Abstract: In some implementations, the techniques described herein relate to a method including: (i) obtaining, by a processor, at least one statistic related to a recent history of a fantasy athletic team managed by a user within a fantasy athletic league, (ii) creating, by the processor, a prompt for a large language model (LLM), the prompt comprising, the at least one statistic and a set of constraints configured to produce as output from the LLM a fantasy team recap that conforms to at least one predetermined guideline, (iii) providing, by the processor, the prompt to the LLM as input, and (iv) causing display, by the processor, of the fantasy team recap output by the LLM in response to the prompt.

Abstract: Techniques for evaluating a user experience experiment designed to use one user experience variant selected from a number of user experience variants as a global-best user experience variant to be used across users relative to a machine model trained to use user data to identify a user-preferred user experience variant. Disclosed systems and methods provide techniques for optimizing user response. In one embodiment, a global-best user experience variant is evaluated by comparing an aggregate user response determined for the global-best user experience variant to an aggregate user response determined using user response predictions determined using the trained machine model, and using the outcome of the comparison to make a recommendation as to which one of the global-best user experience variant and the trained machine model to adopt for providing a user experience to users.

Abstract: The present teaching relates to providing content. Online documents are searched based on a query from a user. For each searched online document, enriched information thereof is created based on social data associated thereto and, if existing, private information previously provided by the user to privately evaluate the online document. An enhanced search result is generated based on the online documents with their respective enriched information and provided to the user in response to the query.

Abstract: In accordance with the present disclosure, one or more computing devices and/or methods are provided. In an example, a query may be received from a client device. In response to the query, a set of search results corresponding to a set of internet resources associated with the query may be generated. The set of search results may include a first search result corresponding to a first internet resource of the set of internet resources. A first popularity score associated with a first search result of the set of search results may be determined based upon one or more first user engagement metrics associated with the first internet resource. A search interface may be displayed on the client device. The search interface may include a first representation of the first search result and a first graphical popularity indicator indicative of the first popularity score.

Abstract: In some implementations, the techniques described herein relate to a method including: (i) identifying, by a processor, a generative machine learning model trained on image data, (ii) generating, by the generative machine learning model executed by the processor, an image based on at least one parameter, (iii) editing, by an image-editing algorithm executed by the processor, the image to comprise a specified string of text in a selected area of the image, and (iv) causing display, by the processor, of the edited image.

Abstract: The present teaching relates to merging data sketches from different data sketch sources. To satisfy performance metrics specified with respect to quality of a merged sketch, sketch merging parameters are generated, which include a first set of parameters for creating obfuscated keys of the data sketches and a second set of parameters for identifying matching obfuscated keys. Keys of the data sketches are obfuscated using the first set of sketch merging parameters. The data sketches are merged by identifying matching obfuscated keys in accordance with the second set of sketch merging parameters to generate a merged sketch.

Abstract: Disclosed are systems and methods that provide a decision-intelligence (DI)-based, computerized framework for deterministically identifying and extracting content from network resources, and generating focused content based therefrom for delivery to electronic users. The framework enables real-time customization of extraction tasks, which can cause tailored, accurate results that are contextually relevant and tied into the purpose of the extraction task. This data can then be compiled and/or leveraged to generate content campaigns that can target specific sets of users, geographies, time periods, trends and the like. The framework can leverage a large language model (LLM) to seamlessly extract relevant information from network resources, which enables the generation and execution of extraction requests that can be dynamically executed and updated, which can enable the framework to “drill-down” on contextual and/or topical aspects of categories of data.

Abstract: The present teaching relates to identify events of interests. Given each of video clips, each capturing an event of interest, spatial attention regions are identified therefrom, each of which includes objects that meet a first condition. A temporal attention region is determined in each video clip according to a second condition. An action that causes an event of interest in the temporal attention region is labeled. The video clips, the respective spatial/temporal attention regions, and the action labels are then used to generate training data for machine learning of models for automatically determining, from an input video clip, a temporal attention zone for an event of interest and an action that causes the event of interest.

Abstract: Techniques for automatically generating a natural language (NL) translation of computer code are disclosed. In one embodiment, a computer-implemented method is disclosed comprising receiving, from a user, a code translation request in connection with code generated by a code generation system based on natural language (NL) input, analyzing the computer-generated code and generating a natural language (NL) translation of the computer-generated code based on the analysis, generating a graphical user interface (GUI) comprising the NL input, the NL translation of the computer-generated code and GUI control elements for receiving input from the user in connection with at least one of the NL input and the NL translation; causing the GUI to be displayed at a client device of the user, and receiving input from the user via at least one GUI control element and causing performance of at least one operation in response to the input.

Abstract: The present teaching relates to method, system, medium, and implementations for personalized content service. Information related to a user is first obtained with a user profile indicative of multiple interests of the user. User embeddings are computed with respect to some interests of the user based on interest embeddings of such interests to capture semantics of such interests as well as additional interests temporally related to the interests. Personalized content is identified based on the user embeddings and is provided to the user.

Abstract: In some implementations, the techniques described herein relate to a method including (i) receiving, by a processor, user input describing at least one parameter for a query image, (ii) generating, via a generative machine learning model executed by the processor, the query image based at least in part on the user input describing the at least one parameter for the query image, (iii) providing, by the processor, the query image as input to an image-based search algorithm, and (iv) returning a result received by the processor from the image-based search algorithm.

Abstract: The present teaching relates to method, system, medium, and implementations for identifying k nearest neighbors. A plurality of combined neighborhoods are received from a plurality of local join executors. Each combined neighborhood represents a neighborhood of a source data point and has one or more pairs of neighbors, each of which includes the source data point, a neighbor of the source point, and a distance in-between. A plurality of KNN lists corresponding to a plurality of source data points are obtained. Each KNN list includes K neighbors to a corresponding source data point, each of which is represented by an index of the neighbor and a distance between the source data point and the neighbor. The plurality of KNN lists are updated based on the plurality of combined neighborhoods to generate updated KNN lists.

Abstract: The present teaching relates to identifying content that matches a query. Train data include queries, advertisement, and hyperlinks associated with query sessions. A plurality of subwords for each of the queries in the training data are identified. A query/ads model is then trained by optimizing vectors associated the plurality of subwords for each of the queries, advertisements, and hyperlinks in the training data with respect to an objective function. At least one vector associated with each of the queries is derived based on the plurality of subword vectors in the query/ads model that represent the plurality of subwords of the query.

Abstract: In some implementations, the techniques described herein relate to a method including: (i) identifying, by a processor, an electronic message addressed to an inbox of a user that comprises a confirmation of a transaction involving a platform, (ii) searching, by the processor, for an additional electronic message addressed to the inbox indicating an alteration applicable to the transaction, (iii) causing display, by the processor, of a prompt informing the user of the alteration applicable to the transaction, (iv) composing, by a large language model (LLM) executed by the processor, a potential electronic message to an operator of the platform requesting the alteration be retroactively applied to the transaction, and (v) in response to receiving user input regarding the potential electronic message, sending, by the processor, a subsequent electronic message to the operator of the platform requesting the alteration be retroactively applied to the transaction.

Abstract: A location-aware search assist capability identifies location-aware search query suggestions using location information associated with the location-aware search query suggestions. A user's search query input and location and a location associated with each location-aware search query suggestion candidates may be used to identify a set of search query suggestions for presentation to the user. Location-aware search query suggestion candidates may be ranked in accordance with a closeness of each one's location to the user's location. The ranking may be performed using a score, such as a popularity score associated with each search query suggestion candidate. The location-aware search query suggestion candidates having a location closer to the user's location may be promoted by adjusting each candidate's popularity score upward, and the search query suggestion candidates that are farther away from the user's location may be demoted by adjusting each such candidate's popularity score downward.

Abstract: An information processing apparatus according to the present application includes the generation unit, the evaluation unit and the reinforcement learning unit. The generation unit generates suggestion information from predetermined content using the generation model which generates from content, suggestion information indicating content of the content. The evaluation unit evaluates the suggestion information generated by the generation unit using the evaluation model which has learned which of a plurality of pieces of suggestion information indicating content of the same target content is favorable. The reinforcement learning unit performs reinforcement learning of the generation model on the basis of the evaluation result by the evaluation unit.

Abstract: The present teaching relates to searching. In one example, a search query is received from a person. A plurality of search results are retrieved based on the search query. An intent of the person is estimated with respect to at least some of the plurality of search results. The estimated intent is what the person intends to do with respect to the at least some of the plurality of search results. The plurality of search results are provided based on the estimated intent of the person.

Abstract: The instant system and methods solves the cold start problem through various systems and methods directed to aggregating user interaction data associated with a user over a period of time, generating an embedding model based on the aggregated user interaction data, generating a content embedding vector based on the embedding model, generating an embedding profile vector based on the embedding model, storing the embedding profile vector in a storage device, receiving each of the content embedding vector and embedding vector profile for training a ranking model, and generating a predicted list of one or more content items of interest for recommending to the user.

Abstract: In some implementations, the techniques described herein relate to a method including: receiving a training data set, the training data set including data representing consumer interactions and actions taken by consumers after the consumer interactions; executing a training run using a predictive model, the predictive model including a plurality of trainable parameters; computing a loss of the training run using a loss function, the loss function including a strict convex function; optimizing the plurality of trainable parameters based on an output of the loss function; storing the trainable parameters as an inference model; and predicting a future ratio using the inference model.

Abstract: One or more computing devices, systems, and/or methods for implementing an automated model update pipeline are provided. User behavior data associated with content provided to users may be collected. An automatic model training is invoked to train a new model to output a set of model parameters based upon a configuration specifying a target audience, features extracted from user behavior data, and training model parameters. In response to determining that the new model will outperform a deployed model on a content serving platform, an automatic model updater is invoked to update the content serving platform with the new model and a ranking profile of the new model for serving content requests.

Abstract: The disclosed systems and methods provide a novel action prediction framework that performs personalized action prediction. According to an embodiment, the disclosed framework is able to dynamically predict which action (if any) a user might perform in response to receiving a given message. In some embodiments, for a given message, the action prediction framework can determine the probability that a user (e.g., sender, recipient) associated with the message may perform an action or set of action actions (e.g., open, forward, delete, reply, archive) related to the message. In some embodiments, the framework may be used to suggest a predicted action to the user. In some embodiments, a computing device may use the predicted actions to automatically perform the action. According to an embodiment, the action prediction framework includes a multi-label or multi-class model using a neural network.

Abstract: The present teaching relates to method, system, medium, and implementations for approximating a non-linear relationship between a numerical feature and an output of a model. A value of a numerical feature is received and is transformed, via a transform function, into a transformed value within a fixed range. With respect to each of a plurality of basis functions used for approximating the non-linear relationship, a respective basis function value of the basis function is computed based on the transformed value. An approximated value of the non-linear numeric feature is generated based on a sum of the plurality of basis function values weighted respectively by each corresponding one of a set of the weights, obtained via machine learning.

Abstract: One or more computing devices, systems, and/or methods are provided. In an example, a first performance metric score may be determined based upon first content item text. A plurality of similarity scores associated with a plurality of sets of content item text may be determined. One or more sets of content item text may be selected from among the plurality of sets of content item text based upon the plurality of similarity scores and a plurality of performance metric scores associated with the plurality of sets of content item text. The plurality of performance metric scores may comprise one or more performance metric scores associated with the one or more sets of content item text. The one or more performance metric scores may be higher than the first performance metric score. One or more representations of the one or more sets of content item text may be displayed.

Abstract: One or more computing devices, systems, and/or methods for classifier validation are provided. A set of in-sample examples are partitioned into a reduced in-sample set and a remaining in-sample set. The reduced in-sample set is processed using a set of classifiers. A subset of classifiers are identified as having error counts, over the reduced in-sample set, below a threshold number of errors. A training procedure is executed to select a classifier having a minimum error rate over the set of in-sample examples. If the classifier is within the subset of classifiers, then an out-of-sample error bound is determined for the classifier.

Abstract: Disclosed are systems and methods that provide a decision-intelligence (DI)-based, computerized framework for compiling and leveraging reliable sequence taggings for input queries related to executed searches. The disclosed framework can compile a trained computer model to fulfill partially labeled queries tagged by AI models as fully labeled queries. The disclosed framework can further leverage other AI models (e.g., deep neural networks, knowledge graphs, and the like), so that cross-checks can be performed between different models to guarantee high quality of labeled tokens. Thus, the framework can automatically generate and implement reliable training data to train a sequence tagging model for search query understanding. Thus, the search engine operating on such tagging model can provide improved results.

Abstract: The present teaching relates to content recommendation. Content is selected from multiple pieces of content for recommending to a user based on a user embedding and contextual information. Performance of the user with respect to the recommended content is obtained. The user embedding for the user is adapted based on the performance information via a memory-dependency aware model previously trained to learn past interests of the user and intensities of such past interests. An updated user embedding is generated to represent current interests of the user via the adapted user embedding produced by the memory-dependency aware model according to the known intensities of the past interests of the user as well as user's interest exhibited in the recommended content.

Abstract: Embodiments of the present invention are directed to detecting misuse of contextual data, such as user data. In accordance with some embodiments of the present invention, artificial contextual data is created for artificial users and is, thereafter, provided to a bidder(s). Based on reception of artificial contextual data, bidders can place bids for presentation of advertisements. Bids placed by bidders receiving such artificial context data can be collected and analyzed. When bids appear to be atypically increased or decreased based on the artificial contextual data, a determination can be made that the bidder retains and/or misuses contextual data, such as user data.

Abstract: The present teaching relates to online user profiling. In one example, content associated with a first user of a social media network is obtained. From the content associated with the first user, a first link to a first piece of content is identified. A second user of the social media network associated with the first user is determined in the context of the social media network. From content associated with the second user of the social media network, a second link to a second piece of content is identified. The first and second pieces of content are retrieved based on the first and second links, respectively. User profile of the first user is generated based, at least in part, on the first and second pieces of content.

Abstract: Disclosed are systems and methods for generating recommendations based on online history information and geospatial data. The disclosed systems and methods provide a novel framework for automating the transmission of electronic recommendations to users by consistently monitoring electronic information regarding a user's interests correlating that data with inventory information and geospatial information. The disclosed framework operates by leveraging available user data, user provided objectives, or various real-time data associated with a user, and using the aforementioned data as inputs for recommendation models, in order to generate and transmit the optimal recommendations to the user.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content searching, hosting and/or providing systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide a novel framework for providing users with electronic retrieval capabilities that are activated upon the users' determined locations respective to real-world locations associated with a message providing entity. The disclosed technology combines the previously separate systems of mail extraction, geo-fencing and content delivery (e.g., notification) into a single system that efficiently manages a user's inbox in order to provide the user with content the user has expressly indicated is of interest to that user.

Abstract: In an example, first entities are extracted from user profiles. Second entities are extracted from content information associated with content item. User-associated metrics associated with the first entities are determined based upon the user profiles and/or content events. First vector representations of the first entities and second vector representations of the second entities are processed to generate an attention distribution array. Each value of the attention distribution array represents, for a user interested in an entity of the first entities, a proportion of (i) entity-specific activity, of the user, related to an entity of the second entities relative to (ii) an entirety of activity of the user. An inferred activity distribution array is generated by applying the user-associated metrics to the attention distribution array. A filtered subset of activity distribution values is generated by pruning values from the inferred activity distribution array.

Abstract: Techniques for evaluating a user experience experiment designed to use one user experience variant selected from a number of user experience variants as a global-best user experience variant to be used across users relative to a machine model trained to use user data to identify a user-preferred user experience variant. Disclosed systems and methods provide techniques for optimizing user response. In one embodiment, a global-best user experience variant is evaluated by comparing an aggregate user response determined for the global-best user experience variant to an aggregate user response determined using user response predictions determined using the trained machine model, and using the outcome of the comparison to make a recommendation as to which one of the global-best user experience variant and the trained machine model to adopt for providing a user experience to users.

Abstract: The disclosed systems and methods provide a novel framework that provides mechanisms for performing cost-effective, accurate and scalable detection and recognition of fine-grained events. The framework functions by training high precision and high recall object/optical character recognition (OCR) models and aligning video frames to text commentaries of the videos (e.g., licensed play-by-play). The disclosed framework operates as a single algorithm that performs multimodal alignments between events/actions within videos and their prescribed text. Thus, the disclosed framework is able to scale to fine-grained action categories across different venues by delving into the key frames and key aspects of a video to identify particular actions performed by particular actors, thereby providing the novelty of fine-granted action detection and recognition.

Abstract: In accordance with the present disclosure, one or more computing devices and/or methods are provided. In an example, an emotional state of a user may be determined based upon one or more signals generated by one or more sensors and/or user activity of the user. A communication interface may be controlled based upon the emotional state.

Abstract: The present teaching relates to method and system for generating a stream of content items. A plurality of entities associated with a time-window are obtained, wherein each entity of the plurality of entities is associated with at least one content item. For each entity, a first parameter with respect to the time-window, a second parameter with respect to previous time-windows, and a trendiness score based on a function of the first parameter and the second parameter are respectively calculated. A graph based on one or more entity-pairs is generated, wherein each entity-pair of the one or more entity-pairs satisfies a first criterion. A stream of content items is generated based on the graph, wherein each content item in the stream of content items corresponds to at least one of the one or more entity pairs.

Abstract: Technologies for enhancing work scheduling of a big data framework. The technologies can include generating, in a database, configurable namespaces to be used by a work scheduling enhancement application to group together tasks of one or more big data clusters. The namespaces can be hierarchical. The technologies can also include linking in the database, by the application, related tasks with respective namespaces to categorize and group together the related tasks. The technologies can also include configuring, by the application, a display scheme for displaying error handling and root cause analysis of tasks of the one or more big data clusters. The technologies can also include generating or rendering, by the application, a GUI having a navigable hierarchal view for displaying the namespaces. The generation or rendering of the GUI can be based partially on the display scheme.

Abstract: One or more computing devices, systems, and/or methods are provided. Machine learning model training may be performed using first training data to generate a first machine learning model. Inference-training data may be generated, wherein the inference-training data may include a plurality of sets of training data of the first training data, a plurality of sets of inference data, and/or target information indicative of the plurality of sets of training data being associated with a first classification and the plurality of sets of inference data being associated with a second classification. Machine learning model training may be performed using the inference-training data to generate a second machine learning model. Predictions associated with one or more sets of data may be determined using the second machine learning model. An evaluation of the first machine learning model and the one or more sets of data may be generated based upon the predictions.

Abstract: The disclosed systems and methods provide a novel framework that provides on-device functionality to user devices for localized content ranking, modification and rendering. The disclosed systems and methods provide functionality for on-device personalization in a real-time, secure and network anonymous manner. Rather than exposing a user's data to the network for content tailoring, the disclosed framework performs the ranking and content manipulation locally on the user's device. The disclosed framework enables locally (on-device) built, updated and hosted user profiles to be used to tailor received content for display on a user device. This ensures the integrity of the personalization while maintaining security for the user's personalized data and activities.

Abstract: In some implementations, the techniques described herein relate to a method including: (i) identifying, by a processor, a generative machine learning model trained on image data, (ii) generating, by the generative machine learning model executed by the processor, an image based on at least one parameter, (iii) editing, by an image-editing algorithm executed by the processor, the image to comprise a specified string of text in a selected area of the image, and (iv) causing display, by the processor, of the edited image.

Abstract: A facility for processing a search query is described. The facility identifies one or more items that satisfy the query, at least one of which is a media sequence. For each identified media sequence, the facility identifies an advertising message based upon the contents of the query. In response to the query, the facility returns a search result that indicates the identified items. When one of the identified media sequences is selected in the search result, the selected media sequence is provided in conjunction with the advertising message identified for it.

Abstract: Various embodiments of this disclosure relate generally to utilizing a machine-learning model to process natural language email instructions. The method comprises receiving, by one or more processors, at least one instruction from a user via an email interface displayed on a user device, the email interface corresponding to an email application, processing, by a trained machine-learning model, the at least one instruction to determine at least one response, wherein processing the at least one instruction includes applying at least one persona of the user to the at least one instruction, performing, by the one or more processors, at least one action or at least one query corresponding to the at least one response, and displaying, by the one or more processors, a confirmation on the email interface of the user device that the at least one response was completed.

Abstract: Disclosed are systems and methods for receiving a meeting request via an electronic account of a user; determining a conflict between the meeting request and a pre-existing meeting, extracting and comparing information associated with each meeting based on an analysis of each meeting; determining a priority associated with each meeting based on the analysis of the information of each meeting; and utilizing a machine learning model to generate an output associated with the priority of each meeting to the electronic account of the user.

Abstract: One or more computing devices, systems, and/or methods for generating and providing recommendations of products are provided. For example, content is extracted from a message sent to a user. The content is evaluated to identify a product identifier corresponding to a product title of a product. If the product identifier is a truncated version of the product title, then a database of product titles and frequencies of occurrence of the product titles is used to complete the product title. A model is used to infer a product category for the product title. Matching scores are assigned to products within a product category based upon weighted attributes. A recommendation is provided to the user for a product having a matching score greater than a matching threshold.

Abstract: The present teaching relates to entity extraction and disambiguation. In one example, an entity name extracted from a data source associated with a user is obtained. One or more entity types associated with the entity name are determined. One or more entity candidates are identified with respect to each of the one or more entity types. An entity candidate is selected with respect to one of the one or more entity types to be an individual associated with the entity name.

Abstract: The disclosed systems and methods provide a novel action prediction framework that performs personalized action prediction. According to an embodiment, the disclosed framework is able to dynamically predict which action (if any) a user might perform in response to receiving a given message. In some embodiments, for a given message, the action prediction framework can determine the probability that a user (e.g., sender, recipient) associated with the message may perform an action or set of action actions (e.g., open, forward, delete, reply, archive) related to the message. In some embodiments, the framework may be used to suggest a predicted action to the user. In some embodiments, a computing device may use the predicted actions to automatically perform the action. According to an embodiment, the action prediction framework includes a multi-label or multi-class model using a neural network.

Abstract: One or more computing devices, systems, and/or methods are provided. A machine learning model may be trained using a plurality of sets of information. One or more pruning operations may be performed in association with the training to generate a machine learning model with a sparse set of field weights associated with feature fields associated with features of the plurality of sets of auction information. A request for content associated with a client device may be received. A set of features associated with the request for content may be determined. Positive signal probabilities associated with a plurality of content items may be determined using the machine learning model based upon field weights, of the machine learning model, associated with the set of features. A content item may be selected from the plurality of content items for presentation via the client device based upon the positive signal probabilities.

Abstract: The present teaching relates to personalized content recommendation. A webpage is contrasted for a user having a plurality of slots each of which is to be allocated with a content item. For each of the plurality of slots, a plurality of content items in a plurality of types of content are accessed. For each of the plurality of types of content, a personalized score is predicted for each content item in the type of content, wherein the personalized score is obtained based on a trained model trained. A recommended content item of the type of content is selected based on personalized scores. An overall recommended content item is selected and allocated to a slot based on criteria associated with the personalized scores of the recommended content items and a business rule. The webpage with the plurality of slots allocated with content items is provided to the user.

Abstract: One or more computing devices, systems, and/or methods for generating a list of suggested queries associated with one or more keywords are provided. For example, one or more keywords may be received via a search interface. A plurality of queries associated with the one or more keywords may be determined based upon the one or more keywords and a historical query database. A plurality of relationship scores associated with the plurality of queries may be generated based upon a plurality of search sessions associated with the historical query database. The historical query database may be analyzed to determine a plurality of click rates associated with the plurality of queries. A list of suggested queries may be generated based upon the plurality of relationship scores and the plurality of click rates.

Abstract: In one embodiment, a method for creating one or more infographics, comprising: receiving and storing data associated with an individual or an entity, in a format according to a schema that includes at least two properties associated with the individual or entity; reading at least a portion of the data; determining which of the at least two properties in the schema do not have corresponding read data associated with the individual or entity; based on that determination, selecting an infographic definition from among a plurality of infographic definitions defining the appearance of at least a portion of an infographic; generating one or more infographics based on (i) the at least a portion of the data and (ii) the selected infographic definition; and providing the one or more generated infographics to an output device.

Abstract: In an example, first-tier Internet Protocol (IP) address reputation scores, including a first first-tier IP address reputation score associated with a first first-tier IP address group and a second first-tier IP address reputation score associated with a second first-tier IP address group, may be determined based upon a plurality of events. A second-tier IP address reputation score associated with a second-tier IP address group may be determined based upon the plurality of first-tier IP address reputation scores. The second-tier IP address group may include the first first-tier IP address group and the second first-tier IP address group. An IP address reputation profile may be generated based upon the first-tier IP address reputation scores and the second-tier IP address reputation score. Whether or not a request for content associated with a first IP address is fraudulent may be determined based upon the IP address reputation profile and the first IP address.