Abstract: A set of inputs from a plurality of inputs are selected based on a user identifier of a user. The set of inputs are apportioned and recombined to produce one or more grouped inputs. The one or more grouped inputs are input to one or more machine learning models to provide individualized information output by the one or more machine learning models to the user.

Abstract: A computerized method of estimating resource requirements in an environment is presented. The method comprises a preparation phase and a simulation phase, wherein the preparation phase comprises a machine learning training phase and a clustering phase. The machine learning training phase trains a machine learning model to predict a resource requirement. Thereby, a subset of features is extracted. The clustering phase determines clusters in the subset of features, a correlation coefficient and least one identifying parameter of a distribution of the feature values of the subset of features. Finally, the simulation phase determines a distribution for a feature, selects at least one value for a feature and uses the second machine learning model to estimate a resource requirement in the environment in at least one time period the future.

Abstract: AI-based orchestration. In an embodiment, a recommendation engine is applied to a data pipeline representing company accounts. Engagement metric(s) are calculated based on activity data associated with the company accounts, and predictive model(s) are applied to the activity data and/or firmographic data associated with the company accounts to generate predictive output. A tactic recommendation model is applied to orchestration features, comprising the engagement metric(s) and predictive output, to generate recommended tactic(s). In addition, a contact recommendation model is applied to contact data to generate recommended contact(s). The recommended tactic(s) are combined with the recommend contact(s) to generate an orchestration, comprising recommended action(s), to be executed.

Abstract: Provided is a method and an apparatus for processing a logistics order task, an electronic device, and a computer medium. The method for processing the logistics order task includes: detecting a quantity of an item on a shelf, the item being associated with a positioning task having been received; determining a time for receiving the positioning task; determining, based on the quantity of the item and/or the time for receiving the positioning task, whether the shelf meets a scheduling condition; and transporting the shelf that meets the scheduling condition to a sorting workstation directed by the positioning task.

Abstract: Aspects of the disclosure relate to intelligently aggregating data using a convolutional network quantum processor. Using an artificial intelligence model, a computing platform may identify dependencies derived from a consumer request and corresponding values. For each configuration of dependency and corresponding value, the computing platform may determine a likelihood of success. The computing platform may alter the configurations and may determine a likelihood of success for each altered configuration. The computing platform may train a convolutional network to analyze the consumer request using the configurations, the altered configurations, and/or corresponding likelihoods of success. The convolutional network may generate a function that describes the dependencies derived from the consumer request. Using a quantum processing model, the computing platform may analyze different states of the function in parallel and may determine whether to approve or deny the consumer request based on the analysis.

Abstract: A system and method are disclosed for immersive guidance using a supply chain execution platform. The method comprises monitoring and receiving input comprising user interaction and user input signals, detecting a user intent within a supply chain execution environment from the user input signals, determining associated focus prompts and action prompts, displaying immersive guidance to the user comprising a visual prompt using augmented reality and updating the immersive guidance based on real-time user action updates received from one or more sensing devices. The method further comprises where the user intent further comprises a series of prompts for the user that guide the user through completion of one or more tasks and where the focus prompts are determined based on eye cornea reflection tracking and facial expressions.

Abstract: A scheduling method using a knowledge graph (KG) performs following steps by a processor: obtaining an initial scheduling solution of the production system; converting the initial scheduling solution into triples to form the KG, each triples includes two entities and a relationship of the production system, the two entities indicate two production resources; embedding triples into a vector space to generate embedded vectors by a KG embedding technique; generating embedded vector combinations according to the embedded vectors and computing a distance of each of embedded vector combinations; and performing a scheduling algorithm to generate a target scheduling solution according to the embedded vector combinations, and providing reference information when the scheduling algorithm generates a schedule of a station, wherein the reference information comprises at least one of the embedded vector combinations associated with the station, and the distance corresponding to said at least one embedded vector combinatio

Abstract: Application, in a single software instrument, of AI and advanced statistics methods for monitoring and analyzing quality problems over the whole lifecycle of a product is provided. A method based on web scraping techniques and AI for monitoring the product out of the warranty period is also provided.

Abstract: One example method is performed at a far edge device and includes collecting data with one or more IoT (Internet of Things) devices, feeding the data to a feedback loop that includes multiple stages, running the feedback loop, providing learning information, comprising output from one or more of the stages of the feedback loop, to a central manager by way of a learn interface, accessing learning information generated by one or more other far edge devices, and updating the feedback loop using the learning information generated by the one or more other far edge devices.

Abstract: Examples of the present disclosure describe systems and methods for monitoring services provided by a service provider to a consumer using a smart contract architecture. In one example, smart contract terms are provided to a DeFi application running on a blockchain, and a smart contract (e.g., a SLA smart contract) is constructed on the blockchain. Once the service provider begins providing service to the consumer, the system may monitor the service performance of the service. If the service performance dips below a certain agreed-upon threshold based on the smart contract, a trigger event may occur. Additionally, the analysis of the service performance data may indicate a service deficiency, wherein a remedial action is suggested to the service provider to be applied. The service deficiency and remedial action may be stored on the blockchain for future review.

Abstract: Provided is an operation management system configured to manage delivery of a raw material from a raw material production base where the raw material including a hydride is produced to a plurality of dehydrogenation bases where the raw material is subjected to a dehydrogenation reaction to obtain a hydrogen-containing gas. The operation management system includes: an information acquisition unit configured to acquire first information on a dehydrogenation status in the plurality of dehydrogenation bases; and a delivery plan creation unit configured to create a delivery plan for delivering the raw material to the plurality of dehydrogenation bases on the basis of the first information.

Abstract: Systems and methods for generating and providing an executable notification to a user based on their interactions with a system. The method involves prompting the user to input values for various preferences, and continuously receiving messages from multiple information sources related to predefined physical and virtual entities, which are stored as context data. An allocation proposition is created by assigning values to the entities, and compared to a comparative composition of entities. The executable notification, which includes the allocation proposition, is provided based on the comparison. User preferences, including regularization types and levels, are incorporated to filter and process messages. Sources are uncertainty and impact, which is used to suppress messages from sources with high uncertainty or significance. The user is prompted to change information sources and obtain a sentiment score, which is used to adjust the system's belief in information sources.

Abstract: A work distribution platform is disclosed that is coupled with client devices associated with workers. The work distribution may include matching systems to match workers with orders. An example matching system is a worker offering system, with which the work distribution platform provides orders to workers so that the workers may submit offers to perform the order. The work distribution platform may include a plurality of matching systems, each of which may perform a different process for matching orders with workers and each of which may be used by the work distribution platform.

Abstract: A system may be configured to perform a method for generating customized training. The system may receive first user interaction data associated with a user. The system may determine, using a machine learning model (MLM), whether the first user interaction data exceeds a predetermined threshold. Based on such determination, the system may assign a training module to the user. The system may access a user profile associated with the user, the user profile comprising a plurality of training modules. The system may generate a training plan based on the training module and the plurality of training modules. The system may receive second user interaction data associated with the user, and may determine an efficacy level of the training plan based on the second user interaction data. The system may dynamically update the training plan based on the efficacy level, and may dynamically display the training plan in the user profile.

Abstract: There is provided a system for intelligent industrial safety monitoring. The system includes a global monitoring unit configured to generate first monitoring information by globally monitoring a work area of an industrial site, a local monitoring unit attached to a work object at work within the work area and configured to generate second monitoring information by monitoring a surrounding area of the work object, a communication unit configured to transmit and receive data to and from the global monitoring unit and the local monitoring unit, and a first controller configured to generate information on a location of the work object based on the first monitoring information and track a moving path of the work object and at least one second controller configured to predict an at-risk object that has approached within a predetermined distance of the work object based on the second monitoring information.

Abstract: A method for automated surveying of hotel guests uses a television device in the hotel room. The method is executed by an interface measurement circuit for detecting the connection of an external playback device and a video signal switch for switching the signal source to an external television device, a) wherein when the television device is switched on by an external remote control, an external minicomputer sends video data to the video input of the television device via the interface measurement circuit and the video signal switch instead of the data received during normal operation. These video data include a survey software, b) wherein the hotel guest sends the survey answers to the minicomputer via the remote control, c) wherein when the survey is completed, the television device is switched back to normal operation, and d) wherein the survey answers are sent to a hotel server by the minicomputer.

Abstract: One embodiment of the invention provides a method for generating and ranking potential barriers that prevent completion of a structured process. The method comprises receiving a textual description of the structured process, receiving one or more templates relating to the structured process, and identifying a set of user actions required to complete the structured process based on the textual description. The method further comprises expanding the set of user actions based on general knowledge to include one or more additional actions, and updating the one or more templates based on the expanded set of user actions. The method further comprises generating, using at least one generative language model, a set of potential barriers that prevent completion of the structured process based on the one or more updated templates.

Abstract: Systems and method described herein include a member assessment and improvement system configured to receive data relating to personal activity of one or more members of an organization; to assess performance of the personal activity of the one or more members based at least in part on the data relating to the personal activity; to generate game-related data based at least in part on the performance of the personal activity of the one or more members; and to transmit one or more command signals to one or more game applications being executed by one or more electronic devices associated with the one or more members to display the game-related data via the one or more game applications.

Abstract: A provider computing system includes one or more processing circuits including one or more processors coupled to one or more memory devices having instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to determine a user track for a user based on one or more of a user track selection received from a user device associated with the user or user information of the user. The instructions further cause the one or more processors to identify one or more features to provide to the user based on the user track. The instructions further cause the one or more processors to generate a user interface corresponding to the user track, the user interface including the one or more features. The instructions further cause the one or more processors to cause the user interface to be displayed to the user.

Abstract: A value chain system that provides recommendations for designing a logistics system generally includes a machine learning system that trains machine-learned models that output logistics design recommendations based on training data sets that each respectively defines one or more features of a respective logistic system and an outcome relating to the respective logistics system; an artificial intelligence system that receives a request for a logistics system design recommendation and determines the logistics system design recommendation based on one or more of the machine-learned models and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation, and one or more physical asset digital twins of physical assets. The digital twin system executes a simulation based on the logistics environment digital twin, the one or more physical asset digital twins.