DETAILED ACTION
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/16/2026 has been entered.
Status of Claims
Applicant's “Request for Continued Examination” filed on 3/16/2026 has been considered.
Rejection to Claims 1-16 under nonstatutory double patenting have not been overcome.
Claims 1, 3, 7, 10, 14, 18 are amended.
Claims 2, 4, 12-13 are cancelled.
Claims 1, 3, 5-11, 14-20 are currently pending and have been examined.
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 of this title, 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 1, 3, 5-6, 8-11, 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application No. 2022/0067814 A1 to Goncalves in view of U.S. Patent No. 11895372 B2 to Sanghavi.
Regarding Claim 1, GONCALVES discloses a computer-implemented method for selecting digital artifacts for recommendation from amongst a plurality of digital artifacts, the method comprising:
identifying a current user as one of: an above-threshold user who has consumed at least a threshold number of artifacts; and ([0139] The recommendation inference can model user behaviour with public profile data and resource interaction data (resource view, comment, rate, bookmark). The process involves persona detection by processing public profile data to fetch information of the returned public user. The recommendation inference receives the profile data, along with resource interaction data. [0156] As an example, there can be three types of users and recommendations: [0157] Non-client non-registered public user is a “cold-start” user—no recommendations, however, the user can use the search function [0158] Non-client registered public user is a “semi-cold-start” user—recommendations based on content-to-content correlation from behaviors saved from previous sessions/visits to the site [0159] Client user is a “warm-start” user—recommendations based on the mapping of the user attributes from the client profile data to a Persona (the persona is linked to preferred service types based on Occupation), in addition, also recommendations based on content-to-content correlation [0059] relevant tools, services and articles (artifacts))
a below-threshold user who has consumed fewer than the threshold number of artifacts; [0157] Non-client non-registered public user is a “cold-start” user
where the current user is identified as being an above-threshold user, selecting artifacts for recommendation according to a first recommendation engine; and ([0004] Embodiments described herein can optimize the machine learning prediction model using cold start and warm start options based on persona data. [0005] The processor has a personalization engine to generate a set of resources for the resource recommendations based on the persona and at least one machine learning model to detect similarities in content from the resource database and user preferences.)
where the current user is identified as being a below-threshold user, selecting artifacts for recommendation according to a second recommendation engine.). ([0004] Embodiments described herein can optimize the machine learning prediction model using cold start and warm start options based on persona data. [0005] The processor has a personalization engine to generate a set of resources for the resource recommendations based on the persona and at least one machine learning model to detect similarities in content from the resource database and user preferences.)
wherein: the first recommendation engine is an artifact-centric recommendation engine; and ([0013] the at least one machine learning model comprises a hybrid model of content-to-content similarity and collaborative filtering by detecting users with similar behaviours (artifact-centric). [0156] As an example, there can be three types of users and recommendations: [0157] Non-client non-registered public user is a “cold-start” user—no recommendations, however, the user can use the search function [0158] Non-client registered public user is a “semi-cold-start” user—recommendations based on content-to-content correlation from behaviors saved from previous sessions/visits to the site [0159] Client user is a “warm-start” user—recommendations based on the mapping of the user attributes from the client profile data to a Persona (the persona is linked to preferred service types based on Occupation) (artifact-centric), in addition, also recommendations based on content-to-content correlation
the second recommendation engine is a property-centric recommendation engine. ([0059] Embodiments relate to a personalized, just-in-time recommendation system for products and services based on content-to-content correlation or similarity (property-centric), mapping of users to personas, similarity between personas and content, and selecting the most relevant tools, services and articles as next best actions for a stepped-care process.)
wherein: the property-centric recommendation engine further identifies each current user who was identified as a below-threshold user as one of: an empty user who has consumed no artifacts; and a naive user who has consumed at least one artifact and fewer than the threshold number of artifacts; and for each current user who is identified as being a naive user, the property-centric recommendation engine deploys a property-centric collaborative filtering engine that selects artifact property criteria by leveraging data about the current user to similar prior users. [0156] As an example, there can be three types of users and recommendations: [0157] Non-client non-registered public user is a “cold-start” user (empty user)—no recommendations, however, the user can use the search function [0158] Non-client registered public user is a “semi-cold-start” user (naïve user)—recommendations based on content-to-content correlation from behaviors saved from previous sessions/visits to the site [0159] Client user is a “warm-start” user —recommendations based on the mapping of the user attributes from the client profile data to a Persona (the persona is linked to preferred service types based on Occupation) (artifact-centric), in addition, also recommendations based on content-to-content correlation)
But does not explicitly disclose by leveraging data about the current user to identify similar prior users having similar proclivities and affinities to the current user to first select one or more properties of those of the digital artifacts that were of interest to the similar prior users and then select digital artifacts having the selected one or more properties; digital artifacts. GONCALVES does disclose articles [0059].
SANGHAVI, on the other hand, teaches digital artifacts. ([Col 2 Ln 65-67] streaming media [Col 6 Ln 45-50] building a list of items that a user has watched on one or more streaming platforms.) by leveraging data about the current user to identify similar prior users having similar proclivities and affinities to the current user to first select one or more properties of those of the digital artifacts that were of interest to the similar prior users and then select digital artifacts having the selected one or more properties; ([Col 15 Ln 35-45]) In an exemplary embodiment, the recommendation system predicts the most the relevant and personalized content title for every user via collaborative filtering, popularity and diversity rules while applying predicting additional exploratory content assets, such as randomized sequencing after the top X (say 20) recommendations are generated. The exploratory content assists in extracting actual preferences of the user to further train the models. [Col 7 Ln 1-10] methods use an item profile (i.e., a set of discrete attributes and features) characterizing the item within the system. To abstract the features (properties) of the items in the system, an item presentation algorithm is applied. A widely used algorithm is the tf—idf representation (also called vector space representation). The system creates a content-based profile of users based on a weighted vector of item features. The weights denote the importance of each feature to the user and can be computed from individually rated content vectors using a variety of techniques. [Col 9 Ln 15-25] the recommendation system (RecSys) may generate recommendations via collaborative filtering with knowledge that the users who have behaviors similar to user X, would enjoy sitcom comedy and as a result the recommendations system recommends TV Show B on Channel Y to this user via a personalized banner ad. )
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by GONCALVES, the features as taught by SANGHAVI, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify GONCALVES, to include the teachings of SANGHAVI, in order to accurately recommend complex items such as movies without requiring an “understanding” of the item itself. (SANGHAVI, [Col 6 Ln 35-40]).
Regarding Claim 3, GONCALVES in view of SANGHAVI teaches the method of claim 1.
GONCALVES discloses wherein the artifact-centric recommendation engine deploys an artifact-centric collaborative filtering engine that selects the digital artifacts for recommendation by comparing the current user to similar prior users ([0013] the at least one machine learning model comprises a hybrid model of content-to-content similarity and collaborative filtering by detecting users with similar behaviours (artifact-centric). [0156] As an example, there can be three types of users and recommendations: [0157] Non-client non-registered public user is a “cold-start” user—no recommendations, however, the user can use the search function [0158] Non-client registered public user is a “semi-cold-start” user—recommendations based on content-to-content correlation from behaviors saved from previous sessions/visits to the site [0159] Client user is a “warm-start” user—recommendations based on the mapping of the user attributes from the client profile data to a Persona (the persona is linked to preferred service types based on Occupation) (artifact-centric), in addition, also recommendations based on content-to-content correlation)
SANGHAVI, on the other hand, teaches and selecting only individual digital artifacts, rather than first selecting properties of the digital artifacts and then selecting digital artifacts having those properties; ([Col 15 Ln 35-45]) In an exemplary embodiment, the recommendation system predicts the most the relevant and personalized content title for every user via collaborative filtering, popularity and diversity rules while applying predicting additional exploratory content assets, such as randomized sequencing after the top X (say 20) recommendations are generated. The exploratory content assists in extracting actual preferences of the user to further train the models. [Col 7 Ln 1-10] methods use an item profile (i.e., a set of discrete attributes and features) characterizing the item within the system. To abstract the features (properties) of the items in the system, an item presentation algorithm is applied. A widely used algorithm is the tf—idf representation (also called vector space representation). The system creates a content-based profile of users based on a weighted vector of item features. The weights denote the importance of each feature to the user and can be computed from individually rated content vectors using a variety of techniques.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by GONCALVES, the features as taught by SANGHAVI, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify GONCALVES, to include the teachings of SANGHAVI, in order to accurately recommend complex items such as movies without requiring an “understanding” of the item itself. (SANGHAVI, [Col 6 Ln 35-40]).
.
Regarding Claim 5, GONCALVES in view of SANGHAVI teaches the method of claim 1.
GONCALVES discloses wherein for each current user who is identified as being an empty user, the property-centric recommendation engine: receives user input from the empty user wherein the user input is indicative of areas of interest to the empty user; and selects the digital artifact property criteria according to the user input. [0156] As an example, there can be three types of users and recommendations: [0157] Non-client non-registered public user is a “cold-start” user (empty user)—no recommendations, however, the user can use the search function [0158] Non-client registered public user is a “semi-cold-start” user (naïve user)—recommendations based on content-to-content correlation from behaviors saved from previous sessions/visits to the site [0159] Client user is a “warm-start” user —recommendations based on the mapping of the user attributes from the client profile data to a Persona (the persona is linked to preferred service types based on Occupation) (artifact-centric), in addition, also recommendations based on content-to-content correlation [0067-0068] The interface 110 monitors for events and resource requests to collect and store user preference data and event data. The web application 100 combines user preferences with personas to generate recommendations for the interface 110.)
Regarding Claim 6, GONCALVES in view of SANGHAVI teaches the method of claim 1.
GONCALVES discloses wherein the property-centric recommendation engine selects the digital artifacts for recommendation from amongst a set of digital artifacts satisfying the selected digital artifact property criteria according to at least one of a relevance score, a release time, or randomness.. [0095] For meta data similarity, the web application 100 uses each resource's binary valued representation of meta data to calculate cosine similarity. By combining text based and meta-data similarity score as: R.sub.score=α*Score.sub.text+β*Score.sub.metadata, such that α=0.5, β=0.5, the web application 100 can sort resources based on R.sub.score to find the most similar resource to the viewed resources.)
Regarding Claim 8, GONCALVES in view of SANGHAVI teaches the method of claim 1.
GONCALVES discloses A data processing system comprising at least one processor and memory coupled to the at least one processor, wherein the memory contains instructions which, when implemented by the at least one processor, cause the at least one processor to implement the method of claim 1. [0162] Throughout the discussion, numerous references will be made regarding servers, services, interfaces, portals, platforms, or other systems formed from computing devices. It should be appreciated that the use of such terms is deemed to represent one or more computing devices having at least one processor configured to execute software instructions stored on a computer-readable, tangible, non-transitory medium. For example, a server can include one or more computers operating as a web server, database server, or other type of computer server in a manner to fulfill described roles, responsibilities, or functions.)
Regarding Claim 9, GONCALVES in view of SANGHAVI teaches the method of claim 1.
GONCALVES discloses A non-transitory, tangible computer-readable medium embodying instructions which, when implemented by at least one processor of a data processing system, cause the data processing system to implement the method of claim 1. [0162] Throughout the discussion, numerous references will be made regarding servers, services, interfaces, portals, platforms, or other systems formed from computing devices. It should be appreciated that the use of such terms is deemed to represent one or more computing devices having at least one processor configured to execute software instructions stored on a computer-readable, tangible, non-transitory medium. For example, a server can include one or more computers operating as a web server, database server, or other type of computer server in a manner to fulfill described roles, responsibilities, or functions.)
Regarding Claim 10, GONCALVES discloses a computer-implemented method for selecting digital artifacts for recommendation from amongst a plurality of digital artifacts, the method comprising:
bifurcating users into low-data users and high-data users; ([0139] The recommendation inference can model user behaviour with public profile data and resource interaction data (resource view, comment, rate, bookmark). The process involves persona detection by processing public profile data to fetch information of the returned public user. The recommendation inference receives the profile data, along with resource interaction data. [0156] As an example, there can be three types of users and recommendations: [0157] Non-client non-registered public user is a “cold-start” user—no recommendations, however, the user can use the search function [0158] Non-client registered public user is a “semi-cold-start” user—recommendations based on content-to-content correlation from behaviors saved from previous sessions/visits to the site [0159] Client user is a “warm-start” user—recommendations based on the mapping of the user attributes from the client profile data to a Persona (the persona is linked to preferred service types based on Occupation), in addition, also recommendations based on content-to-content correlation [0059] relevant tools, services and articles (artifacts))
for the high-data users, directly selecting individual ones of the artifacts for recommendation according to a first recommendation engine; and ([0004] Embodiments described herein can optimize the machine learning prediction model using cold start and warm start options based on persona data. [0005] The processor has a personalization engine to generate a set of resources for the resource recommendations based on the persona and at least one machine learning model to detect similarities in content from the resource database and user preferences.)
for the low-data users, indirectly selecting individual ones of the artifacts for recommendation by first selecting artifact property criteria and then selecting from among those of the artifacts that satisfy the selected artifact property criteria; ([0004] Embodiments described herein can optimize the machine learning prediction model using cold start and warm start options based on persona data. [0005] The processor has a personalization engine to generate a set of resources for the resource recommendations based on the persona and at least one machine learning model to detect similarities in content from the resource database and user preferences.)
further bifurcating the low-data users into zero-data users and some-data users; and for the some-data users, selecting the artifact property criteria using a second recommendation engine, [0156] As an example, there can be three types of users and recommendations: [0157] Non-client non-registered public user is a “cold-start” user (empty user)—no recommendations, however, the user can use the search function [0158] Non-client registered public user is a “semi-cold-start” user (naïve user)—recommendations based on content-to-content correlation from behaviors saved from previous sessions/visits to the site [0159] Client user is a “warm-start” user —recommendations based on the mapping of the user attributes from the client profile data to a Persona (the persona is linked to preferred service types based on Occupation) (artifact-centric), in addition, also recommendations based on content-to-content correlation)
wherein the second recommendation engine is a second collaborative filtering engine that selects the digital artifact property criteria by leveraging data about the some data users. [0156] As an example, there can be three types of users and recommendations: [0157] Non-client non-registered public user is a “cold-start” user (empty user)—no recommendations, however, the user can use the search function [0158] Non-client registered public user is a “semi-cold-start” user (naïve user)—recommendations based on content-to-content correlation from behaviors saved from previous sessions/visits to the site [0159] Client user is a “warm-start” user —recommendations based on the mapping of the user attributes from the client profile data to a Persona (the persona is linked to preferred service types based on Occupation) (artifact-centric), in addition, also recommendations based on content-to-content correlation)
But does not explicitly disclose by leveraging data about the current user to identify similar prior users having similar proclivities and affinities to the some data users to select one or more properties of those of the digital artifacts that were of interest to those similar prior users as the digital artifact property criteria. digital artifacts. GONCALVES does disclose articles [0059].
SANGHAVI, on the other hand, teaches digital artifacts. ([Col 2 Ln 65-67] streaming media [Col 6 Ln 45-50] building a list of items that a user has watched on one or more streaming platforms.) by leveraging data about the current user to identify similar prior users having similar proclivities and affinities to the some data users to select one or more properties of those of the digital artifacts that were of interest to those similar prior users as the digital artifact property criteria; ([Col 15 Ln 35-45]) In an exemplary embodiment, the recommendation system predicts the most the relevant and personalized content title for every user via collaborative filtering, popularity and diversity rules while applying predicting additional exploratory content assets, such as randomized sequencing after the top X (say 20) recommendations are generated. The exploratory content assists in extracting actual preferences of the user to further train the models. [Col 7 Ln 1-10] methods use an item profile (i.e., a set of discrete attributes and features) characterizing the item within the system. To abstract the features (properties) of the items in the system, an item presentation algorithm is applied. A widely used algorithm is the tf—idf representation (also called vector space representation). The system creates a content-based profile of users based on a weighted vector of item features. The weights denote the importance of each feature to the user and can be computed from individually rated content vectors using a variety of techniques. [Col 9 Ln 15-25] the recommendation system (RecSys) may generate recommendations via collaborative filtering with knowledge that the users who have behaviors similar to user X, would enjoy sitcom comedy and as a result the recommendations system recommends TV Show B on Channel Y to this user via a personalized banner ad. )
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by GONCALVES, the features as taught by SANGHAVI, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify GONCALVES, to include the teachings of SANGHAVI, in order to accurately recommend complex items such as movies without requiring an “understanding” of the item itself. (SANGHAVI, [Col 6 Ln 35-40]).
Regarding Claim 11, GONCALVES in view of SANGHAVI teaches the method of claim 10.
GONCALVES discloses wherein the first recommendation engine is a first collaborative filtering engine. ([0013] the at least one machine learning model comprises a hybrid model of content-to-content similarity and collaborative filtering by detecting users with similar behaviours (artifact-centric). [0156] As an example, there can be three types of users and recommendations: [0157] Non-client non-registered public user is a “cold-start” user—no recommendations, however, the user can use the search function [0158] Non-client registered public user is a “semi-cold-start” user—recommendations based on content-to-content correlation from behaviors saved from previous sessions/visits to the site [0159] Client user is a “warm-start” user—recommendations based on the mapping of the user attributes from the client profile data to a Persona (the persona is linked to preferred service types based on Occupation) (artifact-centric), in addition, also recommendations based on content-to-content correlation [0059] Embodiments relate to a personalized, just-in-time recommendation system for products and services based on content-to-content correlation or similarity (property-centric), mapping of users to personas, similarity between personas and content, and selecting the most relevant tools, services and articles as next best actions for a stepped-care process.)
Regarding Claim 14, GONCALVES in view of SANGHAVI teaches the method of claim 10.
GONCALVES discloses further comprising: for the zero-data users, receiving user input from the zero-data users wherein the user input is indicative of areas of interest; and selecting the digital artifact property criteria according to the user input. [0156] As an example, there can be three types of users and recommendations: [0157] Non-client non-registered public user is a “cold-start” user (empty user)—no recommendations, however, the user can use the search function [0158] Non-client registered public user is a “semi-cold-start” user (naïve user)—recommendations based on content-to-content correlation from behaviors saved from previous sessions/visits to the site [0159] Client user is a “warm-start” user —recommendations based on the mapping of the user attributes from the client profile data to a Persona (the persona is linked to preferred service types based on Occupation) (artifact-centric), in addition, also recommendations based on content-to-content correlation [0067-0068] The interface 110 monitors for events and resource requests to collect and store user preference data and event data. The web application 100 combines user preferences with personas to generate recommendations for the interface 110.)
SANGHAVI, on the other hand, teaches by leveraging the user input to identify similar prior users having similar proclivities and affinities to the zero data users to select one or more properties of those of the digital artifacts that were of interest to those similar prior users as the digital artifact property criteria. ([Col 9 Ln 15-25] the recommendation system (RecSys) may generate recommendations via collaborative filtering with knowledge that the users who have behaviors similar to user X, would enjoy sitcom comedy and as a result the recommendations system recommends TV Show B on Channel Y to this user via a personalized banner ad. [Col 15 Ln 35-45]) In an exemplary embodiment, the recommendation system predicts the most the relevant and personalized content title for every user via collaborative filtering, popularity and diversity rules while applying predicting additional exploratory content assets, such as randomized sequencing after the top X (say 20) recommendations are generated. The exploratory content assists in extracting actual preferences of the user to further train the models. [Col 7 Ln 1-10] methods use an item profile (i.e., a set of discrete attributes and features) characterizing the item within the system. To abstract the features (properties) of the items in the system, an item presentation algorithm is applied. A widely used algorithm is the tf—idf representation (also called vector space representation). The system creates a content-based profile of users based on a weighted vector of item features. The weights denote the importance of each feature to the user and can be computed from individually rated content vectors using a variety of techniques.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by GONCALVES, the features as taught by SANGHAVI, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify GONCALVES, to include the teachings of SANGHAVI, in order to accurately recommend complex items such as movies without requiring an “understanding” of the item itself. (SANGHAVI, [Col 6 Ln 35-40]).
Regarding Claim 15, GONCALVES in view of SANGHAVI teaches the method of claim 10.
GONCALVES discloses wherein selecting the digital artifact property criteria according to the user input is done by a third recommendation engine. [0156] As an example, there can be three types of users and recommendations: [0157] Non-client non-registered public user is a “cold-start” user (empty user)—no recommendations, however, the user can use the search function [0158] Non-client registered public user is a “semi-cold-start” user (naïve user)—recommendations based on content-to-content correlation from behaviors saved from previous sessions/visits to the site [0159] Client user is a “warm-start” user —recommendations based on the mapping of the user attributes from the client profile data to a Persona (the persona is linked to preferred service types based on Occupation) (artifact-centric), in addition, also recommendations based on content-to-content correlation [0067-0068] The interface 110 monitors for events and resource requests to collect and store user preference data and event data. The web application 100 combines user preferences with personas to generate recommendations for the interface 110.)
Regarding Claim 16, GONCALVES in view of SANGHAVI teaches the method of claim 10.
GONCALVES discloses A data processing system comprising at least one processor and memory coupled to the at least one processor, wherein the memory contains instructions which, when implemented by the at least one processor, cause the at least one processor to implement the method of claim 10. [0162] Throughout the discussion, numerous references will be made regarding servers, services, interfaces, portals, platforms, or other systems formed from computing devices. It should be appreciated that the use of such terms is deemed to represent one or more computing devices having at least one processor configured to execute software instructions stored on a computer-readable, tangible, non-transitory medium. For example, a server can include one or more computers operating as a web server, database server, or other type of computer server in a manner to fulfill described roles, responsibilities, or functions.)
Regarding Claim 17, GONCALVES in view of SANGHAVI teaches the method of claim 10.
GONCALVES discloses A non-transitory, tangible computer-readable medium embodying instructions which, when implemented by at least one processor of a data processing system, cause the data processing system to implement the method of claim 10. [0162] Throughout the discussion, numerous references will be made regarding servers, services, interfaces, portals, platforms, or other systems formed from computing devices. It should be appreciated that the use of such terms is deemed to represent one or more computing devices having at least one processor configured to execute software instructions stored on a computer-readable, tangible, non-transitory medium. For example, a server can include one or more computers operating as a web server, database server, or other type of computer server in a manner to fulfill described roles, responsibilities, or functions.)
Regarding Claim 18, GONCALVES discloses a computer-implemented method for recommending digital artifacts from amongst a plurality of digital artifacts, the method comprising:
selecting, artifacts for recommendation according to a common recommendation engine; wherein a quantity of artifacts consumed by the user is an input to the common recommendation engine. ([0139] The recommendation inference can model user behaviour with public profile data and resource interaction data (resource view, comment, rate, bookmark). The process involves persona detection by processing public profile data to fetch information of the returned public user. The recommendation inference receives the profile data, along with resource interaction data. [0156] As an example, there can be three types of users and recommendations: [0157] Non-client non-registered public user is a “cold-start” user—no recommendations, however, the user can use the search function [0158] Non-client registered public user is a “semi-cold-start” user—recommendations based on content-to-content correlation from behaviors saved from previous sessions/visits to the site [0159] Client user is a “warm-start” user—recommendations based on the mapping of the user attributes from the client profile data to a Persona (the persona is linked to preferred service types based on Occupation), in addition, also recommendations based on content-to-content correlation [0059] relevant tools, services and articles (artifacts) [0004] Embodiments described herein can optimize the machine learning prediction model using cold start and warm start options based on persona data. [0005] The processor has a personalization engine to generate a set of resources for the resource recommendations based on the persona and at least one machine learning model to detect similarities in content from the resource database and user preferences.)
But does not explicitly disclose digital artifacts; a trained machine learning model used as a common recommendation engine; the trained machine learning model; selecting, for any particular user, artifacts for recommendation. GONCALVES does disclose articles [0059].
SANGHAVI, on the other hand, teaches digital artifacts; ([Col 2 Ln 65-67] streaming media [Col 6 Ln 45-50] building a list of items that a user has watched on one or more streaming platforms.) a trained machine learning model used as a common recommendation engine; the trained machine learning model. ([Col 9 Ln 34-41] the recommendation system will generate recommendations for user X (402) by training an exploratory model 818 with the examples of inactive users that become active in Channel Z via title popularity and diversity signals. Using this methodology, the recommendation system may generate some number of titles (404-1 thru 404-5 (P.sub.n (n−20)), for example, 20 titles for every user at a given time.) selecting, for any particular user, artifacts for recommendation; ([Col 15 Ln 35-45]) In an exemplary embodiment, the recommendation system predicts the most the relevant and personalized content title for every user via collaborative filtering, popularity and diversity rules while applying predicting additional exploratory content assets, such as randomized sequencing after the top X (say 20) recommendations are generated. The exploratory content assists in extracting actual preferences of the user to further train the models. [Col 7 Ln 1-10] methods use an item profile (i.e., a set of discrete attributes and features) characterizing the item within the system. To abstract the features (properties) of the items in the system, an item presentation algorithm is applied. A widely used algorithm is the tf—idf representation (also called vector space representation). The system creates a content-based profile of users based on a weighted vector of item features. The weights denote the importance of each feature to the user and can be computed from individually rated content vectors using a variety of techniques. [Col 9 Ln 15-25] the recommendation system (RecSys) may generate recommendations via collaborative filtering with knowledge that the users who have behaviors similar to user X, would enjoy sitcom comedy and as a result the recommendations system recommends TV Show B on Channel Y to this user via a personalized banner ad. )
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by GONCALVES, the features, as taught by SANGHAVI, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify GONCALVES, to include the teachings of SANGHAVI, in order to accurately recommend complex items without requiring an “understanding” of the item itself. (SANGHAVI, [Col 6 Ln 35-40]).
Regarding Claim 19, GONCALVES in view of SANGHAVI teaches the method of claim 18.
GONCALVES discloses A data processing system comprising at least one processor and memory coupled to the at least one processor, wherein the memory contains instructions which, when implemented by the at least one processor, cause the at least one processor to implement the method of claim 18. [0162] Throughout the discussion, numerous references will be made regarding servers, services, interfaces, portals, platforms, or other systems formed from computing devices. It should be appreciated that the use of such terms is deemed to represent one or more computing devices having at least one processor configured to execute software instructions stored on a computer-readable, tangible, non-transitory medium. For example, a server can include one or more computers operating as a web server, database server, or other type of computer server in a manner to fulfill described roles, responsibilities, or functions.)
Regarding Claim 20, GONCALVES in view of SANGHAVI teaches the method of claim 18.
GONCALVES discloses A non-transitory, tangible computer-readable medium embodying instructions which, when implemented by at least one processor of a data processing system, cause the data processing system to implement the method of claim 18.. [0162] Throughout the discussion, numerous references will be made regarding servers, services, interfaces, portals, platforms, or other systems formed from computing devices. It should be appreciated that the use of such terms is deemed to represent one or more computing devices having at least one processor configured to execute software instructions stored on a computer-readable, tangible, non-transitory medium. For example, a server can include one or more computers operating as a web server, database server, or other type of computer server in a manner to fulfill described roles, responsibilities, or functions.)
Claims 7 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application No. 2022/0067814 A1 to Goncalves and U.S. Patent No. 11895372 B2 to Sanghavi in view of U.S. Patent Application No. 2022/0261863 A1 to Bhan.
Regarding Claim 7, GONCALVES in view of SANGHAVI teaches the method of claim 4.
However the combination of GONCALVES and SANGHAVI does not explicitly teach wherein the digital artifact property criteria comprises a topic determined by Latent Dirichlet Allocation (LDA) topic modeling.
BHAN, on the other hand, teaches wherein the digital artifact property criteria comprises a topic determined by Latent Dirichlet Allocation (LDA) topic modeling. ([0037] Creating the prior knowledge of topic-word distribution may be done manually for each use case to provide custom priors and allow for a guided latent Dirichlet allocation (LDA) approach which specializes in learning topics for a specific domain.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by GONCALVES and SANGHAVI, the features as taught by BHAN, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination, to include the teachings of BHAN, in order to extract topics (BHAN, [0037]).
Response to Arguments
Applicant’s arguments with respect to rejection of the claim under 35 USC 103 have been considered but are moot in view of new grounds of rejection.
Applicant argues “Goncalves does not teach for each current user who is identified as being a naive user, the property-centric recommendation engine deploys a property-centric collaborative filtering engine that selects digital artifact property criteria by leveraging data about the current user to identify similar prior users having similar proclivities and affinities to the current user to first select one or more properties of those of the digital artifacts that were of interest to the similar prior users and then select digital artifacts having the selected one or more properties.”
However, Goncalves is not relied upon to teach (the emphasized) portion of the claims. Examiner turns to Sanghavi to teach this claim element. Examiner directs Applicant’s attention to the office action, above. Sanghavi teaches the recommendation system (RecSys) may generate recommendations via collaborative filtering with knowledge that the users who have behaviors similar to user X, would enjoy sitcom comedy and as a result the recommendations system recommends TV Show B on Channel Y to this user via a personalized banner ad. See Col 9, Ln 15-25.
Applicant further argues Goncalves does not teach or suggest “selecting, for any particular use, digital artifacts for recommendation.
Conclusion
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/MICHELLE T KRINGEN/Primary Examiner, Art Unit 3689