Detailed Action
Claims 16-35 are pending in this application. Claims 1-15 were cancelled in the Preliminary Amendment filed on 4/19/24.
Priority
Acknowledgment is made of applicant's claim for foreign priority under 35 U.S.C. 119(a)-(d).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 16-35 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per claims 16, 20, 24, 25, 29, 33-35 recites the limitation "the data". There is insufficient antecedent basis for this limitation in the claim. It is unclear to what “the data” refers to, does it refer to data content or preprocessed data.
All other dependent claims rejected for the same reasons set forth above.
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 16-35 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recites
As per claims 16,29,35, a method for data enrichment according to a characteristic using data content from at least two data sources comprising at least one data type, the method comprising: assigning a trust factor to a data source for the characteristic;
preprocessing data received from the data source according to a data type;
analyzing information comprised by the data with respect to the characteristic; and
applying an enrichment process to select information for data enrichment related to the characteristic based on the analysis of the information and the trust factor.
As per claims 17,30, the method/computing apparatus of claim 16, 29, wherein the at least one data type comprises data type text, data type structured information, data type image, data type sound, data type video, or a combination thereof.
As per claims 18,31, the method/computing apparatus of claim 16, 29, wherein the trust factor reflects how trustworthy the data source is with respect to the characteristic.
As per claim 20, the method of claim 16 wherein the at least one data type comprises data type text, data type structured information, data type image, data type sound, data type video, and preprocessing data received from the data source according to the data type comprises: extracting content of the data by at least one of: for the data type text: applying natural language processing for translation, correction, formatting, speech tagging, and/or feature extraction of the text; for the data type structured information: organizing content according to tags; for the data type image: applying at least one of object detection, face detection, object segmentation, and object recognition; for the data type sound: applying speech recognition and/or natural language processing; and for the data type video: applying at least one of object detection, face detection, speech recognition, and natural language processing.
As per claim 21, the method of claim 16 wherein preprocessing the data from the data source according to a data type comprises: extracting metadata of the data.
As per claim 22, the method of claim 21 wherein metadata comprises at least one of descriptive metadata relating to at least one of title, subject, genre, and author, rights metadata relating to at least one of title, copyright status, rights holder, and license terms, technical metadata relating to at least one of file types, size, creation date, creation time, type of compression, uniform resource locator, and page rank, preservation metadata relating to an item's place in a hierarchy or in a sequence, and picture metadata relating to at least one of a timestamp, camera properties, resolution, size, and geotag.
As per claim 23, the method of claim 16 wherein the at least one data type comprises data type text, data type structured information, data type image, data type sound, data type video, and analyzing the information comprised by the data with respect to the characteristic comprises at least one of: for the data type text: applying natural language processing for analyzing word similarity and/or sentiments of authors; for the data type image: analyzing detected objects and/or faces with respect to at least one of facial expression, emotion, age, and gender; and for the structured data type: discovering rules in the structure data.
As per claims 24, 33, the method/computing apparatus of claim 16, 29, wherein analyzing the information comprised by the data with respect to the characteristic comprises: determining a relevance score for the data with respect to the characteristic and a confidence value of the relevance score being correct.
As per claim 27, the method of claim 16 wherein applying the enrichment process to select the information for data enrichment related to the characteristic based on the analysis of the information and the trust factor comprises: determining a probability value for the information for data enrichment being correct; and in response to the probability value being higher than a threshold value, selecting the information.
As per claim 28, the method of claim 16 wherein the method is triggered periodically and/or in response to a request for recommendation request concerning the characteristic.
The claims are directed towards receiving, processing, analyzing data/websites/reviews for a particular characteristic/service and summarizing based on analyzing the data/website/reviews. See Fig.3,8. This is akin to a user reading different reviews on different websites on whether a certain cruise would be kid friendly and providing a summary on whether the certain cruise is kid friendly.
Therefore the claims and the specification is drawn to certain methods of organizing human activity(in particular commercial or legal interactions such as advertising, marketing or sales activities or behaviors or business relations ) and/or mental processes(the steps of assigning, preprocessing, analyzing, and applying can be performed mentally and/or with the aid of pen and paper).
If the claim under broadest reasonable interpretation covers limitation that is drawn to certain methods of organizing human activity and mental processes but for recitation of a generic computer and/or generic computer components described at a high level of generality or linking the use of the judicial exception to a particular technological environment or field of use, then it falls within the grouping of abstract ideas. Accordingly, the claim recites an abstract idea. (Step 2A, prong 1).
This judicial exception is not integrated into a practical application. In particular, the claims recites additional elements such as
As per claims 29,35, a computing apparatus/ A non-transitory computer storage medium encoded with a computer program, the computer program comprising a plurality of program instructions that when executed by one or more processors cause the one or more processors to perform operations, the computing apparatus comprising: one or more processors; and at least one memory device coupled with the one or more processors, wherein the at least one memory device contains a plurality of program instructions that, when executed by the one or more processors, cause the computing apparatus to:
As per claims 19,32, the method/computing apparatus of claim 16, 29, wherein the trust factor is determined based on a machine learning model that is continuously trained with data retrieved from the two or more data sources.
As per claims 25,34, the method/computing apparatus of claim 24,33, wherein the enrichment process is based on a machine learning model, and an input to the machine learning model comprises the relevance score for the data with respect to the characteristic, the confidence value of the information being correct, the trust factor of the data source of the information, a weight factor of the information, and a time stamp.
As per claim 26, the method of claim 25 wherein the input to the machine learning model comprises further information extracted from metadata.
The claim does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations of As per claims 29,35, a computing apparatus/ A non-transitory computer storage medium encoded with a computer program, the computer program comprising a plurality of program instructions that when executed by one or more processors cause the one or more processors to perform operations, the computing apparatus comprising: one or more processors; and at least one memory device coupled with the one or more processors, wherein the at least one memory device contains a plurality of program instructions that, when executed by the one or more processors, cause the computing apparatus,
As per claims 19,32, the method/computing apparatus of claim 16, 29, wherein the trust factor is determined based on a machine learning model that is continuously trained with data retrieved from the two or more data sources,
as per claims 25,34, the method/computing apparatus of claim 24,33, wherein the enrichment process is based on a machine learning model, and an input to the machine learning model comprises the relevance score for the data with respect to the characteristic, the confidence value of the information being correct, the trust factor of the data source of the information, a weight factor of the information, and a time stamp,
as per claim 26, the method of claim 25 wherein the input to the machine learning model comprises further information extracted from metadata.
are generic computer components described at a high level of generality and limitations amounts to mere instructions to implement the abstract idea on a computer.
Claims 19,25,26,32,34, recites the use of machine learning model to determine trust factor through training, extracting metadata, and inputting data into the machine learning model for an enrichment process which is “apply it”(or an equivalent) with the judicial exception, or merely uses a computer as a tool to perform an abstract idea. There is no improvement of the machine learning model but merely uses the machine learning model. MPEP 2106.05(f), See also July 2024 Subject Matter Eligibility Example 47 claim 2.
Therefore the additional limitation/elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Even considering all the additional element in combination, they are just providing a computerized system to perform the invention, but doesn’t improve the computing technology as the additional elements do not integrate the invention into a practical application. The claims is directed to an abstract idea and merely reciting generic computer components described at a high level of generality and limitations amounts to mere instructions to implement the abstract idea on a computer and/or adding the words “apply it”(or an equivalent) with the judicial exception, or merely uses a computer as a tool to perform an abstract idea MPEP 2106.05(f). Therefore the claims are not patent eligible. (Step 2A, prong2).
The claim does not include additional elements/limitations that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements/limitations is drawn to limitations that use a computer as a tool, and includes well-understood, routine, and conventional activities(ie. receiving or transmitting data over a network MPEP 2106.05(d)(II)) that amount to no more than implementing the abstract idea with a computerized system. The claim is not patent eligible(Step 2B).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 16-20, 23, 28-32, 35 rejected under 35 U.S.C. 103 as being unpatentable over US 2024/0346233 issued to Paulino et al.(Paulino) in view of US 2020/0213408 issued to Gao et al.(Gao).
As per claims 16,29,35, Paulino teaches a method/ a computing apparatus/ A non-transitory computer storage medium encoded with a computer program, the computer program comprising a plurality of program instructions that when executed by one or more processors cause the one or more processors to perform operations, the computing apparatus comprising: one or more processors; and at least one memory device coupled with the one or more processors, wherein the at least one memory device contains a plurality of program instructions that, when executed by the one or more processors, cause the computing apparatus(Fig.8, [00116][0117]) for data enrichment according to a characteristic using data content from at least two data sources comprising at least one data type([0023] … Generally, a review summary may refer to any summary of a set of reviews associated with an item. For example, an item, such as an application, game, media (e.g., movie) or other product, may have a number of reviews provided by various reviewers or consumers of the item. In such a case, a review summary can be generated to summarize the reviews provided by the various reviewers. … ([0037] ... At a high level, the review summary manager 112 manages generation of review summaries in association with items. In particular, the review summary manager 112 can obtain various review data, such as reviews, review context, item context, user data, reviewer data, and/or review weights. Using the review data, the review summary manager 112 ….), the method comprising:
preprocessing data received from the data source according to a data type(Fig.2, element 222 preview data preprocessor, [0056] The review data preprocessor 222 is generally configured to preprocess review data, or a portion thereof. The review data preprocessor 222 may preprocess review data in any number of ways to effectuate a more efficient and effective review summary prompt.….[0059] Another example of filtering review data may include filtering reviews having negative content or language, such as profanity or other inappropriate language. In this way, the review data preprocessor 222 may identify negative content or language and remove reviews having the negative content. Any technology may be used to identify such negative content, including, for example, machine learning technology.; preprocessing of review is based on the text(data type) of the review);
analyzing information comprised by the data with respect to the characteristic([0037] ... At a high level, the review summary manager 112 manages generation of review summaries in association with items. In particular, the review summary manager 112 can obtain various review data, such as reviews, review context, item context, user data, reviewer data, and/or review weights. Using the review data, the review summary manager 112 can generate a model prompt to initiate generation of a review summary. As one example, a model prompt may include various reviews associated with a particular item. The model prompt can be input into an LLM to obtain, as output, a review summary in association with reviews provided in relation to a particular item. Such reviews used as a basis for generating a review summary may be reviews submitted via reviewer devices 116. Review devices 116a-116n may be any type of computing devices at which a reviewer may provide a review(s) in association with an item (e.g., a product, a computer application, a service, an experience, a website, or other item for which a user may desire to provide a review). For example, upon an individual purchasing or using an item, the individual may provide a review of the time (e.g., via the review device). The review provided via the reviewer device may be provided to the review service 118 that collects reviews for subsequent presentation to potential consumers.); and
applying an enrichment process to select information for data enrichment related to the characteristic based on the analysis of the information ([0023] … Generally, a review summary may refer to any summary of a set of reviews associated with an item. For example, an item, such as an application, game, media (e.g., movie) or other product, may have a number of reviews provided by various reviewers or consumers of the item. In such a case, a review summary can be generated to summarize the reviews provided by the various reviewers. … a review summary can be generated that summarizes the collected reviews, or a portion thereof (e.g., a set of the collected reviews about the particular product)... [0078] The review summary generator 226 is generally configured to generate review summaries. In this regard, the review summary generator 226 utilizes various review data, such as previously provided reviews of an item, to generate a review summary associated with the item. In embodiments, the review summary generator 226 can take, as input, a model prompt or set of model prompts generated by the prompt generator 224. Based on the model prompt, the review summary generator 226 can generate a review summary or set of review summaries associated with the item(s) indicated in the model prompt. For example, assume a model prompt includes a set of reviews associated with a particular item. In such a case, the review summary generator 226 generates a review summary associated with the particular item based on the set of reviews included in the model prompt.).
Paulino does not explicitly teach assigning a trust factor to a data source for the characteristic; trust factor.
Gao explicitly teaches assigning a trust factor to a data source for the characteristic; trust factor([0051] Optionally, at step 320, content items are sorted by quality. Again, quality may be measured in one of multiple ways. One example measure of quality is popularity, as determined based on a number of clicks of the content item, a number of likes of the content item, a number of comments on the content item, a number of shares of the content item. Another measure of quality is a trust score (or a reputation) related to the author of the content item. The more trustworthy the author, the more likely the content item will be highly regarded by others. Thus, the more trustworthy the author, the higher the measure of quality of the content item. Additionally, the higher the trust score (or reputation) of members who have liked, commented, or shared a content item, the higher the measure of quality of the content item.).
Therefore it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Paulino’s of providing a review summary based on multiple review/reviewers of a product/service to include the teachings of Gao teaches a trust/reputation score for an author in order to provide the predictable result of providing a review summary of product/services based on trusted review/reviewers.
One ordinary skill in the art would have been motivated to combine the teachings in order to determine the quality of product/services based on the trust score of an author(Gao, para.51).
As per claims 17,30, Paulino in view of Gao teaches the method/computing apparatus of claim 16, 29, wherein the at least one data type comprises data type text, data type structured information, data type image, data type sound, data type video, or a combination thereof(Paulino, [0023] Referring initially to FIG. 1, a block diagram of an exemplary network environment 100 suitable for use in implementing embodiments described herein is shown. Generally, the system 100 illustrates an environment suitable for facilitating efficient generation of review summaries. Among other things, embodiments described herein efficiently generate summaries of reviews of an item. Generally, a review summary may refer to any summary of a set of reviews associated with an item. For example, an item, such as an application, game, media (e.g., movie) or other product, may have a number of reviews provided by various reviewers or consumers of the item. In such a case, a review summary can be generated to summarize the reviews provided by the various reviewers. Reviews may be provided by entities (e.g., individuals) for various items…..) .
As per claims 18,31, Paulino in view of Gao teaches the method/computing apparatus of claim 16, 29, wherein the trust factor reflects how trustworthy the data source is with respect to the characteristic(Gao, [0051] Optionally, at step 320, content items are sorted by quality. Again, quality may be measured in one of multiple ways. One example measure of quality is popularity, as determined based on a number of clicks of the content item, a number of likes of the content item, a number of comments on the content item, a number of shares of the content item. Another measure of quality is a trust score (or a reputation) related to the author of the content item. The more trustworthy the author, the more likely the content item will be highly regarded by others. Thus, the more trustworthy the author, the higher the measure of quality of the content item. Additionally, the higher the trust score (or reputation) of members who have liked, commented, or shared a content item, the higher the measure of quality of the content item.).
As per claims 19,32, Paulino in view of Gao teaches the method/computing apparatus of claim 16, 29, wherein the trust factor is determined based on a machine learning model that is continuously trained with data retrieved from the two or more data sources(Gao, 0051] …Another measure of quality is a trust score (or a reputation) related to the author of the content item. The more trustworthy the author, the more likely the content item will be highly regarded by others. Thus, the more trustworthy the author, the higher the measure of quality of the content item. Additionally, the higher the trust score (or reputation) of members who have liked, commented, or shared a content item, the higher the measure of quality of the content item [0077] Machine learning is the study and construction of algorithms that can learn from, and make predictions on, data. Such algorithms operate by building a model from inputs in order to make data-driven predictions or decisions. Thus, a machine learning technique is used to generate a statistical or classification model that is trained based on a history of attribute values associated with metadata, content items, and other data extracted from the videos. The machine-learned model is trained based on multiple attributes (or factors) described herein. In machine learning parlance, such attributes are referred to as “features.”). Therefore it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Paulino in view of Gao of trust/reputation score of an author and the use of machine learning to use machine learning for providing trust/reputation score of an author. One ordinary skill in the art would have been motivated to combine the teachings in order to provide an automated system for assignment of trust scores.
As per claim 20, Paulino in view of Gao teaches the method of claim 16 wherein the at least one data type comprises data type text, data type structured information, data type image, data type sound, data type video, and preprocessing data received from the data source according to the data type comprises: extracting content of the data by at least one of: for the data type text: applying natural language processing for translation, correction, formatting, speech tagging, and/or feature extraction of the text; for the data type structured information: organizing content according to tags; for the data type image: applying at least one of object detection, face detection, object segmentation, and object recognition; for the data type sound: applying speech recognition and/or natural language processing; and for the data type video: applying at least one of object detection, face detection, speech recognition, and natural language processing. (Paulino, [0058] Another example of filtering data includes removing reviews associated with a particular level of review feedback. Review feedback, as used herein, refers to other users or customer reviews or indications of a review. For example, in some services, an individual reading a review may include a thumbs up to support the review or indicate the review was helpful (e.g., provide a positive or “like” review feedback), while a thumbs down may be used to disagree with the review or indicate the review was unhelpful (e.g., provide a negative or “dislike” review feedback). In such a case, a review with negative feedback or an extent of negative review (e.g., threshold level of review) may be removed. For example, in cases where more than 50% of the review feedback is negative, the corresponding review may be removed.);
As per claim 23, Paulino in view of Gao teaches the method of claim 16 wherein the at least one data type comprises data type text, data type structured information, data type image, data type sound, data type video, and analyzing the information comprised by the data with respect to the characteristic comprises at least one of: for the data type text: applying natural language processing for analyzing word similarity and/or sentiments of authors; for the data type image: analyzing detected objects and/or faces with respect to at least one of facial expression, emotion, age, and gender; and for the structured data type: discovering rules in the structure data(Paulino, 0058] Another example of filtering data includes removing reviews associated with a particular level of review feedback. Review feedback, as used herein, refers to other users or customer reviews or indications of a review. For example, in some services, an individual reading a review may include a thumbs up to support the review or indicate the review was helpful (e.g., provide a positive or “like” review feedback), while a thumbs down may be used to disagree with the review or indicate the review was unhelpful (e.g., provide a negative or “dislike” review feedback). In such a case, a review with negative feedback or an extent of negative review (e.g., threshold level of review) may be removed. For example, in cases where more than 50% of the review feedback is negative, the corresponding review may be removed.);.
As per claim 28, Paulino in view of Gao teaches the method of claim 16 wherein the method is triggered periodically and/or in response to a request for recommendation request concerning the characteristic(Paulino, [0018] Accordingly, embodiments of the present technology are directed to efficient and programmatic generation of review summaries. In this regard, review summaries are efficiently and effectively generated in an automated manner such that the review summaries may be presented to a user. Generating a review summary in an automated manner reduces computing resources utilized to manually author a review summary. For example, a product (e.g., an application) does not need to be downloaded and used to generate a review. As another example, computing resources used to manually locate, read, and synthesize a set of reviews into a manually authored review summary are not needed. As described herein, in some cases, a generated review summary(ies) may be presented to a potential consumer of an item (e.g., a product). In this way, a potential consumer is presented with a summary of reviews, thereby reducing the additional computing resources consumed with a user reviewing various reviews.).
Claims 21, 22 rejected under 35 U.S.C. 103 as being unpatentable over US 2024/0346233 issued to Paulino et al.(Paulino) in view of US 2020/0213408 issued to Gao et al.(Gao) in view of US 2011/0173191 issued to Tsaparas et al.(Tsaparas).
As per claim 21, Paulino in view of Gao teaches the method of claim 16 wherein preprocessing the data from the data source according to a data type comprises, however does not explicitly teach extracting metadata of the data, which is taught by Tsaparas [0007] … A score may be generated for the review using the feature vector for the review and the review scoring model. Metadata associated with the received review may be received. One or more constraints may be applied to the generated score using the received metadata and the metadata associated with each author associated with the plurality of reviews. The constraints may be one or more of author consistency constraints, link consistency constraints, co-citation constraints, and trust consistency constraints. The metadata may be social networking data. The metadata may be an average review score for the author, the authors that link to or are associated with the author, and/or the review scores of authors that link to or are associated with the author. A feature vector may be determined for each review from the plurality of reviews using the received metadata associated with the author of the review and the text of each review and comprises determining a value for at least one text feature based on the text of each review. The text features may include at least one of text statistics features, syntactic analysis features, conformity based features, sentiment based features, or product feature based features.
Therefore it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Paulino in view of Gao of providing a review summary of product/services based on trusted review/reviewers to apply the teachings of Tsaparas of extracting metadata in order to provide the predictable result of providing a review summary of product/services based on trusted review/reviewers metadata.
One ordinary skill in the art would have been motivated to combine the teachings in order to determine the quality of product/services based on the trust score of an author(Gao, para.51) and scores of the review(Tsaparas, para.3)
As per claim 22, Paulino in view of Gao in view of Tsaparas teaches the method of claim 21 wherein metadata comprises at least one of descriptive metadata relating to at least one of title, subject, genre, and author, rights metadata relating to at least one of title, copyright status, rights holder, and license terms, technical metadata relating to at least one of file types, size, creation date, creation time, type of compression, uniform resource locator, and page rank, preservation metadata relating to an item's place in a hierarchy or in a sequence, and picture metadata relating to at least one of a timestamp, camera properties, resolution, size, and geotag(Tsaparas, ([0007] … A score may be generated for the review using the feature vector for the review and the review scoring model. Metadata associated with the received review may be received. One or more constraints may be applied to the generated score using the received metadata and the metadata associated with each author associated with the plurality of reviews. The constraints may be one or more of author consistency constraints, link consistency constraints, co-citation constraints, and trust consistency constraints. The metadata may be social networking data. The metadata may be an average review score for the author, the authors that link to or are associated with the author, and/or the review scores of authors that link to or are associated with the author. A feature vector may be determined for each review from the plurality of reviews using the received metadata associated with the author of the review and the text of each review and comprises determining a value for at least one text feature based on the text of each review. The text features may include at least one of text statistics features, syntactic analysis features, conformity based features, sentiment based features, or product feature based features.). Motivation to combine set forth in claim 21.
Claims 24-25, 33-34 rejected under 35 U.S.C. 103 as being unpatentable over US 2024/0346233 issued to Paulino et al.(Paulino) in view of US 2020/0213408 issued to Gao et al.(Gao) in view of US 2020/0293586 issued to Singhal et al.(Singhal).
As per claims 24, 33, Paulino in view of Gao teaches the method/computing apparatus of claim 16, 29, wherein analyzing the information comprised by the data with respect to the characteristic comprises, however does not explicitly teach determining a relevance score for the data with respect to the characteristic and a confidence value of the relevance score being correct, which is taught by Singhal [0022] Confidence scores may be generated in any suitable manner, e.g., using any suitable combination of state-of-the-art and/or future machine learning (ML), artificial intelligence (AI), and/or natural language processing (NLP) techniques. For example, confidence scores may be generated by an AI, ML, and/or NLP model based on input data including any suitable combination of the search query, personalization data for a user, and/or data related to candidate rich segment experiences to be provided for the search query. In some examples, the model may be trained to generate confidence scores with regard to a classification task, e.g., for classifying relevance, predicting user satisfaction, and/or correctly generating relevant rich segment experiences as compared to “ground truth” examples of relevant rich segment experiences. For example, the model may be given input data and configured to output a relevance score, user satisfaction score, and/or select a rich segment experience. The output of the model may be assessed according to a loss function (e.g., a loss function measuring accuracy of scoring and/or selection). The model may be configured to output one or more confidence values for the classification task (e.g., confidence values for different classification results regarding user satisfaction and/or relevance, confidence values for the relevance of different candidate rich segment experiences, etc.). Similarly, instead or in addition to using a loss function with regard to a classification task, the model may be trained via reinforcement learning with regard to reinforcement signals, e.g., with regard to user satisfaction or user-provided feedback regarding relevance of selected rich segment experiences. In either case, whether loss function or reinforcement learning is used, the system may be adjusted over time to “penalize” incorrect actions by adjusting parameters of the system so that the incorrect actions are less likely in the future given similar inputs, and to “reward” correct actions by adjusting parameters of the system so that the correct actions are more likely in the future. The loss function and/or reinforcement may be configured to more heavily reward and/or penalize answers in proportion to the confidence value. Accordingly, the confidence value may be adjusted based on the loss function, e.g., to penalize confident but incorrect answers, and/or to reward confident, correct answers. Accordingly, the system may be trained to emit a relatively high confidence value when it is likely to be correct, and to emit a relatively low confidence value when a correct answer cannot be confidently predicted.
Therefore it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Paulino in view of Gao of providing a review summary of product/services based on trusted review/reviewers to apply the teachings of Singhal of determining relevance score and confidence score of the relevance score being correct in order to provide the predictable result of providing a review summary of product/services based on trusted review/reviewers with a relevancy and confidence.
One ordinary skill in the art would have been motivated to combine the teachings in order to determine the quality of product/services based on the trust score of an author(Gao, para.51).
As per claims 25,34, Paulino in view of Gao in view of Singhal teaches the method/computing apparatus of claim 24,33, wherein the enrichment process is based on a machine learning model, and an input to the machine learning model comprises the relevance score for the data with respect to the characteristic, the confidence value of the information being correct(Singhal, [0022]), the trust factor of the data source of the information(Gao, ([0051]), a weight factor of the information(Paulino, [0020][0037]), and a time stamp(Paulino, [0038]).),
Claim 26 rejected under 35 U.S.C. 103 as being unpatentable over US 2024/0346233 issued to Paulino et al.(Paulino) in view of US 2020/0213408 issued to Gao et al.(Gao) in view of US 2020/0293586 issued to Singhal et al.(Singhal) in view of US 2011/0173191 issued to Tsaparas et al.(Tsaparas).
As per claim 26, Paulino in view of Gao in view of Singhal teaches the method of claim 25 wherein the input to the machine learning model however does not teach information extracted from metadata, which is taught by Tsaparas [0007] … A score may be generated for the review using the feature vector for the review and the review scoring model. Metadata associated with the received review may be received. One or more constraints may be applied to the generated score using the received metadata and the metadata associated with each author associated with the plurality of reviews. The constraints may be one or more of author consistency constraints, link consistency constraints, co-citation constraints, and trust consistency constraints. The metadata may be social networking data. The metadata may be an average review score for the author, the authors that link to or are associated with the author, and/or the review scores of authors that link to or are associated with the author. A feature vector may be determined for each review from the plurality of reviews using the received metadata associated with the author of the review and the text of each review and comprises determining a value for at least one text feature based on the text of each review. The text features may include at least one of text statistics features, syntactic analysis features, conformity based features, sentiment based features, or product feature based features.).
Therefore it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Paulino in view of Gao in view of Singhal of providing a review summary of product/services based on trusted review/reviewers to apply the teachings of Tsaparas of extracting metadata in order to provide the predictable result of providing a review summary of product/services based on trusted review/reviewers metadata.
One ordinary skill in the art would have been motivated to combine the teachings in order to determine the quality of product/services based on the trust score of an author(Gao, para.51) and scores of the review(Tsaparas, para.3)
Claim 27 rejected under 35 U.S.C. 103 as being unpatentable over US 2024/0346233 issued to Paulino et al.(Paulino) in view of US 2020/0213408 issued to Gao et al.(Gao) in view of US 2014/0223284 issued to Rankin, JR et al.(Rankin).
As per claim 27, Paulino in view of Gao teaches the method of claim 16 wherein applying the enrichment process to select the information for data enrichment related to the characteristic based on the analysis of the information and the trust factor however does not explicitly teach determining a probability value for the information for data enrichment being correct; and in response to the probability value being higher than a threshold value, selecting the information, which is taught by Rankin, [0048] In one embodiment, the ML algorithm may provide an ML model that may be used to "mimic" human annotations automatically. The model may be used to extract relevant information from documents. A portion of the automatically annotated documents may set aside for human validation (based on ML confidence score, i.e. a threshold probability that the extracted information is correct).
Therefore it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Paulino in view of Gao of providing a review summary of product/services based on trusted review/reviewers to apply the teachings of Rankin of determining probability of extracted information is correct and higher than a threshold in order to provide the predictable result of providing a review summary of product/services based on trusted review/reviewers and probability that the information is correct.
One ordinary skill in the art would have been motivated to combine the teachings in order to determine the quality of product/services based on the trust score of an author(Gao, para.51).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
US 2014/0280017 issued to Indarapu et al. teaches extraction of pieces of content from the Internet, social networks, in particular, and/or of a user's immediate circle of friends and family, where the content relates to trending topics of those networks as relevant to a search query. The content effectively summarizes a trending topic (e.g., of a single social network or across multiple social networks). The summary can be characterized by different content types, such as images only, text only, and so on. The extracted content can then be presented in a number of different ways such as in a display that visually rotates through the different types of content, such as trending images, summarizing updates, and trending webpages, for example. Another technique for presentation of the extracted social content can be as user-selectable "hotspots" on a page or desktop, and which can display the trending topic summaries.
US 2013/0114105 issued to Liu et al., teaches Semantically ranking content in a website with a computerized ranking device includes: parsing content from the website into multiple autonomous content blocks with the computerized ranking device and assigning an importance ranking with said computerized ranking device to each of the content blocks based on a degree to which a substance of the content block is relevant to one of a plurality of predefined categories.
US 2016/0092557 issued to Stojanovic et al. teaches performing similarity metric analysis and data enrichment using knowledge sources. A data enrichment service can compare an input data set to reference data sets stored in a knowledge source to identify similarly related data. A similarity metric can be calculated corresponding to the semantic similarity of two or more datasets. The similarity metric can be used to identify datasets based on their metadata attributes and data values enabling easier indexing and high performance retrieval of data values. A input data set can labeled with a category based on the data set having the best match with the input data set. The similarity of an input data set with a data set provided by a knowledge source can be used to query a knowledge source to obtain additional information about the data set. The additional information can be used to provide recommendations to the user.
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/BACKHEAN TIV/
Primary Examiner
Art Unit 2459
Prosecution Insights