Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-12 are presented for examination.
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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-12 are within the four statutory categories. Claims 1-6 are directed to a system, and claims 7-12 are directed to a method, which are within the statutory categories of invention. (STEP 1: YES).
Prong 1 of Step 2A
Claim 1, which is representative of the inventive concept, recites:
a processor coupled to a network, the processor coupled to a non- transitory memory, the processor to execute instructions stored in the non- transitory memory, the non-transitory memory comprising:
an acquisition system comprising:
instructions stored in the non-transitory memory, the instructions, when executed by the processor, cause the processor to:
access information from data sources on the network;
perform a data transformation on the accessed information;
output one or more data objects to a database, the data objects based on the data transformation, the data objects comprising data transformation data objects based on characteristics of businesses and characteristics of service providers and preference data objects based on at least one of buyer, seller and service provider preferences;
a matching system comprising:
instructions stored in the non-transitory memory, the instructions, when executed by the processor, cause the processor to:
access the one or more data objects stored in the database;
execute a machine learning matching algorithm based on the data objects accessed from the database, the machine learning matching algorithm to create an output based on matching data transformation objects and preference data objects;
generate a match report based on the output of the machine learning matching algorithm, the match report comprising matches of at least one user with at least one other user,
a feedback system comprising:
instructions stored in the non-transitory memory, the instructions, when executed by the processor, cause the processor to:
extract a set of feedback characteristics from an unstructured text input and from user actions;
modify at least one of the acquisition system, the matching system and the machine learning matching algorithm based on the feedback characteristics.
The underlined limitations as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. That is, other than reciting a system implemented by a data processor (computer), database, and machine learning matching algorithm, the claimed invention amounts to commercial interactions by matching buyer, seller and service providers together based on characteristics of each.
The Examiner notes that certain “method[s] of organizing human activity” includes a person' s interaction with a computer (see MPEP 2106.04(a)(2)(II))]. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Any limitations not identified above as part of the abstract idea are deemed to be “additional elements” and will be discussed in further detail below.
The abstract idea for Claim 7 is identical as the abstract idea for Claim 1, because the only difference between Claims 7 and 1 is that Claim 7 recites a system whereas Claim 1 recites a method, with claim 7 being narrowing in scope (due to not reciting any functionality related to the feedback system of claim 1).
Claims 2-6 and 8-12 further narrow the abstract idea described in independent claims 1 and 7 by reciting further details on the information accessed from data sources on the network (claims 2 and 8), characteristics of businesses (claims 3 and 9), characteristics of service providers (claims 4 and 10), and the contents of the match report (claims 5-6 and 11-12).
Prong 2 of Step 2A
Claim 1 is not integrated into a practical application because the additional elements (i.e. the non-underlined, bolded limitations above – in this case, a processor coupled to a network and to a non-transitory memory that executes instructions stored in the non-transitory memory, a database, data transformation objects, an acquisition system, a matching system, a machine learning matching algorithm, and a feedback system, amount to no more than limitations which are recited at a high-level of generality such that they amount to no more than mere instructions to apply an exception using generic computer components – for example, the recitation of the processor, non-transitory memory, database, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see paragraphs 17-21, 24-27 of the present Specification, see MPEP 2106.05(f).
Dependent claims 2-6 do not recite any further additional elements, as they only further define the abstract idea set forth in independent claim 1.
Claim 7 is not integrated into a practical application because the additional elements - in this case, a network and data transformation objects, and a machine learning matching algorithm, amount to no more than limitations which are recited at a high-level of generality such that they amount to no more than mere instructions to apply an exception using generic computer components – for example, the recitation of the processor, non-transitory memory, database, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see paragraphs 17-21, 24-27 of the present Specification, see MPEP 2106.05(f).
Dependent claims 8-12 do not recite any further additional elements, as they only further define the abstract idea set forth in independent claim 7.
Accordingly, the claims as a whole do not integrate the abstract idea into a practical application as they do not impose any meaningful limits on practicing the abstract idea. Hence, claims 1-7 do not include additional elements that integrate the judicial exception into a practical application.
The processor is not described by the applicant and is recited at a high-level of generality (i.e., a generic processor performing generic computer processor functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Similarly, the non-transitory memory, the instructions stored thereon, database, network, data transformation objects, are not described by the applicant and is recited at a high-level of generality (i.e., generic computer hardware/software components performing generic computer hardware/software functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim further recites the additional element of using a trained machine learning matching algorithm (e.g., model) to create an output based on matching data transformation objects and preference data objects. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). Alternatively, or in addition, the implementation of the trained machine learning matching algorithm to create an output based on matching data transformation objects and preference data objects merely confines the use of the abstract idea (i.e., the trained algorithm) to a particular technological environment or field of use and thus fails to add an inventive concept to the claims.
Step 2B
Claims 1-7 do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception.
Representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements (using a processor coupled to a network and to a non-transitory memory that executes instructions stored in the non-transitory memory, a database, data transformation objects, an acquisition system, a matching system, a machine learning matching algorithm, and a feedback system) to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the machine learning matching algorithm (e.g., model) to create an output based on matching data transformation objects and preference data objects was found to represent mere instructions to implement the abstract idea on a generic computer and/or confine the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. See also Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 17 (Fed. Cir. April 18, 2025) (finding that applying machine learning to an abstract idea does not transform a claim into something significantly more).
Claim 1 also includes the additional element of a “data object” which generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) and MPEP 2106.05(A) indicate that merely “generally linking” the abstract idea to a particular technological environment or field of use cannot provide a practical application or significantly more. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. [or] MPEP2106.05(I)(A) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
Claim 7 does not recite any additional elements beyond those recited in claim 1.
Dependent claims 2-6 are similarly rejected because they either further define/narrow the abstract idea and do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
Claims 2-6 merely describe further narrow the abstract idea described in the independent claims by reciting further details on the information accessed from data sources on the network (claim 2), characteristics of businesses (claim 3), characteristics of service providers (claim 4), and the contents of the match report (claims 5-6).
Claims 2-6 do not present any further additional elements and thus cannot provide an inventive concept such that the claims are subject matter eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(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.
Claim(s) 1-12 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by Sahu et al. (US PGPub 2016/0112394), hereinafter referred to as Sahu.
As per claim 1, Sahu teaches a system for matching buyers, sellers and service providers, the system comprising:
a processor coupled to a network, the processor coupled to a non- transitory memory, the processor to execute instructions stored in the non- transitory memory (paragraph 41 - The interaction processing infrastructure 102 may include processing resources communicatively coupled to storage media, random access memory (RAM), read-only memory (ROM), and/or other types of memory; paragraph 80 - The mobile communication device 301 includes a memory 334 communicatively coupled to a processor 336 (e.g., a microprocessor) for processing the functions of the mobile communication device 301; paragraph 82 - The mobile communication device 301 can also include at least one computer-readable medium 346 coupled to the processor 336, which stores application programs and other computer code instructions for operating the device), the non-transitory memory comprising:
an acquisition system comprising:
instructions stored in the non-transitory memory, the instructions, when executed by the processor (paragraph 82 - The mobile communication device 301 can also include at least one computer-readable medium 346 coupled to the processor 336, which stores application programs and other computer code instructions for operating the device), cause the processor to:
access information from data sources on the network (paragraph 40 - The interaction processing infrastructure 102 may facilitate searching of one or more information repositories in response to data received over the one or more networks 108 from any one or combination of the interfaces; paragraph 42 - the interaction processing infrastructure 102 may be communicatively coupled or couplable to one or more data sources via one or more data acquisition interfaces 111. The one or more data sources may include any suitable source of data to facilitate embodiments disclosed further herein. In various embodiments, the one or more data sources may include one or more of a database, a website, any repository of data in any suitable form);
perform a data transformation on the accessed information (paragraph 40 - the interaction processing infrastructure 102 may include a set of devices configured to process, transform, encode, translate, send, receive, retrieve, detect, generate, compute, organize, categorize, qualify, store, display, present, handle, or use information and/or data suitable for the embodiments described herein);
output one or more data objects to a database, the data objects based on the data transformation (paragraph 43 - Certain data pulled and/or pushed from the one or more data sources may be transformed and the transformed data and/or other data generated based thereon may be made available by the interaction processing infrastructure 102 for users of client devices 205 and/or 207. In alternative embodiments, data from the one or more data sources may be made available directly to client devices 205 and/or 207) the data objects comprising data transformation data objects based on characteristics of businesses and characteristics of service providers and preference data objects based on at least one of buyer, seller and service provider preferences In some embodiments, (paragraph 123- the feed engine 239 may further transform the transformed data/content/information into a feed object with a data-interchange format that facilitates parsing. The feed engine 239 and/or the aggregation/transformation engine 231 may translate the data into understandable data, information, and/or content. The transformed data, information, and/or content may be directed to certain tables and/or data stores 268 based on the type of and/or an entity category to which the data, information, and/or content relates; paragraph 248 - In some embodiments, specific weights (also referred to as distributions) are associated with candidate providers and/or candidate categories generated by each module depending on a weight associated with a data source of that module and/or user behaviors, if any, associated with that module);
a matching system comprising:
instructions stored in the non-transitory memory, the instructions, when executed by the processor (paragraph 82 - The mobile communication device 301 can also include at least one computer-readable medium 346 coupled to the processor 336, which stores application programs and other computer code instructions for operating the device), cause the processor to:
access the one or more data objects stored in the database (paragraph 40 - The interaction processing infrastructure 102 may facilitate searching of one or more information repositories in response to data received over the one or more networks 108 from any one or combination of the interfaces; paragraph 42 - the interaction processing infrastructure 102 may be communicatively coupled or couplable to one or more data sources via one or more data acquisition interfaces 111. The one or more data sources may include any suitable source of data to facilitate embodiments disclosed further herein. In various embodiments, the one or more data sources may include one or more of a database, a website, any repository of data in any suitable form);
execute a machine learning matching algorithm based on the data objects accessed from the database, the machine learning matching algorithm to create an output based on matching data transformation objects and preference data objects (paragraph 244 - The recommendation service can be configured to provide recommendations on providers (e.g., business listings) and/or categories of providers that might be appealing to a user based on information gathered about the user including gender, demographics, the user's interests or preferences, the user's prior interactions with aspects of the interaction infrastructure discussed herein (e.g. user's prior searches, selections, clicks, etc.), user's geographic location, other context information such time of day, day of week, month, year, seasonality information (e.g., season of the year, major yearly events or holidays such as Valentine's Day, Thanksgiving Day, Christmas, etc.), and the like; paragraph 245 - Some of the data can be derived through data-mining, statistical analysis, machine learning, or other automated or semi-automated processes performed on other user data such as a user's search behavior, click-through behavior, and the like.);
generate a match report based on the output of the machine learning matching algorithm, the match report comprising matches of at least one user with at least one other user (paragraph 299 - #TopRecommendations; paragraph 306 - The baseScore parameter may indicate a base or default score (e.g., 15000) for the corresponding module and/or for a recommended item (e.g., provider or category) made by the module. The base score of a recommendation may be updated based on other parameters (e.g., the decayPerDay parameter, the boostedScoreType parameter) to obtain a final score. The final score may be compared with the scores of recommended items by other modules during a ranking process to determine the final recommendations; paragraph 328 - The recommendation modules can be configured to generate candidate recommendations based on the data provided by various data sources. In some embodiments, each of the recommendation modules corresponds to a particular factor or consideration in generating the recommendation. As such, each recommendation module may be configured to provide recommended providers and/or categories based primarily on a particular dataset or a particular combination of datasets. The recommendation modules can include user interactions module 1326, user interests module 1328, revisits module 1330, chains module 1332, also clicked module 1334, related categories module 1336, seasonality module 1338, day parting module 1340, and geo module 1342),
a feedback system comprising:
instructions stored in the non-transitory memory, the instructions, when executed by the processor (paragraph 82 - The mobile communication device 301 can also include at least one computer-readable medium 346 coupled to the processor 336, which stores application programs and other computer code instructions for operating the device), cause the processor to:
extract a set of feedback characteristics from an unstructured text input and from user actions (paragraph 137 - in some embodiments, the end user may explicitly specify the location of interest in a search request; and the location engine 244 extracts the location of interest from the search request);
modify at least one of the acquisition system, the matching system and the machine learning matching algorithm based on the feedback characteristics (paragraph 246 - some of these datasets can be provided as input to one or more recommendation modules implementing the recommendation service. Each of the recommendation modules can be configured to provide a set of candid providers (e.g., business listings) and/or candidate categories. The candidate providers and/or candidate categories from various recommendation modules can be blended or otherwise combined to generate the final providers and/or final categories to be recommended to users. A category refers to a type of a provider based on the service and/or product offered by that the provider. For instance, a category for Macy's may be “Department Store. In some embodiments, each of the recommendation modules corresponds to a particular factor or consideration in generating the recommendation. As such, each recommendation module may be configured to provide recommended providers and/or categories based primarily on a particular dataset or a particular combination of datasets. For example, a geo recommendation module may be configured to generate recommendations based primarily on the geographic location of a user. To this end, the geo recommendation module may utilize user specific data (e.g., user searches, clicks) and/or non-user specific data (e.g., provider popularity or rating, transaction volume ranking) that has been correlated with or associated with geographic locations. A seasonality module may be configured to generate recommendations based primarily on the current seasonality. The seasonality recommendation module may utilize user-specific and/or non-user specific data that has been correlated with or associated with seasonality information. For instance, the seasonality data may indicate that certain providers or categories of providers are more popular among users (e.g., more frequently searched and/or clicked) or generate higher transaction volumes than other providers or categories of providers during certain time of the year. As an example, jewelers, florists, and sit-down restaurants may be more popular near or on Valentine's Day and Mother's Day. As another example, electronics and toys may be more popular near or on Christmas and New Year.).
As per claim 2, Sahu teaches the system as claimed in claim 1, wherein the information accessed from data sources on the network comprises structured information, unstructured information and semi-structured information (paragraph 44 - Data, as referenced herein, may correspond to any one or combination of raw data, unstructured data, structured data, information, and/or content which may include media content, text, documents, files, instructions, code, executable files, images, video, audio, and/or any other suitable content suitable for embodiments of the present disclosure.).
Claim 8 recites limitations substantially similar to those of claim 2 above; thus, the same rejection applies.
As per claim 3, Sahu teaches the system as claimed in claim 1, wherein the characteristics of businesses comprise geographic information, product information, personnel information, and financial information (paragraph 131 - The one or more data repositories 268 may include provider information 249 about commercial entities or public end-user information, or other types of searchable end-user information. The one or more provider information repositories 249 may retain any local provider information (e.g., listings of provider information) suitable for embodiments of this disclosure, such as entity, product, and service information; paragraph 132 - Provider information 249 may have street addresses or other location parameters, such as longitude and latitude coordinates, stored as locations in one or more location information repositories 251. The provider information 249 may include addresses, telephone numbers, descriptive content, notifications, and/or end-user information, etc. Provider information 249 may be associated with locations 251. The locations 251 may be part of the provider information 249, or associated with the provider information 249. In some embodiments, the provider information 249 may include information related to entity entities at corresponding locations 251. The entities may be entities or people.).
Claim 9 recites limitations substantially similar to those of claim 3 above; thus, the same rejection applies.
As per claim 4, Sahu teaches the system as claimed in claim 1, wherein the characteristics of service providers comprise recent transactions, areas of expertise, rates, and geographic information (paragraph 172 - the one or more provider information repositories 249 may retain provider information of particular providers. The repositories 249 may retain any information related to providers, including entities and people, which may have street addresses or other location parameters, such as longitude and latitude coordinates, maps, driving directions, and/or the like, stored as locations in one or more location information repositories 251. For example, one or more provider information repositories 249 may retain any information related to provider identification information, provider profiles, provider certification information, entity description, product descriptions, service descriptions, ratings/reviews/comments/preference indicia associated with providers, provider websites, provider authentication information, provider statuses, provider relationships, organization details, payment methods, accounting information, credit information, asset information, collateral information, address information, contact information, entity hours, availability, user account information, descriptive content, notifications, and/or the like.; paragraph 435 - providers can be identified based on any combination of factors such as rating, traffic amount, whether a provider paid a listing fee, and price range).
Claim 10 recites limitations substantially similar to those of claim 4 above; thus, the same rejection applies.
As per claim 5, Sahu teaches the system as claimed in claim 1, wherein the match report comprises an ordered list of businesses matched to at least one of buyer, seller and service provider preferences (paragraph 341 - the recommendation modules discussed above can be configured to provide input to and/or receive output from each other. For instance, the results from the other recommendation modules may be used by the related categories module 1336 to determine relationships between categories of providers. The results from the related categories module can be used by the other modules to filter or to expand their recommended providers. As another example, the ranking of providers by popularity at a certain geographic location as provided by the geo module 1342 can be used to affect the ranking of providers in other modules.).
Claim 11 recites limitations substantially similar to those of claim 5 above; thus, the same rejection applies.
As per claim 6, Sahu teaches the system as claimed in claim 1, wherein the match report comprises matches between users and other users and data objects, the data objects to represent buyers, sellers, businesses for sale and service providers (paragraph 328 - The recommendation modules can be configured to generate candidate recommendations based on the data provided by various data sources. In some embodiments, each of the recommendation modules corresponds to a particular factor or consideration in generating the recommendation. As such, each recommendation module may be configured to provide recommended providers and/or categories based primarily on a particular dataset or a particular combination of datasets. The recommendation modules can include user interactions module 1326, user interests module 1328, revisits module 1330, chains module 1332, also clicked module 1334, related categories module 1336, seasonality module 1338, day parting module 1340, and geo module 1342).
Claim 12 recites limitations substantially similar to those of claim 6 above; thus, the same rejection applies.
As per claim 7 Sahu teaches a method of matching buyers, sellers and service providers, the method comprising:
accessing information from data sources on a network (paragraph 40 - The interaction processing infrastructure 102 may facilitate searching of one or more information repositories in response to data received over the one or more networks 108 from any one or combination of the interfaces; paragraph 42 - the interaction processing infrastructure 102 may be communicatively coupled or couplable to one or more data sources via one or more data acquisition interfaces 111. The one or more data sources may include any suitable source of data to facilitate embodiments disclosed further herein. In various embodiments, the one or more data sources may include one or more of a database, a website, any repository of data in any suitable form);
extracting data objects from the accessed information and storing the data objects in a database (paragraph 43 - Certain data pulled and/or pushed from the one or more data sources may be transformed and the transformed data and/or other data generated based thereon may be made available by the interaction processing infrastructure 102 for users of client devices 205 and/or 207. In alternative embodiments, data from the one or more data sources may be made available directly to client devices 205 and/or 207)
the data objects comprising data transformation data objects based on characteristics of businesses and characteristics of service providers and preference data objects based on at least one buyer, seller and service provider preference (paragraph 123- the feed engine 239 may further transform the transformed data/content/information into a feed object with a data-interchange format that facilitates parsing. The feed engine 239 and/or the aggregation/transformation engine 231 may translate the data into understandable data, information, and/or content. The transformed data, information, and/or content may be directed to certain tables and/or data stores 268 based on the type of and/or an entity category to which the data, information, and/or content relates; paragraph 248 - In some embodiments, specific weights (also referred to as distributions) are associated with candidate providers and/or candidate categories generated by each module depending on a weight associated with a data source of that module and/or user behaviors, if any, associated with that module);
accessing the data objects from the database (paragraph 40 - The interaction processing infrastructure 102 may facilitate searching of one or more information repositories in response to data received over the one or more networks 108 from any one or combination of the interfaces; paragraph 42 - the interaction processing infrastructure 102 may be communicatively coupled or couplable to one or more data sources via one or more data acquisition interfaces 111. The one or more data sources may include any suitable source of data to facilitate embodiments disclosed further herein. In various embodiments, the one or more data sources may include one or more of a database, a website, any repository of data in any suitable form);
and executing a machine learning matching algorithm based on the data objects (paragraph 245 – the data utilized by the recommendation service can include multiple datasets gathered by the interaction infrastructure. Some of the data can be derived through data-mining, statistical analysis, machine learning, or other automated or semi-automated processes performed on other user data such as a user's search behavior, click-through behavior, and the like), and
generating a match report based on the output of the machine learning matching algorithm
(paragraph 245 – the data utilized by the recommendation service can include multiple datasets gathered by the interaction infrastructure. Some of the data can be derived through data-mining, statistical analysis, machine learning, or other automated or semi-automated processes performed on other user data such as a user's search behavior, click-through behavior, and the like)
(paragraph 299 - #TopRecommendations; paragraph 306 - The baseScore parameter may indicate a base or default score (e.g., 15000) for the corresponding module and/or for a recommended item (e.g., provider or category) made by the module. The base score of a recommendation may be updated based on other parameters (e.g., the decayPerDay parameter, the boostedScoreType parameter) to obtain a final score. The final score may be compared with the scores of recommended items by other modules during a ranking process to determine the final recommendations; paragraph 328 - The recommendation modules can be configured to generate candidate recommendations based on the data provided by various data sources. In some embodiments, each of the recommendation modules corresponds to a particular factor or consideration in generating the recommendation. As such, each recommendation module may be configured to provide recommended providers and/or categories based primarily on a particular dataset or a particular combination of datasets. The recommendation modules can include user interactions module 1326, user interests module 1328, revisits module 1330, chains module 1332, also clicked module 1334, related categories module 1336, seasonality module 1338, day parting module 1340, and geo module 1342).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Shuman et al. (US PG Pub 20160048900) facilitates discovery and management of business information to assist a user in identifying one or more businesses of interest.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PETER H CHOI whose telephone number is (469)295-9171. The examiner can normally be reached M-Th 9am-7pm.
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/PETER H CHOI/ Supervisory Patent Examiner, Art Unit 3681