user's previous search history. For example, if a user has searched for sports-related subject matter within a predetermined time period (e.g., within the last two days) [training AI model…associating labels with queries… labels indicate search filter associated with queries, under BRI], and the portion of the search query 130 is “ko”, the search suggestion module 305 determines, in one embodiment, that the search suggestions 140 should relate to “Kobe Bryant” and not “kobe beef”...personalization can occur over any period of time, can occur for one or more users, and can be an option that the user activates...”), paras49-51 (“FIG. 6…user types in a portion of a search query…105 transmits this portion 130 to a front end (FE) 605…605 transmits the portion of the search query 130 to query suggestion databases 610…610 return to the FE 605 search suggestions 140 related to the portion of the query 130 and metadata 615 associated with the search suggestions 140. The metadata 615 may include, as described above, the category or categories associated with each search suggestion, the frequency of search suggestions… geographic metadata of the suggestion (e.g., Pizza Hut Florham Park has a city name), geographic distribution, etc. The FE 605 transmits one or more selected search suggestion 310 of the search suggestions 140 and the metadata 620 associated with the one or more selected search suggestion 310 to a near real-time query intelligence (NRTI) module 625…625 performs content federation…data stored in this database… or cloud system is aggregated (arrow 645) and published as rich content 150 to the NRTI…query/suggestion feedback 665 is transmitted to the query suggestion databases…science modeling (arrow 655) is also performed on the data stored in the database 640. Science modeling can include query classification, frequency analysis, trending detection, user interest analysis or detection, geographic aggregation [training AI model…associating labels with queries…labels indicate search filter associated with queries …providing training data…historical properties…geographical features…, under BRI]…FIG. 7…700 used to determine search suggestions… 605 transmits the portion of the search query 130 to a query suggestion meta service 705...705 transmits data feedback 735 to a user database 740, and the user database 740 transmits aggregated data and results of science modeling (e.g., the user interest analysis and detection (e.g., 30 days ago, 7 days ago, right now, etc.), geographic interest, user group interest… and trending detection, etc.) [training AI model…associating labels with queries…labels indicate search filter… training data…historical properties …geographical features…, under BRI]…”), paras41-48
Regarding claim 9,
Batraski teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Batraski further teaches The method of claim 1, wherein: the first format includes one or more of image, video, text, or audio, and the second format includes one or more of image, video, text, or audio. *see para30(“…rich content 150 is information that is presented next to…the search suggestions 140 in the search suggestion region 145. The rich content 150 can include text, one or more graphics, one or more web links, one or more icons, one or more pictures, one or more videos (e.g., a video is played when the user clicks on a file or link associated with the video), one or more audio tracks (e.g., a song is played when the user clicks on a file or link associated with the song), one or more answers to a question, etc.”), paras40-43(“FIG. 5…search suggestion 310 and the portion of the search query are transmitted to a query classification module 505…505 classifies the search suggestion 310 into categories, such as “Movie”, “Music Artist”,… “Weather forecast” or “Travel Destination”. Some search suggestions 310 can be classified into a single category, while others may belong to multiple categories (e.g., the query “Chicago” is both a movie name and a city name). Based on the suggestion category (or categories), different data sources are needed to fetch the raw data associated with this suggestion/ category combination 510. For example, for “Chicago/movie”, movie data, such as year, rating, director, casting, etc. are needed [format includes video, text, audio, under BRI], but for “Chicago/city”, the city's points of interest, current events, and weather report are needed…if “Movie” is determined to be more important by the data federation module 515, then, based on a fixed priority order technique, the movie details of “Chicago” would be chosen… rich content of the search suggestion can be obtained by using the vertical search engines or web services…rich content can also be fetched/scraped from the web directly. For example, movie data can be fetched from the Internet Movie Database (IMDb) website automatically…”), paras47-48(“...after the federation module 515 finishes fetching data from the web services, and determines which data is to be used, the data federation module 515 sends the data to the presentation rendering module 580, which transmits the rich content to the user's browser for viewing in the search suggestion region 145…rich content can be based on templates. In one embodiment, each category can have its own template, and the server computer 110 can fill in the template with the rich content obtained from the data federation module 515. In another embodiment, the template can be dynamically changing, to allow more relevant and dynamic presentation layout for better user engagement.”)
Regarding claim 14,
Claim 14 recites substantially the same claim limitations as claim 1, and is rejected for the same reasons.
Regarding claim 18,
Claim 18 recites substantially the same claim limitations as claim 5, and is rejected for the same reasons.
Regarding claim 19,
Claim 19 recites substantially the same claim limitations as claim 6, and is rejected for the same reasons.
Regarding claim 22,
Claim 22 recites substantially the same claim limitations as claim 9, and is rejected for the same reasons.
Regarding claim 27,
Claim 27 recites substantially the same claim limitations as claim 1, and is rejected for the same reasons.
Regarding claim 31,
Claim 31 recites substantially the same claim limitations as claim 5 and is rejected for the same reasons.
Regarding claim 32,
Claim 32 recites substantially the same claim limitations as claim 6, and is rejected for the same reasons.
Regarding claim 35,
Claim 35 recites substantially the same claim limitations as claim 9, and is rejected for the same reasons.
Regarding claim 52,
Batraski teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Batraski further teaches The method of claim 1, further comprising: providing as input, by the one or more processors, the intent index value to an artificial intelligence (AI) model; and determining, by one or more processors executing the AI model, the second format of the content, wherein the second format of the content includes one or more of a size and format of the content. *see paras04-07(“…transmitting of the search suggestion to a rich content module further includes classifying, by the rich content module, the search suggestion into a category…classifying is based on a classification technique, such as fixed priority order, query classification confidence level, using search session context of the user, and using a search history profile of the user…search engine query logs are mined by classifying queries in the logs into one or more categories [providing intent index value to AI model…determine second format, under its BRI]… relationship is generated, such as a query to query relationship and/or a query to search result relationship...” ), paras35-38(“FIG. 2…110 receives…portion of a search query... user's geographic information can be used and is passed to the server computer 110…110 transmits the portion of the search query 130 to a search suggestion module 305…305 determines search suggestions 140 that are related to the portion of the search query 130 and/or the location of the user, and/or the intent of the user determined by personal preferences saved in the user's search history or interests… 305 determines the related search suggestions 140 based on the user's previous search history. For example, if a user has searched for sports-related subject matter within a predetermined time period (e.g., within the last two days), and the portion of the search query 130 is “ko”, the search suggestion module 305 determines, in one embodiment, that the search suggestions 140 should relate to “Kobe Bryant” and not “kobe beef” [determining…second format…includes format of content, under its BRI]...personalization can occur [providing intent index value to AI model, under its BRI] over any period of time, can occur for one or more users, and can be an option that the user activates...”), paras49-51 (“FIG. 6…user types in a portion of a search query…105 transmits this portion 130 to a front end (FE) 605…605 transmits the portion of the search query 130 to query suggestion databases 610…610 return to the FE 605 search suggestions 140 related to the portion of the query 130 and metadata 615 associated with the search suggestions 140. The metadata 615 may include, as described above, the category or categories associated with each search suggestion, the frequency of search suggestions… geographic metadata of the suggestion (e.g., Pizza Hut Florham Park has a city name), geographic distribution, etc. The FE 605 transmits one or more selected search suggestion 310 of the search suggestions 140 and the metadata 620 associated with the one or more selected search suggestion 310 to a near real-time query intelligence (NRTI) module 625…625 performs content federation…data stored in this database… or cloud system is aggregated (arrow 645) and published as rich content 150 to the NRTI…query/suggestion feedback 665 is transmitted to the query suggestion databases…science modeling (arrow 655) is also performed on the data stored in the database 640. Science modeling can include query classification, frequency analysis, trending detection, user interest analysis or detection, geographic aggregation [providing intent index value to AI model… determine second format, under its BRI]…FIG. 7…700 used to determine search suggestions… 605 transmits the portion of the search query 130 to a query suggestion meta service 705...705 transmits data feedback 735 to a user database 740, and the user database 740 transmits aggregated data and results of science modeling (e.g., the user interest analysis and detection (e.g., 30 days ago, 7 days ago, right now, etc.), geographic interest, user group interest… and trending detection, etc.) [providing intent index value to AI model… determine second format, under its BRI]…”), paras41-48
Regarding claim 54,
Claim 54 recites substantially the same claim limitations as claim 52, and is rejected for the same reasons.
Claim Rejections - 35 USC § 103
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 7-8, 21, and 34 are rejected under 35 U.S.C. 103 as being unpatentable over Batraski in view of DeLuca (US 2018/0095964 A1).
Regarding claim 7,
Batraski teaches all the claimed limitations as set forth in the rejection of claim 5 above.
However, Batraski doesn’t expressly teach ‘The method of claim 5-, wherein the AI model is a language model (LM) or a large language model (LLM).’
DeLuca teaches …wherein the AI model is a language model (LM) or a large language model (LLM). *see paras15-16(“... providing search results to a search query and, more particularly, to providing search results based on natural language classification and confidence information…natural language classification (NLC) techniques may be used to determine possible intended subjects of a search query, determine confidence levels associated with each intended subject, and provide search results based on the confidence levels…NLC server may receive a search query (e.g., from a user) for “How to replace a flat tire,” and implement NLC techniques to determine confidence scores identifying a likelihood that the search query related to particular subjects or intentions…NLC server may determine an example confidence of 75% that the search query related to a “content” search, and a confidence of 25% that the search query related to a “product” search…search results may be filtered such that a ratio of the search results provided to the user corresponds to the confidence levels returned by the NLC server…the ratio of the search results may match the confidence levels. Continuing with the previous example, the search results may be filtered such that 75% of the search results returned to the user relate to “content” results (e.g., articles relating to tire repair), and 25% of the search results relate to “product” results (e.g., tire products available for purchase)…the ratio of the search results may substantially match the confidence levels…search results may be ranked based on the confidence scores… Advantageously, search results provided to the user may be more accurate since the subject or intention of the search is considered when providing the search results. That is, search results may be produced with consideration to the intention behind a search query” teaches applying language model (LM), under its broadest reasonable interpretation]), paras62-63,77-79,83-86
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Batraski to incorporate the teachings of DeLuca and enable Batraski to apply an AI model comprising a language model, as doing so would enable providing search results to a search query based on analyzing the intention behind the query through natural language classification (DeLuca, paras15-16).
Regarding claim 8,
Batraski teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Batraski does not expressly teach ‘The method of claim 1, wherein determining the intent index value comprises determining a ratio of a number of search queries for the first format to a total number of search queries for all formats.’
However, DeLuca teaches …wherein determining the intent index value comprises determining a ratio of a number of search queries for the first format to a total number of search queries for all formats. *see paras15-16(“... providing search results to a search query and, more particularly, to providing search results based on natural language classification and confidence information…natural language classification (NLC) techniques may be used to determine possible intended subjects of a search query, determine confidence levels associated with each intended subject, and provide search results based on the confidence levels… NLC server may receive a search query (e.g., from a user) for “How to replace a flat tire,” and implement NLC techniques to determine confidence scores identifying a likelihood that the search query related to particular subjects or intentions…NLC server may determine an example confidence of 75% that the search query related to a “content” search, and a confidence of 25% that the search query related to a “product” search [teaches determining ratio of queries for first format to total number of queries… in order to determine intent index value, under broadest reasonable interpretation of elements, and in view of outstanding objections; Examiner notes that claim doesn’t specify if search queries correspond to historical queries, so its scope is interpreted broadly]…search results may be filtered such that a ratio of the search results provided to the user corresponds to the confidence levels returned by the NLC server…ratio of the search results may match the confidence levels. Continuing with the previous example, the search results may be filtered such that 75% of the search results returned to the user relate to “content” results (e.g., articles relating to tire repair), and 25% of the search results relate to “product” results (e.g., tire products available for purchase)…ratio of the search results may substantially match the confidence levels…search results may be ranked based on the confidence scores (e.g., search results with classifications of relatively higher confidence may be ranked higher than search results with classifications of relatively lower confidence)…search results provided to the user may be more accurate since the subject or intention of the search is considered when providing the search results. That is, search results may be produced with consideration to the intention behind a search query”), paras62-63,77-79,83-86
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Batraski to incorporate the teachings of DeLuca and enable Batraski to determine ratio of search queries for different formats in order to determine intent index value, as doing so would enable providing search results to a search query based on analyzing the intention behind the query through natural language classification (DeLuca, paras15-16).
Regarding claim 21,
Claim 21 recites substantially the same claim limitations as claim 8, and is rejected for the same reasons.
Regarding claim 34,
Claim 34 recites substantially the same claim limitations as claim 8, and is rejected for the same reasons.
Conclusion
The prior art made of record in PTO-892 and not relied upon is considered pertinent to applicant's disclosure.
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/A.K./Examiner, Art Unit 2165
/ALEKSANDR KERZHNER/Supervisory Patent Examiner, Art Unit 2165
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