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 .
DETAILED ACTION
1. This communication is responsive to the amendment filed on 08/28/2025.
Claims 1, 4-8, 10-11, 14-18 and 20 have been amended. Claims 1-20 are pending.
Response to Arguments
2. Notice that the strikethrough or canceled terms narrow the scope of the claimed invention.
Applicant's arguments with respect to claims 1-20 have been considered but are moot in view of the new ground(s) of rejection.
Examiner’s Note
3. Preliminary mappings of some pertinent arts:
Semantic similarity (According to Google): “Semantic similarity is the metric used to measure the extent to which two linguistic items—such as words, sentences, or documents—are alike in meaning. It is a foundational concept in computational linguistics and natural language processing (NLP) that enables machines to understand the conceptual relationships within and between pieces of text.”
Sentiment parameter (According to Google): “A sentiment parameter is a component used within sentiment analysis to determine the emotional tone of a piece of text. In natural language processing (NLP) and machine learning, these parameters help quantify subjective opinions and attitudes, such as positive, negative, or neutral feelings.”
Sentiment intensity rating (According to Google): “A sentiment intensity rating is a numerical value that quantifies the strength or intensity of the emotion expressed in a piece of text, often ranging from a negative number to a positive one (e.g., -1 to +1). This rating is a component of sentiment analysis, which not only determines polarity (positive, negative, or neutral) but also measures how strongly that sentiment is felt. For instance, a text with a high positive score indicates strong approval, while a low negative score indicates strong disapproval.”
Engagement score (According to Google): “An engagement score is a metric that measures the level of interaction and commitment an individual has with a product, service, or organization. It provides a single, quantifiable number to track user or employee engagement, helping businesses identify valuable customers, assess employee satisfaction, and gauge the effectiveness of their strategies.”
Kogan et al, US 20240168708, [Paragraphs 24-25 (“receiving textual input that references an attribute of the search result and a sentiment of the user for that attribute; determining, based on the attribute of the search result and the sentiment of the user for that attribute”)] [Paragraphs 57 and 91 (“The sentiment of a segment may be determined based on term(s) of the segment, other term(s) of the textual input, and/or data that is in addition to the textual input itself (e.g., voice characteristics included in voice input on which the textual input is based, preceding textual input, and/or other data). In some implementations, the message processing engine 122 includes a trained sentiment classifier trained to predict, based on a segment of textual input and/or other data, a class/direction of sentiment of the segment and optionally a magnitude of the sentiment. For example, the sentiment classifier may predict whether a segment is positive or negative, and optionally a magnitude of the positivity/negativity, based on term(s) of the segment and optionally based on other data. In some implementations, the message processing engine 122 additionally and/or alternatively utilizes a mapping between terms and sentiments (and optionally sentiment magnitudes) to determine sentiment of a segment. For example, the mapping may define that: a segment that includes “never” has negative sentiment of a strong magnitude; “I'm not a fan of” has negative sentiment of a lesser magnitude; “always” has positive sentiment of a strong magnitude; “I like” has positive sentiment of a lesser magnitude”)].
Shekhar et al, US 20180213284, [Paragraph 43 (“The collaboration-filter may be trained to filter out or remove spurious results from the collaboration-classifier analyzer 162. The sentiment analyzer 166 may be trained to determine a sentiment for each classified keyword. Accordingly, VA 150 may employ speech-to-text engine”)] [Paragraph 35 (“Such data may be employed in generating and/or updating various individual recommendations, group recommendations, user-user influence weights, user dominance weights”)] [Paragraph 21 (“these systems often consider only the previous patterns of users considered somewhat “similar” to the single user”)] [Paragraph 44 (“A user-interest cluster employs a latent variable to generate correlations between users 102-109 and content scores provided by other users, based on the viewing history of users 102-109. Each user-interest cluster includes a distribution of scores or ratings for each title included in the set of available content. Based on user preferences, each of users 102-109 is associated with a probability of being included in each of the user-interest clusters”)] [Paragraphs 59-60 (“A sentiment analyzer, such as but not limited to sentiment analyzer 166 of FIG. 1B, may be employed to determine a sentiment for each determined. Thus, a plurality of keyword and sentiment pairs may be generated at block 210. Each keyword and sentiment pair is associated with the user that is the speaker or communicator of the keyword and the sentiment. Thus, one or more keyword and sentiment pairs may be associated with one or more of the users”)] [Paragraph 61 (“A sentiment may include a numerical value that is associated with or corresponding to the intent of the speaker. In one exemplary embodiment, the value of a sentiment may range from [−1, +1]. A sentiment value of −1.0 may indicate an extremely negative intention of the speaker, while a sentiment value of −0.1 may indicate only a mildly negative intention of the speaker. Similarly, a sentiment value of +1.0 may indicate an extremely positive intention of the speaker, while a sentiment value of +0.1 may indicate a mildly positive intention of the speaker. An intention value of 0.0 may indicate a neutral intention of the speaker. It should be understood that other ranges and scales for the sentiment value are possible. The sentiment value may be continuous or a discretized variable”, i.e., sentiment intensity rating, sentiment score and sentiment parameter)] [Paragraphs 44 and 74 (“A user-interest cluster employs a latent variable to generate correlations between users 102-109 and content scores provided by other users, based on the viewing history of users”, i.e., subjects of interest and sentiment scores)] [Abstract and paragraphs 3, 21, 28 and 48 (“recommending and providing content to a user group for the social consumption of recommended content”, i.e., generating engagement actions associated with subjects of interest)].
Kazi et al, US 20170220578, [Abstract and paragraph 6(“sentiment-scores corresponding to sentiments, wherein each sentiment-score is based on a degree to which n-grams of the text of the communication match sentiment-words associated with the sentiments; determining, for each of the communications, an overall sentiment for the communication based on the calculated sentiment-scores for the communication; calculating sentiment levels for the particular content item corresponding sentiments, each sentiment level being based on a total number of communications determined to have the overall sentiment of the sentiment level; and generating a sentiments-module including sentiment-representations corresponding to overall sentiments having sentiment levels greater than a threshold sentiment level”, i.e., semantic similarity analysis)] [Paragraphs 34 and 71-72 and 75 (“Interest information may include interests related to one or more categories. Categories may be general or specific”, i.e., subjects of interest)] [Paragraph 61 (“The calculated module-score may alternatively be a sum of different functions that may be weighted in a suitable manner (e.g., the weights being pre-determined by the social-networking system 160). As an example and not by way of limitation, the function for calculating a module-score may be represented by the following expression”, i.e., sentiment intensity rating)] [Paragraphs 5, 39 and 43 (“the user may see a sentiments-module (as described in further detail below) displaying a sentiment-representation of the “Excited” sentiment (e.g., resulting from a relative majority of user communications using language that indicates excitement) and a mentions-module displaying the words “so inspirational” (e.g., resulting from a relative majority of user communications containing those words).”, i.e., generating engagement actions associated with subjects of interest)].
Claim Rejections - 35 USC § 102
4. 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 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.
5 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
6. Claims 1-7, 10-17 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kazi et al (US 20170220578).
Claim 1:
Kazi suggests a method of attributing portions of a [[net]] sentiment score (NSS) generated in a transient sentiment community within a distributed communication network, the method performed in a computing system communicatively connected within the distributed communication network, the method comprising: detecting, based at least in part upon a semantic similarity analysis performed in a processor of the computing system, content associated with a plurality of subjects of interest [Paragraphs 34 and 71-72 and 75 (“Interest information may include interests related to one or more categories. Categories may be general or specific”, i.e., subjects of interest)], the content defined in accordance with at least one text character string that is included within social media content data received at the computing system, the content including a sentiment expression associated with the plurality of subjects of interest, the sentiment expression [Abstract and paragraph 6(“sentiment-scores corresponding to sentiments, wherein each sentiment-score is based on a degree to which n-grams of the text of the communication match sentiment-words associated with the sentiments; determining, for each of the communications, an overall sentiment for the communication based on the calculated sentiment-scores for the communication; calculating sentiment levels for the particular content item corresponding sentiments, each sentiment level being based on a total number of communications determined to have the overall sentiment of the sentiment level; and generating a sentiments-module including sentiment-representations corresponding to overall sentiments having sentiment levels greater than a threshold sentiment level”, i.e., semantic similarity analysis)]. Kazi suggests identifying, based on continuously monitoring, by the processor, a first and at least a second subjects of interest of the plurality upon accessing, by the processor, expression [Paragraph 61 (“The calculated module-score may alternatively be a sum of different functions that may be weighted in a suitable manner (e.g., the weights being pre-determined by the social-networking system 160). As an example and not by way of limitation, the function for calculating a module-score may be represented by the following expression”, i.e., sentiment intensity rating)]. Kazi suggests determining, responsive to identifying the first and at least a second subjects of interest, a first and at least a second portions of the [[net]] sentiment score that is attributable to the first and the at least a second subjects of interest respectively [Abstract and paragraph 6(“sentiment-scores corresponding to sentiments, wherein each sentiment-score is based on a degree to which n-grams of the text of the communication match sentiment-words associated with the sentiments; determining, for each of the communications, an overall sentiment for the communication based on the calculated sentiment-scores for the communication; calculating sentiment levels for the particular content item corresponding sentiments, each sentiment level being based on a total number of communications determined to have the overall sentiment of the sentiment level; and generating a sentiments-module including sentiment-representations corresponding to overall sentiments having sentiment levels greater than a threshold sentiment level”, i.e., semantic similarity analysis)] [Paragraphs 34 and 71-72 and 75 (“Interest information may include interests related to one or more categories. Categories may be general or specific”, i.e., subjects of interest)]. Kazi suggests generating, by the processor of the computing system in accordance with the determining, a first and at least a second engagement actions with regard to the first and the at least a second subjects of interest respectively [Paragraphs 5, 39 and 43 (“the user may see a sentiments-module (as described in further detail below) displaying a sentiment-representation of the “Excited” sentiment (e.g., resulting from a relative majority of user communications using language that indicates excitement) and a mentions-module displaying the words “so inspirational” (e.g., resulting from a relative majority of user communications containing those words).”, i.e., generating engagement actions associated with subjects of interest)].
Claim 2:
Kazi suggests wherein the social media content comprises one or more of: a hashtag, a twitter handle, an emoticon, at least a portion of a website content, a text string produced via a speech to text conversion of at least a portion of an audio file, a message exchange, an image, and at least a video portion [Paragraphs 5 and 6 (“The social-networking system may identify communications associated with the particular content item. The identified communications may be authored by users of the online social network, or by any other entity. The communications may include posts, reshares, comments, messages, or other suitable communications”)].
Claim 3:
Kazi suggests wherein the plurality of subjects of interest comprises one or more of a named product, a named feature, a named brand, a named entity, a named individual, and a named organizational group [Paragraphs 32 and 34 (“product information” AND “As an example and not by way of limitation, if a user “likes” an article about a brand of shoes the category may be the brand”)].
Claim 4:
Kazi suggests wherein the [[net]] sentiment score is determined based on (i) a sentiment score in accordance with the sentiment intensity rating and (ii) an engagement score [Paragraph 61 (“The calculated module-score may alternatively be a sum of different functions that may be weighted in a suitable manner (e.g., the weights being pre-determined by the social-networking system 160). As an example and not by way of limitation, the function for calculating a module-score may be represented by the following expression”, i.e., sentiment intensity rating)].
Claim 5:
Kazi suggests wherein the [[net]] sentiment score is determined in accordance with a trained neural network machine learning model in conjunction with the social media content [Paragraphs 73 and 104 (“a dictionary generated by the deep-learning model” AND “determine coefficients using machine-learning algorithms trained on historical actions and past user responses”].
Claim 6:
Kazi suggests wherein the first and at least a second portions of the [[net]] sentiment score that is attributable to the first and the at least a second subjects of interest respectively are determined based respective sentiment scores and engagement scores [Abstract and paragraph 6(“sentiment-scores corresponding to sentiments … (e.g. wall posts, photo-sharing, event organization, messaging, games, or advertisements) to facilitate social interaction between or among users”)] [Paragraph 58 (“The interactive element may be generated for the particular content item if a search-results interface may be generated for the particular content item”] [Paragraph 61 (“The level of engagement may be a measure of how much users engage with particular search-results modules. The social-networking system 160 may award a higher module-score to a search-results module if it has a relatively high level of engagement than otherwise. The social-networking system 160 may determine the level of engagement based on the amount of time users spend on particular search-results modules”)].
Claim 7:
Kazi suggests wherein at least one of the first and the at least a second engagement actions are generated upon reaching at least one of: (i) a predetermined threshold of the [[net]] sentiment score; (ii) a rate of growth of the transient sentiment community that receives the content and the sentiment expression expressive usage; (iii) respective predetermined sentiment attribution thresholds associated with the first and at least a second portions; and (iv) a rate of growth of the NSS over at least a period of time [Abstract and paragraph 6(“sentiment-scores corresponding to sentiments, wherein each sentiment-score is based on a degree to which n-grams of the text of the communication match sentiment-words associated with the sentiments; determining, for each of the communications, an overall sentiment for the communication based on the calculated sentiment-scores for the communication; calculating sentiment levels for the particular content item corresponding sentiments, each sentiment level being based on a total number of communications determined to have the overall sentiment of the sentiment level; and generating a sentiments-module including sentiment-representations corresponding to overall sentiments having sentiment levels greater than a threshold sentiment level”)].
Claim 10:
Kazi suggests wherein the at least one sentiment parameter comprises a sarcasm sentiment classification, and further comprising determining the first and at least a second portions of the [[net]] sentiment score attributable to the first and the at least a second subjects of interest respectively based at least in part upon replacing the sarcasm sentiment classification with one of a contrary and an opposite sentiment classification [Paragraph 72(“the content item 130 contains sarcasm (e.g., because the content provider, the television show Last Week Tonight, illustrated in FIG. 3, is a satirical news program).”)] [Abstract and paragraph 6(“sentiment-scores corresponding to sentiments, wherein each sentiment-score is based on a degree to which n-grams of the text of the communication match sentiment-words associated with the sentiments; determining, for each of the communications, an overall sentiment for the communication based on the calculated sentiment-scores for the communication; calculating sentiment levels for the particular content item corresponding sentiments, each sentiment level being based on a total number of communications determined to have the overall sentiment of the sentiment level; and generating a sentiments-module including sentiment-representations corresponding to overall sentiments having sentiment levels greater than a threshold sentiment level”, i.e., classifying or categorizing or changing sentiment classifications based on their sentiment rating or score)] .
Claim 11:
Claim 11 is essentially the same as claim 1 except that it sets forth the claimed invention as a server rather than a method and rejected under the same reasons as applied above.
Claim 12:
Claim 12 is essentially the same as claim 2 except that it sets forth the claimed invention as a server rather than a method and rejected under the same reasons as applied above.
Claim 13:
Claim 13 is essentially the same as claim 3 except that it sets forth the claimed invention as a server rather than a method and rejected under the same reasons as applied above.
Claim 14:
Claim 14 is essentially the same as claim 4 except that it sets forth the claimed invention as a server rather than a method and rejected under the same reasons as applied above.
Claim 15:
Claim 15 is essentially the same as claim 5 except that it sets forth the claimed invention as a server rather than a method and rejected under the same reasons as applied above.
Claim 16:
Claim 16 is essentially the same as claim 6 except that it sets forth the claimed invention as a server rather than a method and rejected under the same reasons as applied above.
Claim 17:
Claim 17 is essentially the same as claim 7 except that it sets forth the claimed invention as a server rather than a method and rejected under the same reasons as applied above.
Claim 20:
Claim 20 is essentially the same as claim 1 except that it sets forth the claimed invention as a program product rather than a method and rejected under the same reasons as applied above.
. Claim Rejections - 35 USC § 103
7. 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 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.
8. 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.
9. Claims 8-9 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Kazi et al (US 20170220578), in view of Harpur et al (US 20190109811).
Claim 8:
The combined teachings of Kazi and Harpur suggest wherein generating at least one of the first and at least a second engagement actions comprises: accessing, by the processor of the computing system, a fact check engine; receiving a fact check result for at least one of the first and the at least second of the subjects of interest from the fact check engine in accordance with the sentiment expression expression expression [Harpur: Paragraphs 81 and 66 (“identify the subject matter expert via a profile of the users of the social network. Once the subject matter expert is identified, at least a portion of the content of the response is sent to the subject matter expert for validation. The validation may include having the subject matter expert check facts of the content of the response and determine if the content of the response is true or untrue. If the content of the response is true, the subject matter expert may alert the method (400) that the content of the response is true and is to be approved. If the content of the response is untrue, the subject matter expert may alert the method (400) that the content of the response is untrue and is to be rejected” AND “analyzed for facts and sentiments”)].
Both references (Kazi and Harpur) taught features that were directed to analogous art and they were directed to the same field of endeavor, such as data processing. It would have been obvious to one of ordinary skill in the art at the time the invention was made, having the teachings of Kazi and Harpur before him/her, to modify the system of Kazi with the teaching of Harpur in order to fact check data [Harpur: Paragraph 81].
Claim 9:
The combined teachings of Kazi and Harpur suggest identifying one or more sources of dissemination of one or more assertions associated with at least one of the first and the at least a second subjects of interest; and retaining, as potential evidence in a reputation based legal or administrative proceeding, time stamped information associated with the one or more sources, at least some portions of the social media content, and the fact check results [Harpur: Paragraphs 81 and 66 (“identify the subject matter expert via a profile of the users of the social network. Once the subject matter expert is identified, at least a portion of the content of the response is sent to the subject matter expert for validation. The validation may include having the subject matter expert check facts of the content of the response and determine if the content of the response is true or untrue. If the content of the response is true, the subject matter expert may alert the method (400) that the content of the response is true and is to be approved. If the content of the response is untrue, the subject matter expert may alert the method (400) that the content of the response is untrue and is to be rejected” AND “analyzed for facts and sentiments”)] [Paragraph 84 (“a response to a post from a user, the response includes content to be posted on an activity stream of a social network”, i.e., timestamping or taking time into consideration)].
Both references (Kazi and Harpur) taught features that were directed to analogous art and they were directed to the same field of endeavor, such as data processing. It would have been obvious to one of ordinary skill in the art at the time the invention was made, having the teachings of Kazi and Harpur before him/her, to modify the system of Kazi with the teaching of Harpur in order to fact check data [Harpur: Paragraph 81].
Claim 18:
Claim 18 is essentially the same as claim 8 except that it sets forth the claimed invention as a server rather than a method and rejected under the same reasons as applied above.
Claim 19:
Claim 19 is essentially the same as claim 9 except that it sets forth the claimed invention as a server rather than a method and rejected under the same reasons as applied above.
Pertinent Arts
10. Bhan, US 20220261863, discloses transitory sentiment community-based digital asset valuation, wherein the detailed implementation comprises: Monitoring, at a server computing device, generation of a sentiment community in accordance with social media content data rendered at a display interface of a computing device, identifying, in conjunction with indicia of a digital asset referenced in the social media content data, at least one sentiment expression of the sentiment community, and determining a valuation of the digital asset based at least in part on the at least one sentiment expression and a rarity measure, the rarity measure indicating a relative uniqueness of least one attribute of the digital asset.
Ivry et al, US 20220292154, discloses automated sentiment analysis and/or geotagging of social network posts, wherein the detailed implementation comprises: For each of a plurality of user generated content items uploaded to a social network from a plurality of different client devices: selecting a specific geographic region mapping dataset of a plurality of geographic region mapping datasets corresponding to a specific geographic region identified by an analysis of the respective user generated content post, mapping by the specific geographic region mapping dataset, a generic term to a specific geographic location within the specific geographic region, and tagging the respective user generated content item with a geo-location tag of the specific geographic location within the specific geographic region.
Wilkinson et al, US 20250124231, discloses sentiment and misinformation analysis of digital conversations, wherein the detailed implementation comprises: Receiving, via a network, at least one digital transcription of a conversation or statement from a digital communication platform; determining, by a processor, from the digital transcription of the conversation or statement, a conveyed quantitative sentiment value using a plurality of machine learning (ML)
techniques, the determining the conveyed quantitative sentiment value comprising the steps of: a. processing the at least one digital transcription of the conversation into a machine interpretable form by unigram and bigram vectorization and subsequent dimensionality reduction; b. calculating, using a set of trained neural network ML models, a sentiment sub-classification problem from the machine interpretable form; c. calculating a discrete sentiment probability distribution related to a sentiment classification of each model; d. calculating a weighted sum of the discrete sentiment probability distributions; and e. calculating a maximum of the discrete sentiment probability distributions and returning the corresponding sentiment as a final sentiment classification.
Gadh et al, US 20250117695, discloses machine learning sentiment analysis for selective record processing, wherein the detailed implementation comprises: A device receives a candidate record for processing. The device generates, using a machine learning model associated with determining a sentiment value, a determination of the sentiment value for the candidate record. The device determines whether the sentiment value satisfies a threshold. The device selects a first processing action associated with the candidate record or a second processing action associated with the candidate record based on whether the sentiment value satisfies the threshold. The device transmits, based on selecting the first processing action or the second processing action, one or more messages associated with causing the first processing action or the second processing action to be performed.
L’Huillier, US 12,056,721, discloses programmatically assessing consumer sentiment with regard to an attribute descriptor associated with a commercial entity and/or a commercial item, wherein the detailed implementation comprises: Applying a set of matching rules to an attribute descriptor associated with consumer review text and a data structure that represents a dependency grammar of the consumer review text; in response to first determination, based on the set of matching rules, that a match exists between the attribute descriptor and the data structure, generating a first sentiment score for the attribute descriptor based on at least one matching rule from the set of matching rules, and selecting first data for a promotion based on the attribute descriptor and the first sentiment score; in response to a second determination, based on the set of matching rules, that a match does not exist between the attribute descriptor and the data structure, determining whether the attribute descriptor and the data structure match at least one grammar rule of a set of grammar rules, and in response to a third determination that the attribute descriptor and the data structure match the at least one grammar rule of the set of grammar rules, applying the at least one grammar rule to the attribute descriptor and the data structure to generate a second sentiment score for the attribute descriptor, wherein the second sentiment score comprises a value independent of the at least one grammar rule, and select second data for the promotion based on the attribute descriptor and the second sentiment score; and transmitting, via a network device communicatively coupled to the one or more computers via a network interface integrated with the one or more computers, one or more computer- executable instructions to a consumer computing device to facilitate rendering of the promotion comprising the first data or the second data via an electronic interface of the consumer computing device.
Saraee et al, US 20250078453, discloses training a multi-modal machine learning architecture for content generation, wherein the detailed implementation comprises: Receiving a plurality of training images; executing a feature extraction machine learning model to generate a plurality of training embeddings for a plurality of training images each in an embedding space; training a content scoring machine learning model using the plurality of training embeddings to generate performance scores for content items based on embeddings in the embedding space; receiving a set of text; executing the feature extraction machine learning model using the set of text to generate a text embedding in the same embedding space as the training embeddings for the plurality of training images; generating, using the content scoring machine learning model, a text performance score for the set of text using the text embedding in the embedding space; and generating a record identifying the text performance score for the set of textR.
Guzman et al, US 11,587,172, discloses quantifying and indexing sentiment risk in financial markets and risk management contracts, wherein the detailed implementation comprises: 1) The economy as measured by expectations for future economic (GDP) growth; 2) in financial markets as measured by sentiment for securities comprising a benchmark financial index; 3) or for individual securities comprising an individual portfolio; by combining multiple data sources including surveys, opinion polls, prediction markets, news, internet content, activity, and search query data, and extracting a common signal from multiple sources. An index can be created aggregating sentiment measures across securities which are the components of a well-known financial market index to track sentiment risk for that index. An index can be created by aggregating sentiment measures across securities which are the holdings in an individual portfolio. Security-specific sentiment measures can be created. Financial instruments—options, futures, options on futures, ETFs, ETNs and other financial instruments can be created to track e sentiment index and the sentiment measures for each underlying component, providing a way to hedge sentiment risk.
Mao et al, US 12,020,265, discloses discovering online influencers and generating market sentiment index, wherein the detailed implementation comprises: Generating a social media sub-network comprising nodes each representing a respective user and edges connecting the nodes, by: receiving a plurality of postings related to an area of interest in the domain from a plurality of sources in social media, wherein each posting of the plurality of postings comprises text comprising content of the posting; for each of the plurality of postings, extracting a user account name of a user authoring the posting from the text of the posting, by using a first trained machine learning model and the text as input to the first trained machine learning model to determine the user account name in the text, wherein the first trained machine learning model is trained to: determine character embeddings from the text of a given posting, wherein the character embeddings comprise numeric representations of words in the text of the given posting at character-level compositions; and determine user account name in the text of the given posting based on the character embeddings; determining a first set of nodes corresponding respectively to users associated with the extracted user account name; and determining a second set of nodes each representing an additional user and connecting to one or more nodes in the first set of nodes, and edges connecting the first set of nodes and the second set of nodes, wherein an edge connecting a first node and a second node indicates that content of the posting of the user associated with the first node is similar or related to content of the posting of the user associated with the second node; forming a group of potential influencers from the first and second sets of nodes in the social media sub-network; determining metrics representing at least a level of expertise of an individual person of the group of potential influencers, by: using a second trained machine learning model and social media postings authored by the individual person as input to the second trained machine learning model to generate embeddings for the individual person, wherein the embeddings for the individual person quantify semantics in the domain associated with the individual person, and wherein the second trained machine learning model comprises a Bidirectional Encoder Representations from Transformers (BERT) model pretrained to consider words before and after a given word in a training text dataset; and comparing the embeddings for the individual person with reference embeddings for known experts to determine the metrics representing at least the level of expertise of the individual person, wherein the metrics is represented by a similarity score indicative of a similarity between content of the social media postings authored by the individual person and content provided by the known experts; based on the metrics for the group of potential influencers, identifying a group of influencers from the group of potential influencers; and presenting market trends associated with the domain in a user interface based on the identified group of influencers and social media postings of the identified group of influencers.
Rani et al, US 20240176950, discloses aspect based sentiment analysis with contextual grouping of aspects, wherein the detailed implementation comprises: Receiving a collection of textual data, extracting a set of aspects and a set of sentiment words from the textual data, identifying a set of aspect-sentiment word pairs from the extracted aspects and sentiment words, identifying a subset of aspect-sentiment word pairs according to a set of predefined rules, and grouping a plurality of aspects associated with the subset of aspect-sentiment word pairs into one or more clusters. Each of the set of aspect-sentiment word pairs includes an aspect word from the set of aspects and a sentiment word from the set of sentiment words. Each of the subset of aspect-sentiment word pairs is determined to have an aspect-sentiment relationship according to the set of predefined rules.
Meenal Kathiresan, US 20240086311, discloses sentiment analysis using magnitude of entities, wherein the detailed implementation comprises: Receiving, by one or more processors, a first data, wherein the first data corresponds to application source code and includes a plurality of data elements; clustering, by the one or more processors, data elements of the plurality of data elements of the first data into multiple data element clusters using a clustering algorithm; scoring, by the one or more processors, the data elements of each data element cluster based on a frequency of the data element, an intensity of the data element, and a contextual score of the data element; assigning, by the one or more processors, a polarity to the score of each data element of each data element cluster based on labeled input data to generate a polarity score for each data element; generating, by the one or more processors, a data entity dictionary based on the polarity scores for each data element; performing, by the one or more processors, sentiment analysis on second data using the data entity dictionary to generate a second polarity score for each second data element of a plurality of second data elements, wherein the second data corresponds to second application source code and includes the plurality of second data elements; and providing, by the one or more processors, a remediation notification for a particular second data element of the plurality of second data elements based on a corresponding second polarity score of the second polarity scores satisfying a condition.
Rozgic et al, US 11,854,538, discloses sentiment detection in audio data, wherein the detailed implementation comprises: Processing, using a first model, first data, representing an original audio signal, to generate first model output data, wherein the first model output data represents at least one sentiment category and at least one attribute of a speaking user; determining, using the first model output data and a decoder, second data representing an estimation of the original audio signal; determining third data representing a comparison of the first data and the second data; generating a first trained model by updating the first model using the at least one attribute of the speaking user and the third data, wherein the first tratned model is configured to detect one or more sentiment categories from audio; after generating the first trained model, receiving input audio data representing speech; determining, using a stored voice profile associated with a user profile, that a portion of speech corresponding to at least a first portion of the input audio data was spoken by a first user; using the first trained model, processing the first portion of the input audio data to generate second model output data; determining, using the second model output data, a first sentiment category corresponding to the first portion of the input audio data; and associating the first sentiment category with the user profile.
Warner, US 20230162230, discloses targeting content based on implicit sentiment analysis, wherein the detailed implementation comprises: Generating and sending a request via one or more first communication networks to a computing device associated with each of a plurality of first transactions; responsive to receiving feedback data in response to each of the requests, determining a first transaction sentiment score from the feedback data for each of the first transactions; training a sentiment machine learning model based on a correlation of first transaction context data obtained for each of the first transactions with a corresponding one of the first transaction sentiment scores determined from the feedback data; responsive to verifying that an accuracy of the sentiment machine learning model exceeds an accuracy threshold, applying the trained sentiment machine learning model to obtained second transaction context data associated with a second transaction conducted by a user to generate a second transaction sentiment score for the second transaction; inserting at least a portion of the second transaction context data, the second transaction sentiment score, and a unique identifier for the user included in the second transaction context data into a record of a sentiment database; updating a stored dynamic user sentiment score for the user based on the second transaction sentiment score for the second transaction conducted by the user, wherein the dynamic user sentiment score is maintained in the sentiment database associated with the unique identifier for the user; responsive to detecting a login by the user via a mobile or web application: generating a default graphical user interface (GUI); responsive to determining that the dynamic user sentiment score is outside of a score range, outputting the default GUI via the mobile or web application; and responsive to determining that the dynamic user sentiment score is within the score range, automatically: modifying the default GUI to include stored first digital content corresponding to the score range and targeted to the user to generate a modified GUI, wherein the first digital content comprises a graphic and a hyperlink that is selectable by a user interface device to direct the user to another webpage associated with a host of the mobile or web application; and outputting the modified GUI via the mobile or web application.
Yeddu et al, US 20230103840, discloses automated system alert based on logs sentiment analysis, wherein the detailed implementation comprises: Creating a log data specific lexicon based on log data samples, each word in the log data specific lexicon corresponding to a weighted sentiment score with a binary polarity. A log message is obtained, and a sentiment value of the log message is assigned based on respective weighted sentiment scores of words appearing in the log message. The log message is classified for a class indicating an issue the log message addresses. An alert type for the log message is determined based on the sentiment value, the class, and a priority of the log message, and a system alert including a set of key performance indicators according to the alert type is produced to a user.
Gerber et al, US 20220351252, discloses consumer sentiment analysis for selection of creative elements, wherein the detailed implementation comprises: Receiving a sample of consumers and an exposure time period; determining an identity and one or more exposure windows for each of the consumers based on one or more datasets stored in a data cloud; determining a set of static features and a set of dynamic features for each of the consumers based on an identity profile associated with the identity and the one or more exposure windows; training a sentiment prediction model using the set of static features and the set of dynamic features; determining, via the sentiment prediction model, engagement probabilities for multiple creative elements having different sentiment tags; and publishing, on a publication network, a piece of targeted content including at least one of the multiple creative elements selected based on the engagement probabilities.
Conclusion
11. 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 extension fee 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 date of this final action.
12. Any inquiry concerning this communication or earlier communications from the examiner should be directed to [Hung D. Le], whose telephone number is [571-270-1404]. The examiner can normally be communicated on [Monday to Friday: 9:00 A.M. to 5:00 P.M.].
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached on [571-272-4080]. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Hung Le
10/20/2025
/HUNG D LE/Primary Examiner, Art Unit 2161