Abstract: Embodiments of the present disclosure provide a method and a device for music play. The method comprises receiving a first operation instruction in a target application for playing music; in response to the first operation instruction, presenting a first interface of the target application, the first interface including an operation control for enhancing a play effect of the music through at least one processing, the processing being used for representing music content in a way more than sound; receiving a second operation instruction for the operation control; processing the music based on the second operation instruction during a process of playing the music.

Abstract: A method including a user who has brand affinities about a product brand or a product brand category. The method can include analyzing whether the user has brand affinities for the product brand or the brand category with two binary classification models that can include (a) a model one to determine an affinity to a particular product brand and (b) a model two to determine an affinity to a particular product brand category. The method also can include analyzing whether the user has affinities for a similar product brand or a similar product brand category by a model three neural network. The method can include displaying recommendations and the promotions for a product brand, a product brand category, a similar product brand, or a similar product brand category to the user. Other embodiments are disclosed.

Abstract: An iterative attention-based neural network training and processing method and system iteratively applies a focus of attention of a trained neural network on syntactical elements and generates probabilities associated with representations of the syntactical elements, which in turn inform a subsequent focus of attention of the neural network, resulting in updated probabilities. The updated probabilities are then applied to generate syntactical elements for delivery to a user. The user may respond to the delivered syntactical elements, providing additional training information to the trained neural network.

Abstract: A component of the technical system, a component designation and a characteristic parameter designation for a characteristic parameter of relevance to the design of the components are read and a search engine is queried with same is provided. The documents found by the search engine are read and component information, e.g. product information concerning a specific component, is extracted from said documents. The extracted component information is supplied to a machine learning routine which has been trained, using a plurality of predefined training component information and training characteristic parameter values, to reproduce predefined training characteristic parameter values using predefined training component information. Output data from the machine learning routine is selected as characteristic parameter values and inserted into a planning data record. The planning data record is then output to design the technical system.

Abstract: A system receives feature parameters, each identifying possible values for one of a set of features. The system receives outcomes corresponding to the feature parameters. The system generates a simulated patient population dataset with multiple simulated patient datasets, each simulated patient dataset associated with the outcomes and including feature values falling within the possible values identified by the feature parameters. The system may train a machine learning engine based on the simulated patient population dataset and optionally additional simulated patient population datasets. The machine learning engine generates predicted outcomes based on the training in response to queries identifying feature values.

Abstract: Certain aspects of the present disclosure provide techniques for sharing machine learning models and an indication of transmission and reception points (TRPs) for which the machine learning models are applicable between wireless nodes such as user equipments (UEs) and base stations (BSs). For example, a UE receives an indication of one or more machine learning models associated with at least one TRP of one or more TRPs from a BS. The UE then uses the at least one of the one or more machine learning models for applications related to the at least one TRP. The applications may include a channel state information (CSI) compression, a cross frequency channel prediction, and a beam selection.

Abstract: A label switching router for a switching network configured for source routing can be configured to modify one or more reported properties of a link associated with that router based on one or more link quality metrics. For example, a router can include a link with available bandwidth of 10 Gbps, a value that can be used by a headend when evaluating nodes of the network against a set of constraints required by a particular requested connection or tunnel. A link of that router may exhibit an increased bit error rate which can be used by the label switching router to artificially deflate available bandwidth, thereby reducing the number of label switching paths including the router are selected by the headend.

Abstract: A method for dynamically allocating server resources includes receiving a request from a client system, wherein the request comprises a request for a first set of streaming data, providing from the server to the client system a first portion of streaming data from the first set of streaming data, wherein the first portion is associated with a first quality of service level, receiving user activity data from the client system for the first portion of the streaming data, determining a second quality of service level for a second portion of the streaming data from the first set of streaming data, providing from the server to the client system the second portion of streaming data from the first set of streaming data, wherein the second portion provided with the second quality of service level, and wherein the first quality of service level is different from the second quality of service level.

Abstract: Artificial Intelligence (AI) based systems and methods are disclosed for implementing patient-specific medical adherence intervention. An AI model is trained on a first set of data comprising (1) patient data defining a plurality of medical patients having respective prescribed medications and (2) intervention communication data. During a first iteration of a medication adherence session, a second set of data comprising patient data of a new or existing medical patient is received and input by the AI model to generate a medical adherence intervention prediction defining at least a preferred intervention channel specific to the new or existing medical patient. An electronic medical adherence intervention communication, is generated, and related correspondence is sent, to the new or existing medical patient based on (1) the medical adherence intervention prediction and (2) an intervention template corresponding to the preferred intervention channel.

Abstract: In some implementations, a language proficiency of a user of a client device is determined by one or more computers. The one or more computers then determines a text segment for output by a text-to-speech module based on the determined language proficiency of the user. After determining the text segment for output, the one or more computers generates audio data including a synthesized utterance of the text segment. The audio data including the synthesized utterance of the text segment is then provided to the client device for output.

Abstract: A computer-implemented system is provided that includes at least one processor that is adapted to analyze a plurality of frames from a real-time video to identify frames during which an operator is interacting with an image device to examine areas of a patient. The at least one processor is further configured to generate, from the identified frames, data representations of a first area examined by the operator interacting with the image device and further generate data representations of one or more further areas examined by the operator interacting with the image device.

Abstract: A method, computer program product, and computer system identifying intent relating to a query associated with an object. A plurality of informational items associated with the object may be identified. At least a first informational item associated with the object for which a first portion of information is already known may be identified. At least a second informational item associated with the object for which a second portion of information is unknown may be identified. A question may be generated to determine the second portion of information based upon the second portion of information being unknown. An answer may be received to determine the second portion of information. A response to the intent relating to the query may be provided based upon the first informational item for which the first portion of information is already known and the answer for the question generated to determine the second portion of information.

Abstract: Disclosed systems and methods convert user-supplied textual content into animated videos. Textual input is received and processed to identify narrative elements such as characters, settings, and events. These elements are then transformed into visual scene components. Still images generated based on these components are subsequently animated in line with the narrative context. The system can automatically implement storytelling techniques adapted to incorporate neuroscience principles. The system also synthesizes speech for dialogues or narrations using voice synthesis technology that considers emotional markers, tone, and pace. Generated media and metadata are stored in a data storage system that maintains data integrity and enables efficient retrieval. Users can interact with an export interface to choose video resolution, format, and sharing options. A feedback system employing machine learning algorithms collects and analyzes user feedback for real-time adjustments to the generated animated video.

Abstract: An ethical AI development platform operates by: facilitating the development of a training dataset; providing access to auto machine learning tools to facilitate the development of an AI model; providing access to machine learning analysis tools to facilitate the evaluation of the AI model, wherein the machine learning analysis tools include an AI moral insight model that generates predicted moral score data associated with the AI model, wherein the AI moral insight model is trained based on at least one-hundred thousand sets of training data, wherein the training data includes reviewed media content and moral score data associated with moral indices, wherein the moral indices include: a degree of immorality/morality, a degree of betrayal/loyalty, a degree of degradation/sanctity, a degree of harm/care a degree of cheating/fairness and a degree of subversion/authority; and providing access to a version control repository for storing versions of the training dataset and the AI model.

Abstract: Methods for building and training a network of chatbots are provided. Methods may perform data analysis on a first chatbot in the network. The first chatbot may include a first domain of intents. The data analysis may identify and rank the intents in the first domain. The first domain may represent answers to a first domain of user queries. Methods may select a predetermined number of highest-ranking intents from the first domain based on the data analysis. Methods may input the selected intents into a second chatbot in the network. The second chatbot may include a second domain of intents. The second domain may represent answers to a second domain of user queries. Methods may input a hook into the second chatbot. The hook may include a trigger word and may correspond to the first domain. When the hook is triggered, the second chatbot may invoke the first chatbot.

Abstract: A technology is described for correlating application performance to external event types. An example method may include obtaining electronic content from a content server associated with an electronic content provider and extracting a potential reference to an external event from the electronic content, where the external event may impact performance of an application hosted on a computing resources. The reference may be identified as being associated with an external event type using a categorization technique that associates the reference in the electronic content to one or more external event types, and an external event log may be generated to include event data for the external event type to allow correlation of the external event type to performance of the application hosted on the computing resources.

Abstract: An insight comprising a fact about a portion of data in a dataset is detected in the dataset. A context is determined by analyzing a natural language input, the context identifying an intent and an entity referenced by the natural language input. A relevance score is computed for the insight, the relevance score comprising a measure of similarity between a vector representation of the insight and a vector representation of the context, the vector representation of the insight comprising an encoding of the insight in multidimensional numerical form, the vector representation of the context comprising an encoding of the context in multidimensional numerical form. A selected insight is presented, the selected insight selected from a set of insights including the insight, the selected insight selected based on the relevance score.

Abstract: Methods, apparatus, and processor-readable storage media for automatically processing user request data using artificial intelligence techniques are provided herein. An example computer-implemented method includes generating, using a chatbot during a communication session with a user, answers to user requests by processing the user requests using a first set of artificial intelligence techniques associated with the chatbot; generating knowledge base-related predictions associated with the user requests by processing the user requests using a second set of artificial intelligence techniques associated with a knowledge base; calculating at least one score associated with at least a portion of the answers and the user requests based on qualifying values computed in connection with at least one of generating the answers and generating the knowledge base-related predictions; and performing one or more automated actions based on the at least one calculated score.

Abstract: Embodiments of the present disclosure provide an information processing method, apparatus, electronic device, and computer readable storage medium, and relate to the field of natural language processing technologies. The method includes: obtaining attribute information currently corresponding to an information responding party based on current input of a user, and then determining the information to be responded corresponding to the current input of the user, and then generating and outputting a target response based on the attribute information currently corresponding to the information responding party and the information to be responded corresponding to the current input of the user. The embodiments of the present disclosure realize that the target response is outputted more accurately when an information responding party (for example, an intelligent chatbot) interacts with the user, so as to implement the dialogue with the user.

Abstract: A system may port a data model into a strict schema system, translate the data model into a transformation rule definition, fit the transformation rule definition to a transform action, receive strict schema data, perform the transform action on the strict schema data based on the transformation rule definition to form rough data, and execute filtering and enriching operations on the rough data to form loose schema data.

Abstract: Provided is an information processing device to activate communication between an autonomous operation body and a user. The information processing device includes an application control unit that controls operation of an application relating to communication between an autonomous operation body and a user and a reflection unit that causes the autonomous operation body to reflect an operation performed by the user in the application. In the information processing device, the application control unit controls operation of an avatar imitating the autonomous operation body, and the reflection unit causes the autonomous operation body to reflect a reward obtained by the avatar on the basis of the operation performed by the user.

Abstract: A method is provided for operating a multi-channel messaging system. The method may provide automated conversation across multiple communication channels associated with a user by: selecting a first communication channel from the multiple communication channels, wherein the first communication channel is hosted by a first communication server; receiving, via an existing user interface of the first communication channel, a first user input via as part of a conversation with a chatbot, wherein the chatbot comprises a communication data structure comprising a plurality of communication paths; selecting a communication path based on the first user input; and generating a first feedback in response to the first user input according to the selected communication path.

Abstract: Methods, systems, and computer program products for generating context-aware process-based model determinations for greenhouse gas emissions from agricultural fields are provided herein.

Abstract: Systems and methods for controlling traffic flow for software experimentation are disclosed. An example method includes receiving a first traffic from a first plurality of users to receive an existing feature or a new feature, determining, from the first traffic, a first portion of the first plurality of users to receive the existing feature (with the first portion bounded by a first lower threshold and/or a first upper threshold) and a second portion of the first plurality of users to receive a new feature (with the second portion bounded by a second lower threshold and/or a second upper threshold). The method also includes providing the existing feature to the first portion, providing the new feature to the second portion, receiving feedback from the first plurality of users, and adjusting the first portion and/or the second portion based on the feedback and based on the bounds.

Abstract: A method is provided for operating a multi-channel messaging system. The method may provide automated conversation across multiple communication channels associated with a user by: selecting a first communication channel from the multiple communication channels, wherein the first communication channel is hosted by a first communication server; receiving, via an existing user interface of the first communication channel, a first user input via as part of a conversation with a chatbot, wherein the chatbot comprises a communication data structure comprising a plurality of communication paths; selecting a communication path based on the first user input; and generating a first feedback in response to the first user input according to the selected communication path.

Abstract: Based on one metadata template determined from among a plurality of metadata templates, a screen including a scan image, a plurality of keys defined in the one determined metadata template, and value input fields each corresponding to a different one of the plurality of keys is displayed, and in a case where a user selects any one of the plurality of keys displayed in the screen, character string areas determined to match a data format of a value corresponding to the key are displayed in such a manner that the character string areas are recognizable as value candidates on the scan image.

Abstract: Real-time data from one or more sensors can be received about one or more active wellbore operations. A blended data set can be generated that includes the real-time data combined with historical data about previously completed wellbore drilling operations, and associated with performance attributes. Additionally, identification of a criteria of focus, types of parameters for optimization, and operation groupings for the blended data set can be received. Further, the criteria of focus, types of parameters for optimization and operation groupings for the data can be applied to the blended data set to determine an adjustment for an active wellbore drilling operation of interest. The adjustment for the active wellbore drilling operation of interest can be outputted.

Abstract: Telematics and external data relating to real-time driving of a population of drivers may be collected and used to calculate a driving pattern map. The driving pattern map is used to determine a driving quotient for individual drivers wherein the driving quotient is a relative score. The driving quotient may be displayed to the driver.

Abstract: Implementations of the present disclosure relate to visual programming for deep learning. A computer-implemented method comprises presenting a visual representation of an artificial neural network, the visual representation comprising graphical elements representing layers of the artificial neural network; in response to receiving a drag-and-drop operation on the graphical elements, modifying an intermediate representation of the artificial neural network, wherein the intermediate representation is independent of a deep learning framework and the drag-and-drop operation is configured to modify connections between the graphical elements; and modifying, based on the intermediate representation of the artificial neural network, code of the artificial neural network for a target deep learning framework.

Abstract: Embodiments in the present disclosure relate to computer network architectures and methods for predictive analysis using lookup tables as prediction models. The predictive analysis, including the generation of the lookup tables, performed by a predictive system of the present disclosure is driven entirely by a query language, such as Structured Query Language, in various embodiments. The predictive analysis, including the generation of the lookup tables, is performed without machine learning or generative artificial intelligence, in various embodiments.

Abstract: Provided are a method and an apparatus for testing AI chip computing performance, and a non-transitory computer-readable storage medium. The method includes: forming computing performance result data of a to-be-tested AI chip according to a plurality of items of simulation data formed in a development process of the to-be-tested AI chip; acquiring a function instruction set matched with a to-be-tested service function, wherein the function instruction set is composed of a plurality of instructions in a standard instruction set matched with the to-be-tested AI chip; and predicting computing time required by the to-be-tested AI chip to execute the to-be-tested service function according to the function instruction set and the computing performance result data.

Abstract: An apparatus and method for entity data aggregation and analysis is disclosed. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive entity data from a data collection module, wherein the data collection module includes at least a sensor, determine an obstacle datum as a function of the entity data, classify the entity data into one or more obstacle groups as a function of the obstacle datum of the entity data, determine at least an obstacle resolution object as a function of an obstacle resolution template and generate the obstacle resolution data structure as a function of a template form field of an entity specific data structure template, the at least an obstacle resolution object and the one or more obstacle groups.

Abstract: Embodiments are directed towards real time display of event records and extracted values based on at least one extraction rule, such as a regular expression. A user interface may be employed to enable a user to have an extraction rule automatically generate and/or to manually enter an extraction rule. The user may be enabled to manually edit a previously provided extraction rule, which may result in real time display of updated extracted values. The extraction rule may be utilized to extract values from each of a plurality of records, including event records of unstructured machine data. Statistics may be determined for each unique extracted value, and may be displayed to the user in real time. The user interface may also enable the user to select at least one unique extracted value to display those event records that include an extracted value that matches the selected value.

Abstract: Systems and methods of the present disclosure may receive, from a user computing device, a user-provided data record query including a natural language request for information associated with one or more data sources. User persona attributes of the user may be determined, such as a user role or security parameters or both. Based on the user persona attributes a context query may be generated to obtain context attributes associated with the user-provided query. The natural language request and the context attributes are input into the model orchestration large language model (LLM) to output instructions to machine learning (ML) agents based on the context attributes. The ML agents output responses associated with the user-provided data record query based on the instructions, and the responses are input into the model orchestration LLM to output to the user computing device a natural language response based on the context attributes.

Abstract: A system for use in a connected kitchen allows a user to find and use traditional recipes as would be found in a recipe book and to have them automatically customized to their language, locality, dietary preferences and the appliances in their kitchen.

Abstract: Some systems, devices and methods detailed herein provide a system for use in determining metrics of a subject. The system can provide, as an output, a function-metric value determined based on a defined relationship between physiological measures and a chronological age.

Abstract: Systems and methods for presenting configurable machine learning systems through graphical user interfaces are disclosed. In an embodiment, a machine learning server computer stores one or more machine learning configuration files. A particular machine learning configuration file of the one or more machine learning configuration files comprises instructions for configuring a machine learning system of a particular machine learning type with one or more first machine learning parameters. The machine learning server computer displays through a graphical user interface, a plurality of selectable parameter options, each of which defining a value for a machine learning parameter. The machine learning server computer receives a particular input dataset.

Abstract: In a method for AI model optimization, an optimization device receives an original AI model and search configuration information that comprises a plurality of search items each indicating its search categories for performing optimization information search on the original AI model. The device obtains a plurality of search operators corresponding to the plurality of search items, and arranges the search operators in an operation sequence based on the search configuration information. The device then executes the search operators in the arranged operation sequence on the original AI model to obtain an optimized AI model. In the execution of the operation sequence, each search operator, except for the first search operator in the operation sequence, is executed utilizing operation results of a preceding search operator in the operation sequence, the operation results including generated network structures and search space information.

Abstract: A cross domain generalization system for industrial artificial intelligence (AI) applications is disclosed. A target encoder subsystem obtains target data from a target machine product and generates lower dimensional data for obtained target data using a target artificial intelligence (AI) model. The generated lower dimensional data are corresponding to a plurality of target embeddings data. The target encoder subsystem further applies the plurality of target embeddings data into a source classifier AI model. A source classifier subsystem predicts a quality of the target machine product by generating class labels for each of the plurality of target embeddings data based on a result of the classifier AI model. The goal of the present invention is to learn features or representations such that the correlation with a label space is similar both in source and target domains while being invariant of data distributions.

Abstract: An information processing apparatus includes a processor configured to acquire biological information of plural users determined to be within a predetermined area and operate an apparatus based on the biological information of the plural users.

Abstract: Systems and methods can be provided for predicting responses during communication sessions with network devices. In some implementations, systems and methods can facilitate predicting responses using machine learning techniques. Messages received through a platform can be stored in a repository. A machine learning model may be trained using the stored messages. When a terminal device is communicating with a network device in a communication session, the messages exchanged in the communication session and the machine learning model can be used to predict future responses in real-time. The predicted future responses can be presented at the terminal device. A predicted response can be selected at the terminal device. Upon selection, the selected predicted response is transmitted to the network device during the communication session.

Abstract: A non-transitory machine-readable medium can comprise executable instructions that, when executed by a processor, facilitate performance of operations, comprising: determining a workflow comprising a group of files of a distributed file system, determining a type of the workflow, determining a group of historical metrics associated with the type of the workflow, using a model generated based on machine learning applied to the group of historical metrics, determining respective predicted ranks for different settings that are able to be applied to the workflow, based on the respective predicted ranks, determining a setting, of the different settings, to use to apply to the workflow and that has a predicted rank of the respective predicted ranks that satisfies a defined criterion, and applying the setting to at least one of the group of files to decrease a latency associated with processing the workflow.

Abstract: The present disclosure relates to a feature contribution system that accurately and efficiently provides the influence of features utilized in machine-learning models with respect to observed model results. In particular, the feature contribution system can utilize an observed model result, initial contribution values, and historical feature values to determine a contribution value correction factor. Further, the feature contribution system can apply the correction factor to the initial contribution values to determine correction-factor adjusted contribution values of each feature of the model with respect to the observed model result.

Abstract: Systems and methods are provided for enhanced pose generation based on generative modeling. An example method includes accessing an autoencoder trained based on poses of real-world persons, each pose being defined based on location information associated with joints, with the autoencoder being trained to map an input pose to a feature encoding associated with a latent feature space. Information identifying, at least, a first pose and a second pose associated with a character configured for inclusion in an in-game world is obtained via user input, with each of the poses being defined based on location information associated with the joints and with the joints being included on a skeleton associated with the character. Feature encodings associated with the first pose and the second pose are generated based on the autoencoder. Output poses are generated based on transition information associated with the first pose and the second pose.

Abstract: A method is provided for operating a multi-channel messaging system. The method may provide automated conversation across multiple communication channels associated with a user by: selecting a first communication channel from the multiple communication channels, wherein the first communication channel is hosted by a first communication server; receiving, via an existing user interface of the first communication channel, a first user input via as part of a conversation with a chatbot, wherein the chatbot comprises a communication data structure comprising a plurality of communication paths; selecting a communication path based on the first user input; and generating a first feedback in response to the first user input according to the selected communication path.

Abstract: More natural communication and interaction are enabled. The autonomous system (100) includes an action decision unit (140) that decides, based on an attention level map (40) in which an attention level indicating the degree of attention for each position in a predetermined space is set, the action which a drive mechanism is caused to perform.

Abstract: Systems and methods for similarity search are described. Embodiments identify a document and a query corresponding to a matching phrase in the document, encode the query and a candidate phrase, score the candidate phrase using at least one learning-based score and at least one surface form score, wherein the at least one learning based score is based on the encoding, and the at least one surface form score is based on a surface form of the query and a surface form of the candidate phrase, and select the matching phrase based on the scoring.

Abstract: A dialog system and intelligent personal assistant capable of semantic-understanding mapping between user intents and machine services includes an interface to receive a voice or text request from a user. Semantics of the request including at least one of entities, intent, or context are extracted from the user's request. A sequence of action features is selected based on the extracted semantics. A sequence of application programming interfaces (APIs) corresponding to the sequence of action features is then executed to generate a result. An action sequence optimizer may optimize the sequence of action features based on user configuration. The examples provide a technical solution to model a richer semantic-understanding mapping between user intents and available APIs, that will be greatly improving user experience in the spoken dialogue system, as core of personal assistant.

Abstract: Systems, computer program products, and methods are described herein for mitigating data loss in an edge computing environment using machine learning and distributed ledger techniques. The present invention is configured to receive an indication that one or more edge computing nodes is processing one or more portions of a transaction; retrieve dark data associated with each of the one or more edge computing nodes; initiate a machine learning algorithm on the dark data retrieved from each of the one or more edge computing nodes; capture, using the machine learning algorithm, representation information for each of the one or more edge computing nodes from their respective dark data; generate a ledger record for the representation information for each of the one or more edge computing nodes; and register the ledger record for the representation information for each of the one or more edge computing nodes on a first distributed ledger.

Abstract: An online system generates a set of genomic representations, each including multiple genes, in which each gene represents users assigned to a control or test group for performing a test. A metric is identified based on a treatment associated with the test group and a score for each representation is computed based on a difference between two values, in which each value is based on the metric associated with users assigned to the test or control group. A propagation process is executed by identifying representations having at least a threshold score, propagating genes included in the representations to an additional set of representations through recombination and/or mutation, and computing the score for each additional representation. The propagation process is repeated for each additional set of representations until stopping criteria are met and a representation is selected based on scores associated with one or more representations.