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 .
Response to Arguments
Claim Rejections - 35 USC § 112
Applicant’s arguments, see 1, 7, 13, and 17, filed 08/11/2026, with respect to 112(b) have been fully considered and are persuasive. The 112(b) of 1, 7, 13, and 17 has been withdrawn.
Claim Rejections - 35 USC § 101
Applicants’ arguments filed 08/11/2026 have been fully considered but they are not persuasive. The applicant argues that “Amended claim 1 is not directed merely to evaluating information or making
mental judgment. Rather, claim 1 recites a specific computer-implemented technique for extracting and selecting semantic information from multiple heterogeneous data sources. In particular, claim 1 requires individually processing each data source using a technique determined based upon the data source type of the corresponding data source to create source-specific candidate collections comprising semantic-information candidates extracted from those respective data sources. The claim then utilizes authority scores to select semantic information from the resulting candidate collections before performing a semantic-based action. The amendment therefore recites a particular technological process for generating semantic information from heterogeneous sources, rather than a generalized concept of collecting information and making a determination based on that information.
Under Step 2A, Prong Two of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claim integrates any alleged abstract idea into a practical application. The claim does not merely recite a desired result, but instead specifies how semantic information is generated and selected, including source-type-specific processing and creation of source-specific candidate collections. Similar to the claims found eligible in
McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1313-16 (Fed. Cir. 2016), the claim recites a specific technique for automatically producing a result through a defined processing framework, rather than simply invoking a computer as a tool to perform an otherwise abstract process.
The claim is also distinguishable from Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1353-55 (Fed. Cir. 2016). The claims in Electric Power Group were directed to collecting information, analyzing the information, and displaying certain results of that analysis. By contrast, amended claim 1 recites a specific manner of processing heterogeneous data sources, including selecting processing techniques based on source type and generating source-specific candidate collections from which semantic information is selected. The claim is therefore directed to a particular technological implementation for semantic-information extraction and selection, rather than merely collecting information, analyzing it, and reporting the results.
Even assuming arguendo that the Office were to conclude that claim 1 recites an abstract idea, the claim recites significantly more under Step 2B. The ordered combination of source-type-based processing, source-specific candidate collection generation, and authority-score-based semantic-information selection defines a particular processing architecture for handling heterogeneous data sources. The claim therefore recites a specific and non-generic implementation rather than merely applying an abstract idea on generic computer components. Such a non-conventional arrangement of processing operations is consistent with the eligibility principles discussed in BASCOM Global Internet Services, Inc. v. AT&T Mobility LLC, 827 F.3d 1341, 1350-52 (Fed. Cir. 2016).
Accordingly, claim 1 is believed to overcome the 101 rejection.”
Examiner respectfully disagrees. Data source types used in the specification can be anything from user reviews to search results [0045]. Examiner disagrees with the applicant’s argument that the claim language, as currently presented, is different from Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1353-55 (Fed. Cir. 2016). The claim as currently written can be boiled down to collecting information, analyzing that information, and displaying the results which is similar to Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1353-55 (Fed. Cir. 2016). The use of “a technique determined based upon a data source type” does not add enough to the limitation to make it less broad and more specific due to how broadly data source types are being recited in the claims and the spec.
Claim Rejections - 35 USC § 102
Applicant’s arguments, see Claim 1, filed 08/11/2026, with respect to the rejection(s) of claim(s) 1-2, 7, 13, 15, 17, and 19-20 under 102(a)(1) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of new art.
Claim Rejections - 35 USC § 103
Applicant’s arguments with respect to claim(s) 3-5, 6, 8-12, 14, 16, 18 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Regarding Claim 1
Step 1: “A method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: assigning authority scores to the data sources points to mental process that could be done with the aid of pen and paper, a person can assign a score to a data source
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 a score to select information for items
And performing a semantic-based action using the semantic information is a mental process that can be done with the aid of pen and paper, a person can perform a semantic based action based off of information
Step 2A Prong 2: The additional limitations collecting data from sources that provide information about content items; is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g). Examiner’s note: collecting data from sources that provide information in this case amounts to nothing more than mere data gathering
processing the data from the data sources to create candidate collections) wherein the processing comprises individually processing each data source, using a technique determined based upon a data source type of the corresponding data source, to create a source-specific candidate collection comprising semantic information candidates extracted from the corresponding data source, 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 is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) – examiners note: high level recitation of using different techniques to process data to create a collection of 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 limitations collecting data from sources that provide information about content items; is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g). Examiner’s note: collecting data from sources that provide information in this case amounts to nothing more than mere data gathering
processing the data from the data sources to create candidate collections) wherein the processing comprises individually processing each data source, using a technique determined based upon a data source type of the corresponding data source, to create a source-specific candidate collection comprising semantic information candidates extracted from the corresponding data source, 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 is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) – examiners note: high level recitation of using different techniques to process data to create a collection of data
Regarding Claim 2
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
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 an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) - 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 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 an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) - examiner’s note: high level recitation of modifying an application based on semantic information.
Regarding Claim 3
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
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 an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) - 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 an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) 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 limitations selecting content from available content to provide to a user based upon the content corresponding to the semantic information is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) - 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 an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) Examiner’s note: high level recitation of selecting and displaying content based off criteria
Regarding Claim 4
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
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
Step 2A Prong 2: The additional limitation and displaying the content to the user through a display of a device is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05 – 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 and displaying the content to the user through a display of a device is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05 – see MPEP 2106.05(g). Furthermore, the additional element is directed to transmitting data over a network, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II). Examiner’s note: displaying content to a user here is an insignificant extra-solution.
Regarding Claim 5
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
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; are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) - Examiner’s note: high level recitation of training a model
Utilizing the model to select content from available content to provide to a user are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) - Examiner’s note: high level recitation of using a model to select content for a user
And recommending the content to the user is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) - 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; are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) - Examiner’s note: high level recitation of training a model
Utilizing the model to select content from available content to provide to a user are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) - Examiner’s note: high level recitation of using a model to select content for a user
And recommending the content to the user is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) - Examiner’s note: high level recitation of recommending content to a user
Regarding Claim 6
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
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 look at application and identify similar application based on information
Step 2A Prong 2: The additional limitation and providing a recommendation to the application to the user is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) - 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 is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) - Examiner’s note: high level recitation of recommending content to a user
Regarding Claim 7
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater). Step 2A Prong 1: wherein the assigning the authority scores is based upon 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 assign a score based on 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: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
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, a person can look at fields and create collections from them.
Step 2A Prong 2: 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; is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) - Examiner’s note: high level recitation of identifying fields of interests for a user using either machine learning, markup language xpaths, or strucutal 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; is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) - Examiner’s note: high level recitation of identifying fields of interests for a user using either machine learning, markup language xpaths, or structural information.
Regarding Claim 9
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: recites the abstract ideas of claim 1
Step 2A Prong 2: the additional limitation filtering, from the candidate collections, blacklisted entries is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) - Examiner’s note: high level recitation of filtering blacklisted 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 is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) - Examiner’s note: high level recitation of filtering blacklisted entries from data
Regarding Claim 10
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
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 collection of data
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, a person can select a subset based off of rankings
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 additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 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 is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g). Furthermore, the additional element is directed to electronic recordkeeping, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II) - 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.
Regarding Claim 11
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
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 an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) - 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 an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).) 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 is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) - 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 an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).) Examiner’s note: high level recitation of selecting information from the weighted combinations
Regarding Claim 12
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
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 an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) - 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 the semantic information is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) - Examiner’s note: high level recitation of tagging content with information
Regarding Claim 13
Step 1: “A non-transitory machine readable medium” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: Assigning authority scores to the data sources is a mental process that can be done with the aid of pen and paper, a person can assign a score to 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 scores to help select information for an item
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; is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g)– 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.
processing the data from the data sources to create candidate collections wherein the processing comprises individually processing each data source, using a technique determined based upon a data source type of the corresponding data source, to create a source-specific candidate collection, 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 is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) – examiners note: high level recitation of using different techniques to process data to create a collection of data
And tagging the content item with the semantic information. is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) - 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; is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g)– see MPEP 2106.05(g). Furthermore, the additional element is directed to receiving data over a network, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II). - 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.
processing the data from the data sources to create candidate collections wherein the processing comprises individually processing each data source, using a technique determined based upon a data source type of the corresponding data source, to create a source-specific candidate collection, 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 is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) – examiners note: high level recitation of using different techniques to process data to create a collection of data
And tagging the content item with the semantic information. is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) - Examiner’s note: high level recitation of tagging content with information
Regarding Claim 14
Step 1: “The non-transitory machine readable medium” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
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 is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 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 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 is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g). Furthermore, the additional element is directed to receiving data over a network, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II) - 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
Regarding Claim 15
Step 1: “The non-transitory machine readable medium” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
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 is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 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 is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g). Furthermore, the additional element is directed to receiving data over a network, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II) - Examiner’s note: collecting data from content amounts to nothing more than mere data gathering
Regarding Claim 16
Step 1: “The non-transitory machine readable medium” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
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 generate a query including keywords and a mobile platform
Step 2A Prong 2: the additional limitations submitting the query to a search engine to obtain one or more search results and one or more summaries is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2). Examiner’s note: high level recitation of searching on a search engine
And extracting the data from the one or more search results and the one or more summaries is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 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 one or more search results and one or more summaries is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2). Examiner’s note: high level recitation of searching on a search engine
And extracting the data from the one or more search results and the one or more summaries is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g). Furthermore, the additional element is directed to retrieving information in memory, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II) - 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
Regarding Claim 17
Step 1: “A computing device” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: Assigning authority scores to the data sources is a mental process that can be done with the aid of pen and paper, a person can assign a score to a data source
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 use scores to select information
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; is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 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.
processing the data from the data sources to create candidate collections wherein the processing comprises processing a data source, using a technique determined based upon a data source type of the data source, to create a candidate collection for a content item comprising a set of semantic information from a data source is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) – examiners note: high level recitation of using different techniques to process data to create a collection of data
And tagging the content item with the semantic information is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) - 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; is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g). Furthermore, the additional element is directed to receiving data over a network, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II). - Examiner’s note: collecting data from data sources that provide different information does not amount to anything more than mere data gathering.
processing the data from the data sources to create candidate collections wherein the processing comprises processing a data source, using a technique determined based upon a data source type of the data source, to create a candidate collection for a content item comprising a set of semantic information from a data source is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) – examiners note: high level recitation of using different techniques to process data to create a collection of data
And tagging the content item with the semantic information is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) - Examiner’s note: high level recitation of tagging content with information
Regarding Claim 18
Step 1: “The computing device” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
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 are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) - 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.
Regarding Claim 19
Step 1: “The computing device” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: recites the abstract ideas of claim 17
Step 2A Prong 2: the additional limitation processing a second data source, using a second technique determined based upon a second data source type of the second data source, to create a second candidate collection is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) - Examiner’s note: high level recitation of processing data sources with another technique
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 a second data source, using a second technique determined based upon a second data source type of the second data source, to create a second candidate collection is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) - Examiner’s note: high level recitation of processing data sources with another technique
Regarding Claim 20
Step 1: “The computing device” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
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 an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) - 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 is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) - Examiner’s note: high level recitation of processing data to create a collection
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-2, 7, 13, 15, 17, and 19-20 are rejected under U.S.C 130 as being unpatentable over Cho et al (NPL: What is Metacritic and Metascore?) (“Metacritic” “Cho”) in view of Temidayo Omoniyi (NPL: Connect Power BI to Multiple Data Sources (Cloud & On-premises)) (“Omoniyi”)
Regarding Claim 1, Cho teaches a method executing on a processor of a computing device that causes the computing device to perform operations comprising: collecting data from data sources that provide information about content items ([Page 1] teaches that Metacritic is a review aggregating site for movies, tv shows, and video games)
assigning authority scores to the data sources ([Page 2-3] 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.)
utilizing the authority scores to select semantic information for the content item from the candidate collections; ([Page 4-5] 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 ([Page 5-6] 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.)
Cho does not teach processing the data from the data sources to create candidate collections wherein the processing comprises individually processing each data source, using a technique determined based upon a data source type of the corresponding data source, to create a source-specific candidate collection comprising semantic information candidates extracted from the corresponding data source, 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
However, Omoniyi does teach processing the data from the data sources to create candidate collections wherein the processing comprises individually processing each data source, using a technique determined based upon a data source type of the corresponding data source, to create a source-specific candidate collection comprising semantic information candidates extracted from the corresponding data source, 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 ([Page 2] teaches different data formats being used on their platform and those different data formats being transformed with power query. It can be reasonably interpreted that when handling a data format like a csv vs a pdf that a different technique is when processing those documents. [page 5] teaches a table that is created from the multiple different data source types containing information that a user may want to analyze. The different columns of categories is being interpreted as the source-specific candidate with the information contained in those columns being the semantic information extracted from the data sources teaching this limitation.)
Cho and Omoniyi are analogous art because they both deal with providing insights into the data
It would have been obvious to combine Cho with the multiple data source handling of Omoniyi. Doing so allow for a more streamlined data analysis process ([Omoniyi-Page 1])
Regarding Claim 2, the combination of Cho and Omoniyi teaches all the limitations of Claim 1
The combination of Cho and Omoniyi 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. ([Page 9] 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 as a personalized experience while interreacting with an application. The Metascore constantly adjusting with more reviews coming in can be interpreted as modifying an operation based upon semantic information.)
Regarding Claim 7, the combination of Cho and Omoniyi teaches all the limitations of Claim 1
Cho also teaches wherein the assigning the authority scores is based upon a domain quality and information comprehensiveness of information extracted from the data source ([Page 2-3] 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, The combination of Cho and Omoniyi teaches a non-transitory machine readable medium having stored thereon processor-executable instructions that when executed cause performance of operations, the operations comprising: collecting data from data sources that provide at least one of reviews, search results, access to, or information about content items ([Cho-Page 1] Details that Metacritic is a review aggregating site for movies, tv shows, and video games)
assigning authority scores to the data sources ([Cho: Page 2-3] 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 the processing comprises individually processing each data source, using a technique determined based upon a data source type of the corresponding data source, to create a source-specific candidate collection, 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 ([Omoniyi-Page 2] teaches different data formats being used on their platform and those different data formats being transformed with power query. It can be reasonably interpreted that when handling a data format like a csv vs a pdf that a different technique is when processing those documents. [page 5] teaches a table that is created from the multiple different data source types containing information that a user may want to analyze. The different columns of categories is being interpreted as the source-specific candidate with the information contained in those columns being the semantic information extracted from the data sources teaching this limitation)
utilizing the authority scores to select semantic information for the content item from the candidate collections ([Cho-Page 4] Metacritic has different meanings for the score depending on what range it falls under and that ranges for different platforms)
and tagging the content item with the semantic information ([Cho: Page 5-6] 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, The combination of Cho and Omoniyi teaches all the limitations of Claim 13
Cho also 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 ([Page 1] Metacritic is a review aggregation site that takes in reviews for video games, movies, and tv shows)
Regarding Claim 17, see the analysis of Claim 13
Regarding Claim 19, The combination of Cho and Omoniyi teaches all the limitations of Claim 17
Omoniyi also teaches wherein the processing comprises: processing a second data source, using a second technique determined based upon a second data source type of the second data source, to create a second candidate collection ([Page 2] teaches different data formats being used on their platform and those different data formats being transformed with power query. It can be reasonably interpreted that when handling a data format like a csv vs a pdf that a different technique is when processing those documents. [page 5] teaches a table that is created from the multiple different data source types containing information that a user may want to analyze. Different data source type would utilize different connectors to link the sources to the Power BI table; these different connectors are being interpreted as second technique for a second data source [Page 5]. The different columns of categories is being interpreted as the source-specific candidate with the information contained in those columns being the semantic information extracted from the data sources teaching this limitation.)
Regarding Claim 20, The combination of Cho and Omoniyi teaches all the limitations of Claim 17
Cho also teaches processing combinations of the data sources to create the candidate collections ([Page 5-6] shows red dead redemption reviews for two different platforms, those platforms being the candidate collections in this case. Which would teach this limitation.)
Claims 3-5 are rejected under U.S.C. 103 as being unpatentable over Cho et al (NPL: What is Metacritic and Metascore?) (“Metacritic” “Cho”) in view of Temidayo Omoniyi (NPL: Connect Power BI to Multiple Data Sources (Cloud & On-premises)) (“Omoniyi”), and Recombee (NPL: Recombee) (“Recombee”)
Regarding Claim 3, The combination of Cho and Omoniyi teaches all the limitations of Claim 1
The combination of Cho and Omoniyi does not teach 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;
and displaying the content to the user through a display of a device
However, Recombee does teach 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)
Cho, Omoniyi, and Recombee are all analogous art because they all deal with providing insights on data
It would have been obvious to combine Cho with the multiple data source handling of Omoniyi and the personalized recommendation system of Recombee. Doing so would allow for a more personalized user experience ([Recombee-Showmax]).
Regarding Claim 4, The combination of Cho and Omoniyi teaches all the limitations of Claim 1
The combination of Cho and Omoniyi does not teach 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;
However, Recombee does teach 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)
Regarding Claim 5, the combination of Cho and Omoniyi teaches all the limitations of Claim 1
The combination of Cho and Omoniyi does not teach 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.
However, Recombee does teach 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)
Claims 6 and 12 are rejected as being unpatentable under U.S.C. 103 over Cho et al (NPL: What is Metacritic and Metascore?) (“Metacritic” “Cho”) in view of Temidayo Omoniyi (NPL: Connect Power BI to Multiple Data Sources (Cloud & On-premises)) (“Omoniyi”), and Google (NPL: Google Play) (“Google”)
Regarding Claim 6, the combination of Cho and Omoniyi teaches all the limitations of Claim 1
The combination of Cho and Omoniyi 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 does teach 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, Omoniyi, and Google are all analogous art because they all deal with providing insights on data
It would have been obvious to combine Cho with the multiple data source handling of Omoniyi and the app recommendations of Google. Doing so would allow for more users to discover high quality apps ([Google Play-How Google Play Works])
Regarding Claim 12, The combination of Cho and Omoniyi teaches all the limitations of Claim 1
The combination of Cho and Omoniyi does not teach tagging an application with the semantic information
However, Google does teach 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 under U.S.C. 103 over Cho et al (NPL: What is Metacritic and Metascore?) (“Metacritic” “Cho”) in view of Temidayo Omoniyi (NPL: Connect Power BI to Multiple Data Sources (Cloud & On-premises)) (“Omoniyi”), and Google (NPL: Advanced machine learning helps Play Store users discover personalised apps) (“Google”)
Regarding Claim 8 the combination of Cho and Omoniyi teaches all the limitations of Claim 1
The combination of Cho and Omoniyi 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, Omoniyi, and Google are all analogous art because they all deal with providing insights on data
It would have been obvious to combine Cho with the multiple data source handling of Omoniyi and the app recommendations of Google. Doing so would allow for a more personalized user experience ([Google: Our Collaboration with the Google Play Store])
Claim 9 is rejected as being unpatentable under U.S.C 103 over Cho et al (NPL: What is Metacritic and Metascore?) (“Metacritic” “Cho”) in view of Temidayo Omoniyi (NPL: Connect Power BI to Multiple Data Sources (Cloud & On-premises)) (“Omoniyi”), and Halimsaputera (US20200342550A1) (“Halimsaputera”)
Regarding Claim 9, the combination of Cho and Omoniyi teaches all the limitations of Claim 1
The combination of Cho and Omoniyi 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, Omoniyi, and Halimsaputera are all analogous art because they all deal with providing insights into the data
It would have been obvious to combine Cho with the multiple data source handling of Omoniyi and the filtering technology of Halimsaputera. Doing so would allow for users to be presented with options to meet their specific needs ([Halimsaputera-Background])
Claim 10 is rejected as being unpatentable under U.S.C 103 over Cho et al (NPL: What is Metacritic and Metascore?) (“Metacritic” “Cho”) in view of Temidayo Omoniyi (NPL: Connect Power BI to Multiple Data Sources (Cloud & On-premises)) (“Omoniyi”), Darryn Bonthuys (NPL: Sony Is Metacritic’s Highest-Rated Publisher Of 2022) (“Bonthuys”) and Boxofficemojo (NPL: Boxofficemojo) (“Boxofficemojo”)
Regarding Claim 10, the combination of Cho and Omoniyi teaches all the limitations of Claim 1
The combination of Cho and Omoniyi does not teach 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
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
And selecting the semantic information for the content item using the subset of the ranked candidate collections
However, Bonthuys does teach 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 ([Page 2] Metacritic posts a publisher ranking of the best and worst performing studio games which teaches this limitation)
Cho, Omoniyi and Bonthuys are analogous art because they all deal with providing insights on data
It would have been obvious to combine Cho with the multiple data source handling of Omoniyi and the candidate ranking of Bonthuys. Doing so would allow for a more streamlined data analysis process ([Omoniyi-Page 1])
The combination of Cho, Omoniyi and Bonthuys 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, Omoniyi, Bonthuys, and Boxofficemojo are analogous art because they all deal with providing insights on data
It would have been obvious to combine Cho with the multiple data source handling of Omoniyi and the candidate ranking of Bonthuys, and the ranked candidate selections of Boxofficemojo. Doing so would provide users with another way to gauge the quality of a piece of media ([Boxofficemojo-About Box Office Mojo])
Claim 11 is rejected as being unpatentable under U.S.C 103 over Cho et al (NPL: What is Metacritic and Metascore?) (“Metacritic” “Cho”) in view of Temidayo Omoniyi (NPL: Connect Power BI to Multiple Data Sources (Cloud & On-premises)) (“Omoniyi”), Darryn Bonthuys (NPL: Sony Is Metacritic’s Highest-Rated Publisher Of 2022) (“Bonthuys”) and Boxofficemojo (NPL: Boxofficemojo) (“Boxofficemojo”) and Icon-era (NPL: Icon-era) (“Icon-era”)
Regarding Claim 11, the combination Cho, Omoniyi, Bonthuys, and Boxofficemojo teaches all the limitations of Claim 10
The combination Cho, Omoniyi, Bonthuys, and Boxofficemojo 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, Omoniyi, Bonthuys, Boxofficemojo, and Icon-era are analogous art because they all deal with providing insights on data.
It would have been obvious to combine Cho with the multiple data source handling of Omoniyi and the candidate ranking of Bonthuys, the ranked candidate selections of Boxofficemojo and the weighted combinations of icon-era. Doing so would provide users with another way to gauge the quality of a piece of media ([Boxofficemojo-About Box Office Mojo])
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Claim 14 is rejected as being unpatentable under U.S.C 103 over Cho et al (NPL: What is Metacritic and Metascore?) (“Metacritic” “Cho”) in view of Temidayo Omoniyi (NPL: Connect Power BI to Multiple Data Sources (Cloud & On-premises)) (“Omoniyi”), and Vyas et al (NPL: Understanding the Mobile Game App Activity) (“Vyas”)
Regarding Claim 14, the combination of Cho and Omoniyi teaches all the limitations of Claim 13
The combination of Cho and Omoniyi 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, Omoniyi, and Vyas are analogous art because they all deal with providing insights on data.
It would have been obvious to combine Cho with the multiple data source handling of Omoniyi and the mobile app scraping of Vyas. Doing so would allow developers to look at different platforms to develop more successful games ([Vyas-Introduction])
Claim 16 is rejected as being unpatentable under U.S.C 103 over Cho et al (NPL: What is Metacritic and Metascore?) (“Metacritic” “Cho”) in view of Temidayo Omoniyi (NPL: Connect Power BI to Multiple Data Sources (Cloud & On-premises)) (“Omoniyi”) and Vu et al (NPL: Mining User Opinions in Mobile App Reviews: A Keyword-based Approach) (“Vu”)
Regarding Claim 16, the combination of Cho and Omoniyi teaches all the limitations of Claim 13
The combination of Cho and Omoniyi 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 one or more search results and one or more summaries; and extracting the data from the one or more search results and the one or more summaries.
However, Vu does 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 one or more search results and one or more summaries; and extracting the data from the one or more search results and the one or more summaries. ([Abstract] describes a framework that takes in user keywords and associates them with positive or negative reviews)
Cho, Omoniyi, and Vu are analogous art because they all deal with providing insights on data.
It would have been obvious to combine Cho with the multiple data source handling of Omoniyi and the mobile app review association of Vu. Doing so would make identifying relevant reviews easier ([Vu-Abstract])
Claim 18 is rejected as being unpatentable under U.S.C 103 over Cho et al (NPL: What is Metacritic and Metascore?) (“Metacritic” “Cho”) in view of Temidayo Omoniyi (NPL: Connect Power BI to Multiple Data Sources (Cloud & On-premises)) (“Omoniyi”), 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, the combination of Cho and Omoniyi teaches all the limitations of Claim 17
The combination of Cho and Omoniyi 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 does teach 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)
Cho, Omoniyi, and Baker are analogous art because they all deal with providing insights on data.
It would have been obvious to combine Cho with the multiple data source handling of Omoniyi and the text processing and text analysis of Baker. Doing so would allow for highlighting of positive and negative features present in an application ([Baker: Background of invention])
The combination of Cho, Omoniyi, and Baker does not teach a deep learning model, N-grams
However, Li does teach 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, Omoniyi, Baker, and Li are analogous art because they all deal with providing insights on the data.
It would have been obvious to combine Cho with the multiple data source handling of Omoniyi, and the text processing and text analysis of Baker and the n-grams processing of Li. Doing so would allow for a more effective technique when it comes to capturing the words and semantics of a dataset while keeping the expressing power of learned vectors ([Li: Abstract])
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 express 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.”)
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/URIAH VENDELL MOORE/ Examiner, Art Unit 2142
/HAIMEI JIANG/ Primary Examiner, Art Unit 2142
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