DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
The following is a final office action.
Claims 1-20 are currently pending and have been examined on their merits.
Claims 1-2, 4-12, 14-16, and 19 are currently amended see REMARKS November 11, 2025.
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 under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1-7 recites a method (i.e. a series of steps), claims 8-14 recite a processor, and claims 15-20 recite a system and therefore each claim falls within one of the four statutory categories.
Step 2A prong 1 (Is a judicial exception recited?):
The representative claim 1 recites: a method, comprising: performing, sentiment analysis with respect to content associated with an item; performing, sentiment analysis of user-generated content submitted in response to the content associated with the item, at least a portion of the user generated content including both sentiment associated with the content and sentiment associated with the item, at least a portion of the user-generated content including both sentiment associated with the content and sentiment associated with the item; storing, determined sentiment information associated with the item; and generating, responsive to a query for information about a creator of the content associated with the item, one or more visualization corresponding to a predicted reason for one or more changes to a creator sentiment score.
Claim 8: perform, sentiment analysis with respect to content associated with an item and generated at a first time; perform, sentiment analysis of first user- generated content submitted in response to the content associated with the item and generated at a second time; perform, sentiment analysis of second user-generated content submitted in response to the content associated with the item, the second user-generated content generated at a third time, later than each of the first time and the second time; store, determined sentiment information associated with the item; determine, responsive to one or more input queries, a temporal change in sentiment for the item based, at least in part, on a change in sentiment between the content, the user-generated content, and the second user-generated content; and provide, responsive to the one or more input queries, a visualization of the temporal change for display.
Claim 15: determine, based in part upon content associated with an item and user-generated content associated with the content associated with the item, sentiment data for the item, the sentiment data including first sentiment data associated with the item and second sentiment data associated with a creator of the content associated with the item, to be stored and associated with information about the item, and to generate, responsive to a query, a visualization of one or more of a temporal change in the first sentiment data or the second sentiment data.
The claims recite a certain method of organizing human activity. The claims recite a certain method of organizing human activity as the disclosure is directed to commercial or legal interactions. The claims recite a series of steps for performing sentiment analysis on user generated content associated with an item to be provided to other users when requested. The steps recite a method for allowing a user such as a customer to query about the sentiment or opinions of others concerning a product. The method allows for further advertising for a product by presenting a user with review information such as sentiment corresponding to a product.
Alternatively, the claims recite a mental process. The claims recite a method for performing sentiment analysis on a piece of content associated with an item and user-generated content submitted in response to the content associated with an item, such as determining a user’s feeling in a review for a product, and providing the sentiment information to a user in response to a query which can be performed in the human mind or by using simple tools such as pen and paper. The courts have identified concepts such as observation, evaluation, judgement and opinion as reciting a mental process. Therefore, merely analyzing the sentiment of content corresponding to an item and providing the analyzed information to a user when requested is a mental process.
Step 2A Prong 2 (Is the exception integrated into a practical application?): The claims additionally recite;
Claim 1: A computer, a first trained language model, a second trained language model, and a repository.
Claim 8: A processor, comprising: one or more circuits, a first trained language model, a second trained language model, and a repository.
Claim 15: A system comprising: one or more processors to use one or more language models and a repository.
However, the additional elements merely amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Merely utilizing generic computer elements such as a computer and a user device to perform basic actions of the abstract idea by receiving, analyzing, and storing information. Furthermore, a method for processing and storing information does not amount to improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a), applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b), effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c), such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B (Does the claim recite additional elements that amount to significantly more that the judicial exception?): As discussed above, the additional imitations amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). See reasoning for Step 2A prong 2. Therefore, the claims do not amount to significantly more as they do not recite an improvement to a technology or technical field. The claims merely recite “apply it” or applying generic computer elements to receiving and analyze information.
Claims 2-7, 9-14, and 16-20 are directed to further narrowing the abstract idea of extracting information associated with an item and performing sentiment analysis to help generate a result such as a summary of the content or identifying features for potential improvement.
Additional elements recited by the dependent claims include:
Claims 6, 13, and 20: a user interface.
However, these elements are directed to merely “apply it” or applying generic computer elements to perform the abstract idea.
Therefore, claims 1-20 are rejected under U.S.C. 101.
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 factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2, 6-9, 13-16, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Clark (US 2017/0124575) in view of Hanson (US 8825515).
Claim 1: Clark discloses a computer-implemented method, comprising: performing, using a first trained language model, sentiment analysis with respect to content associated with an item (Paragraph [0003]; [0017]; [0019-0021]; Fig. 3, embodiments of the present disclosure include a method for evaluating product features. A processor uses natural language processing to ingest product reviews for a plurality of products. Each of the products embodies a specific for each of the plurality of product features. The processor analyzes the ingested product reviews for sentiment associated with the specific forms. The processor generates a sentiment score for each product feature based on the analysis);
performing, using a second trained language model, sentiment analysis of user-generated content submitted in response to the content associated with the item, at least a portion of the user-generated content including both sentiment associated with the content and sentiment associated with the item (Paragraph [0003]; [0017]; [0021]; [0032]; [0072]; Fig. 3, embodiments of the present disclosure include a method for evaluating product features. A processor uses natural language processing to ingest product reviews for a plurality of products. Each of the products embodies a specific for each of the plurality of product features. The processor analyzes the ingested product reviews for sentiment associated with the specific forms. The processor generates a sentiment score for each product feature based on the analysis. In some embodiments of the present disclosure an emphasis may be put on the concept of using sentiment found in product reviews in order to inform prospective purchasers which features are important to consider when buying a particular product or type of product. In some embodiments, the natural language processing system may further include a search application. The search application may be configured to search one or more databases for product reviews that are related to a target group of products. For example, the search application may be configured to search a corpus of information related to product reviews previously submitted by the product review submission module);
storing, using a repository, determined sentiment information associated with the item (Paragraph [0044-0045]; Fig. 2, the output of the natural language processor may be stored as an information corpus in one or more data sources. In some embodiments, the natural language processing system may include a sentiment ranker module. The sentiment ranker module may be configured to generate sentiment scores for specific forms of product features based on the analysis of annotated product reviews);
Clark discloses a system of monitoring the sentiment of user generated content pertaining to an item. However, Clark does not specifically disclose the following claim limitations: and generating, responsive to a query for information about a creator of the content associated with the item, one or more visualizations corresponding to a predicted reason for one or more changes to a creator sentiment score.
In the same field of endeavor of monitoring user sentiments for a topic Hanson teaches and generating, responsive to a query for information about a creator of the content associated with the item, one or more visualizations corresponding to a predicted reason for one or more changes to a creator sentiment score ([Col. 2 ll. 23-30]; [Col. 4 ll. 1-35]; [Col. 6 ll. 40-Col. 7 ll. 37]; Fig. 4A, a system and method related generally to sentiment aggregation based upon the one or more user-generated postings, such as social media user generated postings, which may include: opinions, reviews, ratings, predictions, rankings, recommendations, and votes. Thus the system is configured to collect and display sentiments of one or more topics from one or more users. The server includes a user generated comments database which is configured to store user defined topics, cush as user-generated comments that are provided via the client devices. As described herein a user can cause a sentiment to be associated with the keyword portion of the comments. The module also represents functionality to furnish one or more graphical displays conveying sentiment statistics related to the user’s comments. For example, the module may cause the publishing of a graph representing sentiment statistics corresponding to one or more comments to the display. The graphics may represent statistics and/or measurements in the form of bar graphs, line graphics, and the like. The graphics can represent a statistical measurement of the sentiment of a user over a predetermined time period. For example, the graphics represent a line graph measuring a sentiment of a user over a predetermined time period. The graphics may also covey sentiment statistics of an individual user across multiple topics. An opinion feed may include each of the user’s comments over the predetermined time period. The opinion feed may also display one or more comments generated within the system my other users that correspond to the desired topic of interest. The interface is configured to cause the display of graphics representing leading opinion makers (e.g. users) by the keyword portion).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of analyzing product reviews to determine user sentiment as disclosed by Clark (Clark [0003]) with the system of and generating, responsive to a query for information about a creator of the content associated with the item, one or more visualizations corresponding to a predicted reason for one or more changes to a creator sentiment score as taught by Hanson (Hanson [Col. 4 ll. 1-35]). With the motivation of help determine the opinions of users towards a product over a period of time (Hanson [Col. 1 ll. 20-26]).
Claim 8: Clark discloses a processor, comprising: one or more circuits to: perform, using a first trained language model, sentiment analysis with respect to content associated with an item and generated at a first time (Paragraph [0003]; [0017]; [0019-0021]; Fig. 3, embodiments of the present disclosure include a method for evaluating product features. A processor uses natural language processing to ingest product reviews for a plurality of products. Each of the products embodies a specific for each of the plurality of product features. The processor analyzes the ingested product reviews for sentiment associated with the specific forms. The processor generates a sentiment score for each product feature based on the analysis);
perform, using a second trained language model, sentiment analysis of first user- generated content submitted in response to the content associated with the item and generated at a second time (Paragraph [0003]; [0017]; [0021]; [0032]; [0072]; Fig. 3, embodiments of the present disclosure include a method for evaluating product features. A processor uses natural language processing to ingest product reviews for a plurality of products. Each of the products embodies a specific for each of the plurality of product features. The processor analyzes the ingested product reviews for sentiment associated with the specific forms. The processor generates a sentiment score for each product feature based on the analysis. In some embodiments of the present disclosure an emphasis may be put on the concept of using sentiment found in product reviews in order to inform prospective purchasers which features are important to consider when buying a particular product or type of product. In some embodiments, the natural language processing system may further include a search application. The search application may be configured to search one or more databases for product reviews that are related to a target group of products. For example, the search application may be configured to search a corpus of information related to product reviews previously submitted by the product review submission module);
perform, using the second trained language model, sentiment analysis of second user-generated content submitted in response to the content associated with the item, the second user-generated content generated at a third time, later than each of the first time and the second time (Paragraph [0003]; [0017]; [0021]; [0032]; [0072]; Fig. 3, embodiments of the present disclosure include a method for evaluating product features. A processor uses natural language processing to ingest product reviews for a plurality of products. Each of the products embodies a specific for each of the plurality of product features. The processor analyzes the ingested product reviews for sentiment associated with the specific forms. The processor generates a sentiment score for each product feature based on the analysis. In some embodiments of the present disclosure an emphasis may be put on the concept of using sentiment found in product reviews in order to inform prospective purchasers which features are important to consider when buying a particular product or type of product. In some embodiments, the natural language processing system may further include a search application. The search application may be configured to search one or more databases for product reviews that are related to a target group of products. For example, the search application may be configured to search a corpus of information related to product reviews previously submitted by the product review submission module);
store, using a repository, determined sentiment information associated with the item (Paragraph [0044-0045]; Fig. 2, the output of the natural language processor may be stored as an information corpus in one or more data sources. In some embodiments, the natural language processing system may include a sentiment ranker module. The sentiment ranker module may be configured to generate sentiment scores for specific forms of product features based on the analysis of annotated product reviews).
Clark discloses a system of monitoring the sentiment of user generated content pertaining to an item. However, Clark does not specifically disclose the following claim limitations: determine, responsive to one or more input queries, a temporal change in sentiment for the item based, at least in part, on a change in sentiment between the content, the user-generated content, and the second user-generated content; and provide, responsive to the one or more input queries, a visualization of the temporal change for display.
In the same field of endeavor of monitoring user sentiments for a topic Hanson teaches determine, responsive to one or more input queries, a temporal change in sentiment for the item based, at least in part, on a change in sentiment between the content, the user-generated content, and the second user-generated content; and provide, responsive to the one or more input queries, a visualization of the temporal change for display ([Col. 2 ll. 23-30]; [Col. 4 ll. 1-35]; [Col. 6 ll. 40-Col. 7 ll. 37]; Fig. 4A, a system and method related generally to sentiment aggregation based upon the one or more user-generated postings, such as social media user generated postings, which may include: opinions, reviews, ratings, predictions, rankings, recommendations, and votes. Thus the system is configured to collect and display sentiments of one or more topics from one or more users. The server includes a user generated comments database which is configured to store user defined topics, cush as user-generated comments that are provided via the client devices. As described herein a user can cause a sentiment to be associated with the keyword portion of the comments. The module also represents functionality to furnish one or more graphical displays conveying sentiment statistics related to the user’s comments. For example, the module may cause the publishing of a graph representing sentiment statistics corresponding to one or more comments to the display. The graphics may represent statistics and/or measurements in the form of bar graphs, line graphics, and the like. The graphics can represent a statistical measurement of the sentiment of a user over a predetermined time period. For example, the graphics represent a line graph measuring a sentiment of a user over a predetermined time period. The graphics may also covey sentiment statistics of an individual user across multiple topics. An opinion feed may include each of the user’s comments over the predetermined time period. The opinion feed may also display one or more comments generated within the system my other users that correspond to the desired topic of interest. The interface is configured to cause the display of graphics representing leading opinion makers (e.g. users) by the keyword portion).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of analyzing product reviews to determine user sentiment as disclosed by Clark (Clark [0003]) with the system of determine, responsive to one or more input queries, a temporal change in sentiment for the item based, at least in part, on a change in sentiment between the content, the user-generated content, and the second user-generated content; and provide, responsive to the one or more input queries, a visualization of the temporal change for display as taught by Hanson (Hanson [Col. 4 ll. 1-35]). With the motivation of help determine the opinions of users towards a product over a period of time (Hanson [Col. 1 ll. 20-26]).
Claim 15: Clark discloses a system comprising: one or more processors to use one or more language models to determine, based in part upon content associated with an item and user-generated content associated with the content associated with the item, sentiment data for the item, the sentiment data including first sentiment data associated with the item and second sentiment data associated with a creator of the content associated with the item, to be stored to a repository and associated with information about the item (Paragraph [0003]; [0017]; [0021]; [0032]; [0072]; Fig. 3, embodiments of the present disclosure include a method for evaluating product features. A processor uses natural language processing to ingest product reviews for a plurality of products. Each of the products embodies a specific for each of the plurality of product features. The processor analyzes the ingested product reviews for sentiment associated with the specific forms. The processor generates a sentiment score for each product feature based on the analysis. In some embodiments of the present disclosure an emphasis may be put on the concept of using sentiment found in product reviews in order to inform prospective purchasers which features are important to consider when buying a particular product or type of product. In some embodiments, the natural language processing system may further include a search application. The search application may be configured to search one or more databases for product reviews that are related to a target group of products. For example, the search application may be configured to search a corpus of information related to product reviews previously submitted by the product review submission module);
Clark discloses a system of monitoring the sentiment of user generated content pertaining to an item. However, Clark does not specifically disclose the following claim limitations: and to generate, responsive to a query, a visualization of one or more of a temporal change in the first sentiment data or the second sentiment data.
In the same field of endeavor of monitoring user sentiments for a topic Hanson teaches and to generate, responsive to a query, a visualization of one or more of a temporal change in the first sentiment data or the second sentiment data ([Col. 2 ll. 23-30]; [Col. 4 ll. 1-35]; [Col. 6 ll. 40-Col. 7 ll. 37]; Fig. 4A, a system and method related generally to sentiment aggregation based upon the one or more user-generated postings, such as social media user generated postings, which may include: opinions, reviews, ratings, predictions, rankings, recommendations, and votes. Thus the system is configured to collect and display sentiments of one or more topics from one or more users. The server includes a user generated comments database which is configured to store user defined topics, cush as user-generated comments that are provided via the client devices. As described herein a user can cause a sentiment to be associated with the keyword portion of the comments. The module also represents functionality to furnish one or more graphical displays conveying sentiment statistics related to the user’s comments. For example, the module may cause the publishing of a graph representing sentiment statistics corresponding to one or more comments to the display. The graphics may represent statistics and/or measurements in the form of bar graphs, line graphics, and the like. The graphics can represent a statistical measurement of the sentiment of a user over a predetermined time period. For example, the graphics represent a line graph measuring a sentiment of a user over a predetermined time period. The graphics may also covey sentiment statistics of an individual user across multiple topics. An opinion feed may include each of the user’s comments over the predetermined time period. The opinion feed may also display one or more comments generated within the system my other users that correspond to the desired topic of interest. The interface is configured to cause the display of graphics representing leading opinion makers (e.g. users) by the keyword portion).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of analyzing product reviews to determine user sentiment as disclosed by Clark (Clark [0003]) with the system of and to generate, responsive to a query, a visualization of one or more of a temporal change in the first sentiment data or the second sentiment data as taught by Hanson (Hanson [Col. 4 ll. 1-35]). With the motivation of help determine the opinions of users towards a product over a period of time (Hanson [Col. 1 ll. 20-26]).
Claims 2, 9, and 16: Modified Clark discloses the computer-implemented method of claim 1, the processor as per claim 8, and the system as per claim 15. Clark further discloses further comprising: extracting information from the content associated with the item as textual data, wherein performing sentiment analysis with respect to the content associated with the item is based on the extracted textual data, and wherein performing sentiment analysis of the first user-generated content is based on analysis generated using the first language model and the user-generated content (Paragraph [0003]; [0017]; [0021]; [0032]; [0072]; Fig. 3, embodiments of the present disclosure include a method for evaluating product features. A processor uses natural language processing to ingest product reviews for a plurality of products. Each of the products embodies a specific for each of the plurality of product features. The processor analyzes the ingested product reviews for sentiment associated with the specific forms. The processor generates a sentiment score for each product feature based on the analysis).
Claims 6, 13, and 20: Modified Clark discloses the computer-implemented method of claim 1, the processor as per claim 8, and the system as per claim 15. Clark further discloses further comprising: receiving, through a user interface, a reference to a second content associated with the item, wherein the reference is inputted by a user; retrieving the second content form one or more external sources; performing sentiment analysis to the second content associated with the item (Paragraph [0003]; [0017]; [0021]; [0032]; [0072]; Fig. 3, embodiments of the present disclosure include a method for evaluating product features. A processor uses natural language processing to ingest product reviews for a plurality of products. Each of the products embodies a specific for each of the plurality of product features. The processor analyzes the ingested product reviews for sentiment associated with the specific forms. The processor generates a sentiment score for each product feature based on the analysis. In some embodiments of the present disclosure an emphasis may be put on the concept of using sentiment found in product reviews in order to inform prospective purchasers which features are important to consider when buying a particular product or type of product. In some embodiments, the natural language processing system may further include a search application. The search application may be configured to search one or more databases for product reviews that are related to a target group of products. For example, the search application may be configured to search a corpus of information related to product reviews previously submitted by the product review submission module);
and storing determined sentiment data associated with the item to the repository (Paragraph [0044-0045]; Fig. 2, the output of the natural language processor may be stored as an information corpus in one or more data sources. In some embodiments, the natural language processing system may include a sentiment ranker module. The sentiment ranker module may be configured to generate sentiment scores for specific forms of product features based on the analysis of annotated product reviews).
Claims 7 and 14: Modified Clark discloses the computer-implemented method of claim 6 and the processor as per claim 8. Clark further discloses wherein the determined sentiment data comprises one or more of: a summary of the content associated with the item, a bias, a sentiment type, one or more positive features, one or more negative features, one or more related products, one or more top issues and concerns, one or more related items, technical analysis, and one or more recommended items (Paragraph [0003]; [0017]; [0021]; [0032]; [0052]; Fig. 5, embodiments of the present disclosure include a method for evaluating product features. A processor uses natural language processing to ingest product reviews for a plurality of products. Each of the products embodies a specific for each of the plurality of product features. The processor analyzes the ingested product reviews for sentiment associated with the specific forms. The processor generates a sentiment score for each product feature based on the analysis. In some embodiments of the present disclosure an emphasis may be put on the concept of using sentiment found in product reviews in order to inform prospective purchasers which features are important to consider when buying a particular product or type of product. By parsing the product reviews to identify subject predicate pairings. This may involve analyzing parse trees generated by a natural language processor. The subject and predict in the sentence may be used to identify a specific form of a product feature and a text element indicating sentiment about the specific form. For example, a sentence in a product review of a smart phone might read “the camera on this smart phone” and a predict “terrible.” Based on the parsing the system may determine the product feature’s specific form (i.e. the particular camera on this smart phone) is associated with strongly negative sentiment).
Claim(s) 3-4, 10-11, and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Clark (US 2017/0124575) in view of Hanson (US 8825515) further in view of Melo (US 2017/0270588).
Claims 3, 10, and 17: Modified Clark discloses the computer-implemented method of claim 1, the processor as per claim 8, and the system as per claim 15. However, Clark does not disclose wherein the content associated with the item and first user-generated content comprise timestamp data associated with the content associated with the item and the first user-generated content.
In the same field of endeavor of determining sentiment of a product review Melo teaches wherein the content associated with the item and first user-generated content comprise timestamp data associated with the content associated with the item and the first user-generated content (Paragraph [0014]; [0069]; Fig. 2, another embodiment of the present disclosure provides a method for processing reviews. The method comprises receiving a request for information about an item from a client device. The method comprises identifying a group of reviews for the item in the request. In an example, an object node has a number of different field as depicted timestamp and other data).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of analyzing product reviews to determine user sentiment as disclosed by Clark (Clark [0003]) with the system of wherein the content associated with the item and user-generated content comprise timestamp data associated with the content associated with the item and the user-generated content as taught by Melo (Melo [0069]). With the motivation of help determine the opinions of users towards a product (Melo [0011]).
Claims 4, 11, and 18: Modified Clark discloses the computer-implemented method of claim 1, the processor as per claim 8, and the system as per claim 15. However, Clark does not disclose further comprising: determining a degree of agreement of the first user-generated content with respect to the review content; and determining a degree of influence of the first user-generated content to subsequent item-related content.
In the same field of endeavor of determining sentiment of a product review Melo teaches further comprising: determining a degree of agreement of the first user-generated content with respect to the review content; and determining a degree of influence of the first user-generated content to subsequent item-related content (Paragraph [0014]; [0084]; [0090]; Fig. 3, another embodiment of the present disclosure provides a method for processing reviews. The method comprises receiving a request for information about an item from a client device. The method comprises identifying a group of reviews in the group of reviews made by a set of people having an influence on the user using a hypergraph comprising object representing people in a social network. Influence node has a number of different field including influence level, item, and other data. Influence level identifies the level of an influence of the first edge which is identified by the source edge node identifier on the second edge which is identified by target edge node identifier).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of analyzing product reviews to determine user sentiment as disclosed by Clark (Clark [0003]) with the system of further comprising: determining a degree of agreement of the user-generated content with respect to the review content; and determining a degree of influence of the user-generated content to subsequent item-related content as taught by Melo (Melo [0084]). With the motivation of help determine the opinions of users towards a product (Melo [0011]).
Claim(s) 5, 12, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Clark (US 2017/0124575) in view of Hanson (US 8825515) in view of Roy (US 2021/0241289).
Claims 5, 12, and 19: Modified Clark discloses the computer-implemented method of claim 1, the processor as per claim 8, and the system as per claim 15. However, Clark does not disclose further comprising: Identifying, based on the sentiment analysis, one or more product features for potential improvement; (Claim 5) and providing information associated with the identified one or more product features. (Claims 12 and 19) report the one or more product features to relevant personnel.
In the same field of endeavor of determining the sentiment of reviews for a product Roy teaches further comprising: Identifying, based on the sentiment analysis, one or more product features for potential improvement; (Claim 5) and providing information associated with the identified one or more product features. (Claims 12 and 19) report the one or more product features to relevant personnel (Paragraph [0003]; [0017-0018]; [0040] Fig. 2, embodiments of the present disclosure include a method for mitigating user dissatisfaction related to a product. A processor may collect a first set of user interaction data related to the product form a device and a first set of user sentiment data related to the product. The user processor may generate a user profile. In response to the satisfaction threshold being exceeded, the processor may output an action to reduce dissatisfaction of the user).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of analyzing product reviews to determine user sentiment as disclosed by Clark (Clark [0003]) with the system of further comprising: Identifying, based on the sentiment analysis, one or more product features for potential improvement; and providing information associated with the identified product features as taught by Roy (Roy [0003]). With the motivation of help determine and improving customer sentiment towards a product (Roy [0002]).
Therefore, claims 1-20 are rejected under U.S.C. 103.
Response to Arguments
Applicant’s arguments, see REMARKS, filed November 11, 2025, with respect to the rejections of claims 1-20 under U.S.C. 101 have been fully considered and are not persuasive.
The applicant argues that the claims do not recite an abstract idea. However, the examiner respectfully disagrees as the claims recite a method for performing sentiment analysis with respect to content associated with an item; storing determined sentiment information; and generating one or more visualization corresponding to a predicted reason for one or more changes to a creator sentiment score. The claims recite a mental process as the claims merely recite a series of steps of receiving and analyzing user content information to determine sentiment scores and generating a visualization of the scores. Receiving and processing information and generating an output are a mental processes as the courts have recited similar concepts, such as observation, evaluation, opinions, and judgement, as being mental processes. Additionally, the claims can be practically performed by a person mentally, or with simple tool such as pen and paper. As a person is capable of mentally performing sentiment analysis on user generated content pertaining to an item such as a product review, as well as analyzing the comments of other users pertaining to the review, and generating a sentiment score for the comments. Additionally, a person is capable of using simple tools such as pen and paper to generate a visualization corresponding to the sentiment analysis to display the changes in sentiment over time. Therefore, the claims recite a mental process. Alternatively, the claims recite a certain method of organizing human activity as they recite commercial interactions. The claims recite a process of gather user reviews pertaining to a product or item and generating a graphical visual representation of sentiments over time. The claims merely recite a process of monitoring consumer sentiment towards an item or product by performing basic functions of receiving and analyzing user generated content for sentiment values. Therefore, the claims recite an abstract idea.
The representative further argues that the claims are directed to a practical application. However, the examiner respectfully disagrees as the additional elements of a computer to implement the method and using trained language models to perform the sentiment analysis are not improvements to a technology or technical field. The additional elements are directed to merely “apply it” or applying generic computer elements to perform the abstract idea of receiving and analyzing user generated content to determine an output. Therefore, the claims are not directed to a practical application.
As the additional elements are directed to merely “apply it” or applying generic computer element to perform the abstract idea they further do not amount to significantly more.
Therefore, The examiner maintains the rejection of claims 1, 8, and 15 under U.S.C. 101.
Applicant argues that claims 2-7, 9-14, and 16-20 are allowable as being dependent on claims 1, 8 and 15 and therefore are rejected under U.S.C. 101
Applicant’s arguments, see REMARKS, filed November 11, 2025, with respect to the rejections of Claims 1-2, 6-9, 13-16, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Clark (US 2017/0124575) in view of Hanson (US 8825515) are not persuasive as the claims were amended which required further search and consideration and new art was applied.
Claims 1, 8, and 15: The applicant argues that the current prior art of does not disclose the newly amended claim limitations. However, upon further search and consideration the examiner finds that Clark can be used in combination with Hanson to disclose the newly amended claim limitations. As Clark discloses a system of using natural language processing to ingest product reviews and analyzing the sentiment of the reviews to generate a sentiment score for each product (Clark [0003]). Which can be used in combination with Hanson which teaches a system of continuously receiving user generated content to be able to perform sentiment analysis and generate a visual representation of the changes in sentiment of the content over time (Hanson [Col. 6 ll. 40-Col. 7 ll. 37]). Therefore, the examiner finds that the combination of Clark and Hanson discloses the newly amended claim limitations.
Therefore, claim 1, 8, and 15 are new rejected under U.S.C. 103.
Claims 2-7, 9-14, and 16-20 were argued as being allowable only as being dependent on claims 1, 8, and 15. Therefore, they are also allowable over U.S.C. 103.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Ebi (US 2023/0394511) Integrated system and method for determining consumer insight and analyzing market trends.
Bala (US 2022/0292527) Methods of assessing long term indicators of sentiment.
Chang (US 2012/0185544) Method and apparatus for analyzing and applying data related to customer interactions with social media.
Werth (US 2015/0149153) Systems and methods for identifying and recording the sentiment of a message, posting, or other online communication using an explicit sentiment identifier.
Daniluk (US 2008/0162157) Method and apparatus for creating and aggregating rankings of people, companies, and products based on social network acquittances and authorities’ opinions.
Mass (US 2014/0071134) Visualization of user sentiment for product features.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to COREY RUSS whose telephone number is (571)270-5902. The examiner can normally be reached on M-F 7:30-4:30.
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/COREY RUSS/Primary Examiner, Art Unit 3629