Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
Regarding claims 1,7, 13, and 17, the phrase “authoritativeness”, is recites as a key structural element; however no clear definition for authoritativeness exists in the claims or specification. At most the specification declares “authoritativeness” as something that is, non-exclusively, be based upon the type of source, the reputation of the reviewers, and information comprehensiveness.
Given the indefiniteness of the phrase, and in the interest of simplicity, the examiner has chosen to interpret “authoritativeness” to mean any way to check the quality of the review data from different data sources.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim’s 1,7,13, and 17 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claims 1, 13, 7 and 17 The use of the term authoritativeness of the data source renders the term indefinite due to it not being clearly defined what authoritativeness is with three different ways being stated in the spec that may be used to define the authoritativeness of a data source in the specification
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected as being directed to an abstract idea without significantly more.
Regarding Claim 1
Step 1: A process
Step 2A Prong 1: assigning authority scores to the data sources based upon authoritativeness of the data sources points to mental process that could be done with the aid of pen and paper, a person can arbitrarily assign a score to a data source based on its quality.
Processing the data from the data sources to create candidate collections, wherein a candidate collection for a content item comprises a set of semantic information corresponding to at least one of a category, an entity, or a term extracted from data source; points to a mental process that could be done with the aid of pen and paper, a person can manually create collections of data.
Utilizing the authority scores to select semantic information for the content item from the candidate collections; points to a mental process that could be done with the aid of pen and paper, a person can use their own arbitrary metric to choose semantic information for items in a collection.
Step 2A Prong 2: The additional limitations collecting data from sources that provide information about content items; is an insignificant extra-solution activity that does not amount to an inventive concept, particularly when the activity is well-understood or conventional – see MPEP 2106.05(g) Examiner’s note: collecting data from sources that provide information in this case amounts to nothing more than mere data gathering
performing a semantic-based action using the semantic information is a process that doesn’t add anything significantly more to the abstract idea beyond just applying a generic computer method to the abstract idea– see MPEP 2106.05(f) Examiner’s note: high level recitation of a semantic-based action using semantic information
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitations of collecting data from sources that provide information about content items; does not add significantly more to the abstract idea to make it an inventive concept – CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011)
performing a semantic-based action using the semantic information does not add significantly more to the abstract idea to make it an inventive concept – see Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015)
Regarding Claim 2
Step 1: A process
Step 2A Prong 1: Recites the abstract ideas of Claim 1
Step 2A Prong 2: The additional limitations modifying operation of an application based upon the semantic information, wherein the operation is modified to provide a user with a personalized experience while interacting with the application is a process that doesn’t add anything significantly more to the abstract idea beyond just applying a - generic computer method to the abstract idea– see MPEP 2106.05(f) Examiner’s note: high level recitation of modifying an application based on semantic information.
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation modifying operation of an application based upon the semantic information, wherein the operation is modified to provide a user with a personalized experience while interacting with the application does not add significantly more to the abstract idea to make it an inventive concept – see Bancorp Servs., LLC v. Sun Life Assur. Co. of Canada, 687 F.3d 1266, 1280-81, 103 USPQ2d 1425, 1434-35 (Fed. Cir. 2012).
Regarding Claim 3
Step 1: A process
Step 2A Prong 1: Recites the abstract ideas of Claim 1
Step 2A Prong 2: The additional limitations selecting content from available content to provide to a user based upon the content corresponding to the semantic information; is a process that doesn’t add anything significantly more to the abstract idea beyond just applying a generic computer method to the abstract idea– see MPEP 2106.05(f) Examiner’s note: high level recitation of selecting content based on a criteria
and displaying the content to the user through a display of a device is a process that doesn’t add anything significantly more to the abstract idea beyond just applying a generic computer method to the abstract idea– see MPEP 2106.05(f) Examiner’s note: high level recitation of selecting and displaying content based off criteria
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation selecting content from available content to provide to a user based upon the content correspond to the semantic information; does not add significantly more to the abstract idea to make it an inventive concept; and displaying the content to the user through a display of a device does not add significantly more to the abstract idea to make it an inventive concept – see Bancorp Servs., LLC v. Sun Life Assur. Co. of Canada, 687 F.3d 1266, 1280-81, 103 USPQ2d 1425, 1434-35 (Fed. Cir. 2012).
Regarding Claim 4
Step 1: A process
Step 2A Prong 1: Generating content based upon the semantic information, wherein the content is tailored to an interest of a user; is a mental process that can be done with the aid of pen and paper, a person can manually create content based off of information they may find interesting.
Step 2A Prong 2: The additional limitation and displaying the content to the user through a display of a device is an insignificant extra-solution activity that does not amount to an inventive concept, particularly when the activity is well-understood or conventional – see MPEP 2106.05(g) Examiner’s note: displaying content to a user here is an insignificant extra-solution.
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation displaying the content to the user through a display of a device does not add significantly more to the abstract idea to make it an inventive concept – see Bancorp Servs., LLC v. Sun Life Assur. Co. of Canada, 687 F.3d 1266, 1280-81, 103 USPQ2d 1425, 1434-35 (Fed. Cir. 2012).
Regarding Claim 5
Step 1: A process
Step 2A Prong 1: Recites the abstract ideas of Claim 1
Step 2A Prong 2: The additional limitations training a model using the semantic information; is a process that doesn’t add anything significantly more to the abstract idea beyond just applying a generic computer method to the abstract idea– see MPEP 2106.05(f) Examiner’s note: high level recitation of training a model
Utilizing the model to select content from available content to provide to a user; is a process that doesn’t add anything significantly more to the abstract idea beyond just applying a generic computer method to the abstract idea– see MPEP 2106.05(f) Examiner’s note: high level recitation of using a model to select content for a user
And recommending the content to the user is a process that doesn’t add anything significantly more to the abstract idea beyond just applying a generic computer method to the abstract idea– see MPEP 2106.05(f) Examiner’s note: high level recitation of recommending content to a user
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitations training a model using the semantic information; does not add significantly more to the abstract idea to make it an inventive concept – see Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015); utilizing the model to select content from available content to provide to a user – see Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015); does not add significantly more to the abstract idea to make it an inventive concept; and recommending the content to the user does not add significantly more to the abstract idea to make it an inventive concept – see Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015);.
Regarding Claim 6
Step 1: A process
Step 2A Prong 1: identifying an application that is similar to one or more applications utilized by a user, wherein the application is identified as being similar to the one or more applications based upon the semantic information; recites a mental process that can be done with the aid of pen and paper, a person can identify applications that they think are similar to applications they already use.
Step 2A Prong 2: The additional limitations and providing a recommendation to the application to the user is a process that doesn’t add anything significantly more to the abstract idea beyond just applying a generic computer method to the abstract idea– see MPEP 2106.05(f) Examiner’s note: high level recitation of recommending content to a user
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation and providing a recommendation to the application to the user does not add significantly more to the abstract idea to make it an inventive concept – see Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017).
Regarding Claim 7
Step 1: A process
Step 2A Prong 1: determining authoritativeness for the data source type, a domain quality, and information comprehensiveness of information extracted from the data source points to mental process that could be done with the aid of pen and paper, a person can arbitrarily determine the credibility of a data source.
Step 2A Prong 2 and Step 2B: There are no additional elements recited so the claim does not provide a practical application and is not considered to be significantly more. As such, the claim is patent ineligible.
Regarding Claim 8
Step 1: A process
Step 2A Prong 1: and processing the fields of interest to create the candidate collection recites a mental process that can be done with the aid of pen and paper
Step 2A Prong 2: the additional limitations identifying fields of interest from the mobile app data feed, wherein the fields of interest correspond to at least one of a title, a main category, a description, meta keywords, user reviews, or related applications for the mobile app, wherein the fields of interest are identified using at least one of field-specific markup language xpaths, a layout based machine learning model, or structural information; is a process that doesn’t add anything significantly more to the abstract idea beyond just applying a generic computer method to the abstract idea– see MPEP 2106.05(f) Examiner’s note: high level recitation of identifying fields of interests for a user using either machine learning, markup language xpaths, or structural information.
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation identifying fields of interest from the mobile app data feed, wherein the fields of interest correspond to at least one of a title, a main category, a description, meta keywords, user reviews, or related applications for the mobile app, wherein the fields of interest are identified using at least one of field-specific markup language xpaths, a layout based machine learning model, or structural information; does not add significantly more to the abstract idea to make it an inventive concept – see Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017).
Regarding Claim 9
Step 1: A method
Step 2A Prong 1: recites the abstract ideas of claim 1
Step 2A Prong 2: the additional limitations filtering, from the candidate collections, blacklisted entries are a process that doesn’t add anything significantly more to the abstract idea beyond just applying a generic computer method to the abstract idea– see MPEP 2106.05(f) Examiner’s note: high level recitation of filtering black listed entries from data
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation filtering, from the candidate collections, blacklisted entries does not add significantly more to the abstract idea to make it an inventive concept – see Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1319, 120 USPQ2d 1353, 1361 (Fed. Cir. 2016).
Regarding Claim 10
Step 1: A process
Step 2A Prong 1: assigning ranks to the candidate collections to create ranked candidate collections based upon at least one of relevancy, frequency, or uniqueness of semantic information within the candidate collections; is a mental process that can be done with the aid of pen and paper, a person can assign ranks to a candidate collection based on relevancy, frequency or uniqueness.
Selecting a subset of the ranked candidate collections based upon the ranks; is a mental process that can be done with the aid of pen and paper.
Step 2A Prong 2: The additional limitation selecting the semantic information for the content item using the subset of the ranked candidate collections is an insignificant extra-solution activity that does not amount to an inventive concept, particularly when the activity is well-understood or conventional – see MPEP 2106.05(g) Examiner’s note: selecting semantic information from the content using the candidate collections amounts to nothing more than selecting a particular data source or type of data to be manipulated.
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation selecting the semantic information for the content item using the subset of the ranked candidate collections does not add significantly more to the abstract idea to make it an inventive concept – see Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
Regarding Claim 11
Step 1: A method
Step 2A Prong 1: Recites the abstract ideas of claim 10
Step 2A Prong 2: The additional limitations creating weighted combinations of categories, entities, and terms using the subset of the ranked candidate collections and the authority scores; is a process that doesn’t add anything significantly more to the abstract idea beyond just applying a generic computer method to the abstract idea– see MPEP 2106.05(f) Examiner’s note: high level recitation of creating weighted combinations from the candidate collections and authority scores
And selecting semantic information for the content item using the weighted combinations is a process that doesn’t add anything significantly more to the abstract idea beyond just applying a generic computer method to the abstract idea– see MPEP 2106.05(f) Examiner’s note: high level recitation of selecting information from the weighted combinations
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitations creating weighted combinations of categories, entities, and terms using the subset of the ranked candidate collections and the authority scores – see Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015); does not add significantly more to the abstract idea to make it an inventive concept; and selecting semantic information for the content item using the weighted combinations does not add significantly more to the abstract idea to make it an inventive concept – see Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015).
Regarding Claim 12
Step 1: A process
Step 2A Prong 1: Recites the abstract ideas of claim 1
Step 2A Prong 2: the additional limitation tagging an application with the semantic information is a process that doesn’t add anything significantly more to the abstract idea beyond just applying a generic computer method to the abstract idea– see MPEP 2106.05(f) Examiner’s note: high level recitation of tagging content with information
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation tagging an application with semantic information does not add significantly more to the abstract idea to make it an inventive concept – see Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015).
Regarding Claim 13
Step 1: A system
Step 2A Prong 1: Assigning authority scores to the data sources based upon authoritativeness of the data sources; is a mental process that can be done with the aid of pen and paper, a person can arbitrarily assign a score to a data source based on its quality.
Processing the data from the data sources to create candidate collections, wherein a candidate collection for a content item comprises a set of semantic information corresponding to at least one of a category, an entity or a term extracted from a data source; points to a mental process that could be done with the aid of pen and paper, a person can manually create a collection of data.
Utilizing the authority scores to select semantic information for the content item from the candidate collections; points to a mental process that could be done with the aid of pen and paper, a person can arbitrarily select semantic information for content in a collection based off of a score.
Step 2A Prong 2: The additional limitations collecting data from data sources that provide at least one of reviews, search results, access to, or information about content items; insignificant extra-solution activity that does not amount to an inventive concept, particularly when the activity is well-understood or conventional – see MPEP 2106.05(g) Examiner’s note: collecting data from data sources that provide at least one of reviews, search results, access to, or information about content items amount to nothing more than mere data gathering in this case.
And tagging the content item with the semantic information. is a process that doesn’t add anything significantly more to the abstract idea beyond just applying a generic computer method to the abstract idea– see MPEP 2106.05(f) Examiner’s note: high level recitation of tagging content with information
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation collecting data from data sources that provide at least one of reviews, search results, access to, or information about content items; does not add significantly more to the abstract idea to make it an inventive concept – see CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011); and tagging the content with the semantic information does not add significantly more to the abstract idea to make it an inventive concept – see Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015).
Regarding Claim 14
Step 1: A system
Step 2A Prong 1: Recites the abstract ideas of claim 13
Step 2A Prong 2: the additional limitation collecting the data from app store pages of an app store, wherein the data comprises titles, reviews, and descriptions of applications available from the app store pages, and wherein the content items comprise the applications insignificant extra-solution activity that does not amount to an inventive concept, particularly when the activity is well-understood or conventional – see MPEP 2106.05(g) Examiner’s note: collecting data from app store pages that contain titles, reviews, and descriptions of applications on the app store amounts to nothing more than mere data gathering
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation collecting the data from app store pages of an app store, wherein the data comprises titles, reviews, and descriptions of applications available from the app store pages, and wherein the content items comprise the applications does not add significantly more to the abstract idea to make it an inventive concept – see CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011);
Regarding Claim 15
Step 1: A system
Step 2A Prong 1: Recites the abstract ideas of claim 13
Step 2A Prong 2: the additional limitation collecting the data from content item review websites, and wherein the content items comprise at least one of applications, movies, music, videogames, videos, or shopping products insignificant extra-solution activity that does not amount to an inventive concept, particularly when the activity is well-understood or conventional – see MPEP 2106.05(g) Examiner’s note: collecting data from content amounts to nothing more than mere data gathering
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation collecting the data from content item review websites, and wherein the content items comprise at least one of applications, movies, music, videogames, videos, or shopping products does not add significantly more to the abstract idea to make it an inventive concept – see CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011);
Regarding Claim 16
Step 1: A system
Step 2A Prong 1: generating a query that includes a mobile platform and keywords relating to at least one of ratings and reviews; is an abstract idea that can be done with the aid of pen and paper, a person can make a query that includes a platform, along with a rating or a review.
Step 2A Prong 2: the additional limitation submitting the query to a search engine to obtain search results and summaries; is a process that doesn’t add anything significantly more to the abstract idea beyond just applying a generic computer method to the abstract idea– see MPEP 2106.05(f) Examiner’s note: high level recitation of searching on a search engine
And extracting the data from the search results and the summaries insignificant extra-solution activity that does not amount to an inventive concept, particularly when the activity is well-understood or conventional – see MPEP 2106.05(g) Examiner’s note: extracting the data from search results and summaries amounts to nothing more than selecting a particular data source or type of data to be manipulated
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitations submitting the query to a search engine to obtain search results and summaries; does not add significantly more to the abstract idea to make it an inventive concept; and extracting the data from the search results and the summaries does not add significantly more to the abstract idea to make it an inventive concept – see Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)
Regarding Claim 17
Step 1: A system
Step 2A Prong 1: Assigning authority scores to the data sources based upon authoritativeness of the data sources is a mental process that can be done with the aid of pen and paper, a person can arbitrarily assign a score to a data source based on its quality.
Processing the data from data sources to create candidate collections, wherein a candidate collection for a content item comprises a set of semantic information from a data source; sources is a mental process that can be done with the aid of pen and paper, a person can manually create a collection of data.
Utilizing the authority scores to select semantic information for the content item from the candidate collections; sources is a mental process that can be done with the aid of pen and paper, a person can arbitrarily select semantic information for content in a collection based off of a score.
Step 2A Prong 2: the additional limitations collecting data from data sources that provide at least one of reviews, search results, access to, or information about content items; insignificant extra-solution activity that does not amount to an inventive concept, particularly when the activity is well-understood or conventional – see MPEP 2106.05(g) Examiner’s note: collecting data from data sources that provide different information does not amount to anything more than mere data gathering.
And tagging the content item with the semantic information is a process that doesn’t add anything significantly more to the abstract idea beyond just applying a generic computer method to the abstract idea– see MPEP 2106.05(f) Examiner’s note: high level recitation of tagging content with information
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitations collecting data from data sources that provide at least one of reviews, search results, access to, or information about content items; does not add significantly more to the abstract idea to make it an inventive concept – see CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011); and tagging the content with the semantic information does not add significantly more to the abstract idea to make it an inventive concept – see Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015).
Regarding Claim 18
Step 1: A system
Step 2A Prong 1: recites the abstract ideas of claim 17
Step 2A Prong 2: The additional limitations processing the data from the data sources utilizing at least one of text processing, a deep learning model, N-grams or a text analysis platform is a process that doesn’t add anything significantly more to the abstract idea beyond just applying a generic computer method to the abstract idea– see MPEP 2106.05(f) Examiner’s note: high level recitation of processing data utilizing at least text processing, deep learning model, N-grams or a text analysis platform.
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation processing the data from the data sources utilizing at least one of text processing, a deep learning model, N-grams or a text analysis platform does not add significantly more to the abstract idea to make it an inventive concept – see Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015).
Regarding Claim 19
Step 1: A system
Step 2A Prong 1: recites the abstract ideas of claim 17
Step 2A Prong 2: the additional limitation individually processing each data source to create the candidate collections is a process that doesn’t add anything significantly more to the abstract idea beyond just applying a generic computer method to the abstract idea– see MPEP 2106.05(f) Examiner’s note: high level recitation of processing data sources to create a collection
Step 2B The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation individually processing each data source to create the candidate collections does not add significantly more to the abstract idea to make it an inventive concept – see Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015).
Regarding Claim 20
Step 1: A system:
Step 2A Prong 1: recites the abstract ideas of claim 17
Step 2A Prong 2: the additional limitation processing combinations of the data sources to create the candidate collections is a process that doesn’t add anything significantly more to the abstract idea beyond just applying a generic computer method to the abstract idea– see MPEP 2106.05(f) Examiner’s note: high level recitation of processing data to create a collection
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation processing combinations of the data sources to create the candidate collections does not add significantly more to the abstract idea to make it an inventive concept – see Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015).
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 –
Claims 1-2, 7, 13, 15, 17, 19, 20 are rejected under 102 as being unpatentable over Cho et al (NPL: What is Metacritic and Metascore?) (“Metacritic” “Cho”).
Regarding Claim 1, Metacritic teaches A method executing on a processor of a computer of a computing device that causes the computing device to perform operations comprising: collecting data from data sources that provide information about content items; ([What is Metacritic?] Details that Metacritic is a review aggregating site for movies, tv shows, and video games)
assigning authority scores to the data sources based upon authoritativeness of the data source; ([How are scores calculated?] teaches Metascore which selects different review outlets and convert them to a 100 point scale, and they then calculate the weighted average of each score based off of the quality and size of the outlet. Which teaches this limitation.)
processing the data from the data sources to create candidate collections, wherein a candidate collection for a content item comprises a set of semantic information corresponding to at least one of a category, an entity, or a term extracted from a data source; ([Platform] shows red dead redemption reviews for two different platforms, those platforms being the candidate collections in this case. Which would teach this limitation.)
utilizing the authority scores to select semantic information for the content item from the candidate collections ([Score Conversion] Metacritic has different meanings for the score depending on what range it falls under and that ranges for different platforms)
and performing a semantic-based action using the semantic information. ([platform] shows red dead redemption 2 being tagged with different platforms. With the PS4 reviews being based on 98 reviews and the Xbox One reviews being based on 33 reviews which is a semantic based action.)
Regarding Claim 2, Cho teaches all the limitations of Claim 1.
Cho also teaches modifying operation of an application based upon the semantic information, wherein the operation is modified to provide a user with a personalized experience while interacting with the application. ([Despite all this] details users accessing the site just to find games they may be interested in with high Metascores to confirm the quality of the title. Users accessing a site just for this purpose can be interpreted it as a personalized experience while interreacting with an application. The Metascore constantly adjusting with more reviews coming in can be interpreted it as modifying an operation based upon semantic information.)
Regarding Claim 7, Cho teaches determining authoritativeness for the data source based upon a data source type, a domain quality, and information comprehensiveness of information extracted from the data source ([How are scores calculated?] teaches Metascore which selects different review outlets and convert them to a 100 point scale, and they then calculate the weighted average of each score based off of the quality and size of the outlet. Which teaches this limitation.)
Regarding Claim 13, Cho teaches a non-transitory machine readable medium having stored thereon process-executable instructions that when executed cause performance of operations, the operations comprising: collecting data from data sources that provide at least one reviews, search results, access to, or information about content items; ([What is Metacritic?] Details that Metacritic is a review aggregating site for movies, tv shows, and video games)
assigning authority scores to the data sources based upon authoritativeness of the data sources; ([How are scores calculated?] teaches Metascore which selects different review outlets and convert them to a 100 point scale, and they then calculate the weighted average of each score based off of the quality and size of the outlet. Which teaches this limitation.)
processing the data from the data sources to create candidate collections, wherein a candidate collection for a content item comprises a set of semantic information from a data source; ([Platform] shows red dead redemption reviews for two different platforms, those platforms being the candidate collections in this case. Which would teach this limitation.)
utilizing the authority scores to select semantic information for the content item from the candidate collections; ([Score Conversion] Metacritic has different meanings for the score depending on what range it falls under and that ranges for different platforms)
And tagging the content with the semantic information ([platform] shows red dead redemption 2 being tagged with different platforms. With the PS4 reviews being based on 98 reviews and the Xbox One reviews being based on 33 reviews)
Regarding claim 15 Metacritic teaches collecting the data from content item review websites, and wherein the content item comprises at least one of applications, movies, videogames, videos, or shopping products ([What is Metacritic?] Metacritic is a review aggregation site that takes in reviews for video games, movies, and tv shows)
Regarding Claim 17, it is rejected according to the rejection of claim 13
Regarding Claim 19, Cho teaches all the limitations of Claim 17.
Metacritic also teaches individually processing each data source to create the candidate collections ([Platform] shows red dead redemption reviews for two different platforms, those platforms being the candidate collections in this case. Which would teach this limitation.)
Regarding claim 20, Metacritic teaches processing combinations of the data sources to create the candidate collections ([Platform] shows red dead redemption reviews for two different platforms, those platforms being the candidate collections in this case. Which would teach this limitation.)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 3-5 are rejected under 103 as being unpatentable over Cho (NPL: What is Metacritic and Metascore?) (“Cho”), in view of Recombee (NPL: Recombee) (“Recombee”)
Regarding Claim 3, Cho and Recombee teaches all the limitations of Claim 1.
Recombee also teaches wherein the performing the semantic-based action comprises: selecting content from available content to provide to a user based upon the content corresponding the semantic information; ([Page 3] Recombee partnered with Showmax to help create more personalized recommendation content to the user)
and displaying the content to the user through a display of a device ([Page 7] shows Recombee’s similar section which shows similar movies in the streaming service which teaches this limitation)
Recombee and Cho are analogous art because they both function as ways to recommend content to users.
It would have been obvious to a person skilled in the art before the effective filling data of the claimed invention to combine Cho with the personalized recommendation system of Recombee. Doing so would allow for a more personalized user experience ([Recombee Showmax] Showmax has chosen Recombee as a long-term strategic partner for personalization service).
Regarding Claim 4, Cho and Recombee teaches all the limitations of Claim 1.
Recombee also teaches wherein performing the semantic-based action comprises: generating content based upon the semantic information, wherein the content is tailored to an interest of a user; ([Page 5] Recombee through working with Showmax creates personalized media recommendations based off of their interests)
And displaying the content to the user through a display device ([Page 7] Recombee works with Showmax which is a streaming service for media)
Regarding Claim 5, Cho and Recombee teaches all the limitations of Claim 1.
Recombee also teaches wherein the performing the semantic-based action comprises: training a model using the semantic information; utilizing the model to select content from available content to provide to a user; and recommending the content to the user. ([Page 5] They are constantly optimizing their model to select content to recommend to a user with their Showmax partnership)
Claim 6 and 12 are rejected as being unpatentable over Cho (NPL: What is Metacritic and Metascore?) (“Cho”) in view of Google (NPL: Google Play) (“Google”)
Regarding Claim 6, Cho teaches all the limitations of Claim 1
Cho does not teach wherein the performing semantic-based action comprises: identifying an application that is similar to one or more applications utilized by a user, wherein the applications is identified as being similar to the one or more applications based upon the semantic information; and providing a recommendation of the application to the user.
However, Google teaches identifying an application that is similar to one or more applications utilized by a user, wherein the applications that is similar to one or more applications utilized by a user, wherein the applications is identified as being similar to the one or more applications based upon the semantic information (Google Play has a similar section to recommend similar apps to the ones you are currently interacting with on the platform)
And providing the recommendation of the application to the user (Google Play has a similar section to recommend similar apps to the ones you are currently interacting with on the platform)
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Cho and Google play are analogous art because they have a system in place centered around recommendations
It would have been to a person skilled in the art before the effective filling data of the claimed invention to combine Cho with the app recommendations of google play. Doing so would allow for a more uses to discover high quality apps ([Google Play: How Google Play Works] Google Play is a global digital content store that makes it easy for more than 2.5 billion monthly users across 190+ markets worldwide to discover millions of high-quality apps, games, books, and more. We connect people looking for apps and games they’ll love with the businesses who build them.).
Regarding Claim 12, Cho teaches all the limitations of Claim 1
Google also teaches tagging an application with the semantic information (Google play tags applications with genres that they would be associated with)
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Claim 8 is rejected as being unpatentable over Cho (NPL: What is Metacritic and Metascore?) (“Cho”) in view of Google (NPL: Advanced machine learning helps Play Store users discover personalised apps) (“Google”)
Regarding Claim 8, Cho teaches all the limitations of Claim 1
Cho does not teach wherein the data source comprise a mobile app data feed of a mobile app, and wherein the method comprises: identifying fields of interest from the mobile app data feed, wherein the fields of interest correspond to at least one of a title. A main category, a description, meta keywords, user reviews, ore related applications for the mobile app, wherein the fields of interest are identified using at least one of a field-specific markup language xpaths, a layout based machine learning model, or structural information; and processing the fields of interest to create the candidate collection
However, Google does teach wherein the data source comprise a mobile app data feed of a mobile app, and wherein the method comprises: identifying fields of interest from the mobile app data feed, wherein the fields of interest correspond to at least one of a title. A main category, a description, meta keywords, user reviews, ore related applications for the mobile app, wherein the fields of interest are identified using at least one of a field-specific markup language xpaths, a layout based machine learning model, or structural information; (Google play is a mobile platform used on androids to download apps and the paper details how google uses different machine learning techniques to recommend apps to users)
And processing the fields of interest to create the candidate collection (Google play in their recommendations has different collections of recommendations that a user may be interested in)
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Cho and Google are analogous art because they have a system in place centered around recommendations.
It would have been to a person skilled in the art before the effective filling data of the claimed invention to combine Cho with the app recommendations of google play. Doing so would allow for a more personalized user experience ([Google: Our Collaboration with the Google Play Store] “We know users get the most out of their phone when they have apps and games they love, and that it’s exciting to discover new favourites. In collaboration with Google Play, our team that leads on collaborations with Google has driven significant improvements in the Play Store's discovery systems, helping to deliver a more personalised and intuitive Play Store experience for users.”).
Claim 9 is rejected as being unpatentable over Cho (NPL: What is Metacritic and Metascore?) (“Cho”) in view of Halimsaputera (US20200342550A1) (“Halimsaputera”)
Regarding Claim 9, Cho teaches all the limitations of Claim 1
Cho does not teach comprising: filtering from the candidate collections, blacklisted entries.
However, Halimsaputera teaches comprising: filtering from the candidate collections, blacklisted entries ([abstract] This teaches matching restaurants with the users’ dietary restrictions which functions as a blacklist for restaurants. That teaches this limitation)
Cho and Halimsaputera are analogous art because they are all focused on user recommendations
It would have been to a person skilled in the art before the effective filling data of the claimed invention to combine Cho with the filtering technology of Halimsaputera. Doing so would allow for users to be presented with options to meet their specific needs ([Halimsaputera-Background] “There is therefore an unmet need for an application that provides all of the desired information, presenting individuals with options that meet their individual preferences and dietary restrictions, and are continually updated to reflect changes in both restaurant offerings and personal preferences”).
Claim 10 is rejected under 103 as being unpatentable Cho (NPL: What is Metacritic and Metascore?) (“Cho”), in view of Boxofficemojo (NPL: Boxofficemojo) (“Boxofficemojo”)
Regarding Claim 10, Cho teaches all the limitations of Claim 1.
Cho also teaches the limitation assigning ranks to the candidate collections to create ranked candidate collections based upon at least one of relevancy, frequency, or uniqueness of semantic information within the candidate collections (Metacritic posts a publisher ranking of the best and worst performing studio games which teaches this limitation)
Cho does not teach Selecting a subset of the ranked candidate collections based upon the ranks; and selecting the semantic information for the content item using the subset of the ranked candidate collections
However, Boxofficemojo does teach Selecting a subset of the ranked candidate based upon the ranks (Boxofficemojo allows you to search by genres with a toggle on the list to rank the genres by box office totals or number of titles)
And selecting the semantic information for the content item using the subset of the ranked candidate collections (The Boxofficemojo genres list is interactive and you can click on the titles in the genres which teaches the limitation of selecting semantic information. The different genres of movies are then ranked which would teach the limitation of the ranked candidate collections)
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Cho and Boxofficemojo are analogous art because they are media based platforms targeted towards users.
It would have been obvious to a person skilled in the art before the effective filling data of the claimed invention to combine Cho with the semantic information selection of ranked candidate collections of Boxofficemojo. Doing so would provide users another way to gauge the quality of a piece of media ([About Box Office Mojo] Box Office Mojo is an online movie publication and box office reporting service. Our purpose is to illuminate the movies through the integration of art and business. Based in Burbank, California, we produce analysis, reviews, interviews and the most comprehensive box office tracking available online).
Claim 11 is rejected under 103 as being unpatentable Cho (NPL: What is Metacritic and Metascore?) (“Cho”), in view of Boxofficemojo (NPL: Boxofficemojo) (“Boxofficemojo”) and Icon-era (NPL: Icon-era) (“Icon-era”)
Regarding Claim 11, Cho and Boxofficemojo teach all the limitations of Claim 10.
Cho does not teach creating weighted combinations of categories, entities, and terms using the subset of the ranked candidate collections and the authority scores; and selecting the semantic information for the content item using the weighted combinations
However, Icon-era teaches creating weighted combinations of categories, entities, and terms using the subset of the ranked candidate collections and the authority scores (Icon-era shows a screenshot of a post on Metacritic’s top publishers of the year list in 2023. It is a list were Metacritic lists their top publishers using their Metascore metric which is a weighted average);
and selecting the semantic information for the content item using the weighted combinations. (Icon-era shows a screenshot of a post on Metacritic’s top publishers of the year list in 2023. Metacritic links the best performing titles from each different publisher in the rankings based off of their Metascore. In the figure it shows Sony entertainment and they link God of War Ragnarök in the article since it was the best performing title based off of the Metascore. Which would teach this limitation).
Cho, Icon-era, and Boxofficemojo are analogous art because they focus on media based platforms targeted towards users.
It would have been obvious to a person skilled in the art before the effective filling data of the claimed invention to combine Cho with the semantic information selection of ranked candidate collections of Boxofficemojo, and the ranked candidate collections list of icon-era. Doing so would provide users another way to gauge the quality of a piece of media ([About Box Office Mojo] Box Office Mojo is an online movie publication and box office reporting service. Our purpose is to illuminate the movies through the integration of art and business. Based in Burbank, California, we produce analysis, reviews, interviews and the most comprehensive box office tracking available online).
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Claim 14 is rejected under 103 as being unpatentable over Cho (NPL: What is Metacritic and Metascore?) (“Cho”), in view of Vyas et al (NPL: Understanding the Mobile Game App Activity) (“Vyas”)
Regarding Claim 14, Cho teaches all the limitations of Claim 13.
Cho does not teach wherein the collecting comprises: collecting the data from app store pages of an app store, wherein the data comprises titles, reviews, and descriptions of applications available and wherein the content items comprise the applications
However, Vyas teaches wherein the collecting comprises: collecting the data from app store pages of an app store, wherein the data comprises titles, reviews, and descriptions of applications available and wherein the content items comprise the applications ([implementation] teaches collecting data that includes title, reviews, and summary which functions as the description in the limitation)
Cho and Vyas are analogous art because they both focus on collecting online data from user interactions.
It would have been to a person skilled in the art before the effective filling data of the claimed invention to combine Cho with the mobile app scraping of Vyas. Doing so would allow for developers to look at different platforms to develop more successful games ([Introduction] “In this paper, we will deal with web scraping and analyzing data from Google Play Store for Game based Apps to provide insights on developing games which lead to high user interaction and high ratings”).
Claim 16 is rejected as being unpatentable over Cho (NPL: What is Metacritic and Metascore?) (“Cho”), in view of Vu et al (NPL: Mining User Opinions in Mobile App Reviews: A Keyword-based Approach) (“Vu”)
Regarding Claim 16, Cho teaches all the limitations of Claim 13.
Cho does not teach wherein the collecting comprises: generating a query that includes a mobile platform and keywords relating to at least one of ratings and reviews; submitting the query to a search engine to obtain search results and summaries; and extracting the data from the search results and the summaries
However, Vu teaches wherein the collecting comprises: generating a query that includes a mobile platform and keywords relating to at least one of ratings and reviews; submitting the query to a search engine to obtain search results and summaries; and extracting the data from the search results and the summaries ([Abstract] describes a framework that takes in user keywords and associates them with positive or negative reviews)
Cho and Vu are analogous art because they both focus on providing users with data for user recommendations
It would have been to a person skilled in the art before the effective filling data of the claimed invention to combine Cho with the mobile app review association of Vu. Doing so would make identifying relevant reviews easier ([Abstract] —User reviews of mobile apps often contain com plaints or suggestions which are valuable for app developers to improve user experience and satisfaction. However, due to the large volume and noisy-nature of those reviews, manually analyzing them for useful opinions is inherently challenging. To address this problem, we propose MARK, a keyword-based framework for semi-automated review analysis. MARK allows an analyst describing his interests in one or some mobile apps by a set of keywords. It then finds and lists the reviews most relevant to those keywords for further analysis. It can also draw the trends over time of those keywords and detect their sudden changes, which might indicate the occurrences of serious issues. To help analysts describe their interests more effectively, MARK can automatically extract keywords from raw reviews and rank them by their associations with negative reviews. In addition, based on a vector-based semantic representation of keywords, MARK can divide a large set of keywords in to more cohesive subsets, or suggest keywords similar to the selected ones).
Claim 18 is rejected as being unpatentable over Cho (NPL: What is Metacritic and Metascore?) (“Cho”), in view of Baker et al (US9223831B2) (“Baker”) and Li et al (NPL: Learning Document Embeddings by Predicting N-Grams for Sentiment Classification of Long Movie Reviews) (“Li”)
Regarding Claim 18, Cho teaches all the limitations of Claim 17
Metacritic does not teach processing the data from the data sources utilizing at least one of text processing, a deep learning model, N-grams, or a text analysis platform
However, Baker teaches processing the data from the data sources utilizing at least one of text processing (Fig 2B describes process of pulling from an app store website and processing the data)
Or a text analysis platform (Summary describes an analysis algorithm used with the supervised ML to present more informative summaries of apps based off app reviews which teaches this limitation)
Li teaches a deep learning model, N-grams ([Abstract] Discusses how their model outperforms deep learning models and bag-of-ngram models when it comes to capturing the semantics and word order in a IMDB movie review data set)
Cho, Baker, and Li are analogous art because they all are centered around reviews of media.
It would have been to a person skilled in the art before the effective filling data of the claimed invention to combine Cho, with the text processing and text analysis of Baker with the deep learning and n-grams processing of Li. Doing so would allow for highlighting of positive and negative features present in an application ([Baker: Background of invention] “These applications do not disclose comparing and contrasting different mobile applications using statistical analysis or other computing methods to highlight the most positive and most negative features of a mobile application as determined by multiple reviewers, and to quantify the ratings of the particular features; as well as to provide separate displays of reviews by professional information technology reviewers versus non-technical user reviewers.
Neither do these systems provide a cross-referencing feature to display other mobile applications: 1) that a reviewer rated as highly as the application that the user is investigating in order for them to comparison shop; nor 2) that a reviewer who gave a negative rating to the user's application of interest, alternatively rated other applications highly in order for the user to find a better performing application”).
Conclusion
The prior art made of record and relied upon is considered to applicant’s disclosure
Wilson et al US 20240078586 A1 (2023-05-04) (Abstract “In selected embodiments a recommendation generator builds a network of interrelationships between venues, reviewers and users based on their attributes and reviewer and user reviews of the venues. Each interrelationship or link may be positive or negative and may accumulate with other links (or anti-links) to provide nodal links the strength of which are based on commonality of attributes among the linked nodes and/or common preferences that one node, such as a reviewer, expresses for other nodes, such as venues. The links may be first order (based on a direct relationship between, for instance, a reviewer and a venue) or higher order (based on, for instance, the fact that two venue are both liked by a given reviewer). The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by aggregating the link matrices and determining the venues which are most strongly coupled to the user.”)
Kato US 9584586 B2 (2014-05-01) (Abstract “An information processing apparatus is disclosed which includes: a storing element for storing content data; a managing element for managing preference data by which to determine preferences of a user; a reading element for reading the content data from the storing element in response to an instruction from the user; a sorting element for sorting the content data read by the reading element, in accordance with the preference data managed by the managing element; and a composing element for composing the content data sorted by the sorting element, into a single item ready to be handled by the user.”)
Hamza et al Google Play Content Scraping and Knowledge Engineering using Natural Language Processing Techniques with the Analysis of User Reviews (July 2020) (Abstract “To maintain the competitive edge and evaluating the needs of the quality app is in the mobile ap plication market. The user’s feedback on these applications plays an essential role in the mobile application development industry. The rapid growth of web technology gave people an opportunity to interact and ex press their review, rate and share their feedback about applications. In this paper we have scrapped 506259 of user reviews and applications rate from Google Play Store from 14 different categories. The statistical information was measured in the results using different of common machine learning algorithms such as the Logistic Regression, Random Forest Classifier, and Multinomial Naïve Bayes. Different parameters including the accuracy, precision, recall, and F1 score were used to evaluate Bigram, Trigram, and N-gram, and the statistical result of these algorithms was compared. The analysis of each algorithm, one by one, is performed, and the result has been evaluated. It is concluded that logistic regression is the best algorithm for review analysis of the Google Play Store applications. The results have been checked scientifically, and it is found that the accuracy of the logistic regression algorithm for analyzing different reviews based on three classes, i.e., positive, negative, and neutral.)
Ozcan et al US20220414741A1 (2022-08-26) ([Abstract] “There is disclosed a method and system for engaging in a dialog with a user. The dialog system may receive input from the user. The dialog system may determine text for responding to the user. The dialog system may determine products to recommend to the user. The dialog system may generate a summary of reviews corresponding to the products. A response may be output to the user based on the text for responding to the user, the products to recommend to the user, and the summary of reviews corresponding to the products.”)
Kouritzin et al US9693086B2 (2015-11-23) ([Abstract] “A targeted advertising system selects an asset (e.g., ad) for a current user of a user equipment device (e.g., a digital set top box in a cable network). The system can first operate in a learning mode to receive user inputs and develop evidence that can characterize multiple users of the user equipment device audience. In a working mode, the system can process current user inputs to match a current user to one of the identified users of that user equipment device audience. Fuzzy logic and/or stochastic filtering may be used to improve development of the user characterizations, as well as matching of the current user to those developed characterizations. In this manner, targeting of assets can be implemented not only based on characteristics of a household but based on a current user within that household.”)
Nice et al US20160140643A1 (2016-05-19) ([Abstract] “Example apparatus and methods access multiple sources of information concerning features for applications, clean the data from the multiple sources, extract features from the cleaned data, selectively weight the sources, data or extracted features and produce a feature vector. The feature vector may then be used in a single language feature space or in a multi-language feature space. Feature spaces may then be used to find similarities between applications to facilitate recommending applications. In one embodiment, different feature spaces may be connected using a graph where nodes represent items and edges represent similarity relationships between items based on related feature spaces. Traversing the graph may allow similarities to be found that might not otherwise be possible. For example, while there may be no direct English to Hebrew similarity relationship, there may be English to French and French to Hebrew relationships that can be followed in the graph.”)
Liu et al US10861077B1 (2020-12-08) ([Abstract] “A recommendation system increases the diversity of item recommendations provided to a target user by using machine learning to generate rules for identifying cross-category collections of items. For example, a first machine learning technique can be used to generate combination rules, representing categories of items frequently bought together, and these rules can be applied to generate a listing of cross-category seed item-recommended item pairs. These item pairs can be passed through a set of validation rules, generated by a second machine learning technique and representing correlations between attributes of items frequently bought together, to generate a confidence score representing the likelihood that a customer will want to purchase those two items together. The confidence score can be based on correlating one or more of color, price, seasonality, freshness, brand affinity, customer reviews, or item detail page views of the two items.”)
Si et al US11669915B1 2023-06-06 ([Abstract] “Systems, methods, and non-transitory computer-readable media can identify a set of accounts, each account of the set of accounts having a number of followers. The set of accounts are grouped into a plurality of groups based on number of followers, wherein each group is associated with a value score. A machine learning model is trained using a set of training data comprising account recommendation conversion information, wherein the account recommendation conversion information comprises a plurality of successful account recommendations, and each successful account recommendation is assigned a weight based on the value scores associated with the plurality of groups. One or more accounts of the set of accounts are selected to present as account recommendations based on the machine learning model.”)
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/Mariela Reyes/ Supervisory Patent Examiner, Art Unit 2142
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