Abstract: Systems and methods for dynamic mapping and routing using machine learning. The system can access requested items, a map, and process data signals to generate route segments that indicate a path to the requested items. The method includes accessing data associated with a delivery request that includes requested items. The method includes accessing map data associated with the initial location of the items. The method includes processing data signals from computing devices to confirm an initial location of an item, determine a new location of an item, or determine an availability of an item. The method includes iteratively generating a plurality of route segments for a user to follow that indicates a path to the requested items. The method includes outputting a command instruction to generate a user interface that iteratively displays each route segment based on the user selecting or disregarding the respective items.

Abstract: Various embodiments of the teachings herein include an information processing method. An example method includes: generating a workflow in an operations technology (OT) domain based on an instruction entered by a user, wherein the workflow defines an operation to be performed by a work cell in the OT domain, and identifications (IDs) of elements in the workflow are named using a universal namespace rule; mapping the IDs of the elements in the workflow to runtime of a master controller of the work cell; and determining a target function to be executed in the workflow based on the IDs of the elements that are mapped to the runtime and a target body to execute the target function.

Abstract: A control circuit obtains a first set of rules that define a conveyability range for containers as a function of conveyability constraints pertaining to an intermediary distribution facility. The control circuit also accesses conveyability constraint information for the intermediary distribution facility and generates a conveyability range for that facility by evaluating the constraint information against the first set of rules. The control circuit obtains a second set of rules that define an objective function that uses the conveyability range as a constraint to determine an optimal container pack size for the given item. (By one approach these teachings will also accommodate using at least two additional constraints, such as a so-called days-of-supply (DOS) constraint and a so-called pack-and-a-half constraint.) The control circuit then generates an optimal container pack size for the given item by evaluating the objective function against the conveyability range (and other selected constraints).

Abstract: Disclosed is a system and process that include inputs and verification of various portions of a logistical system from an outset to an ending. In various embodiments, the logistical system may include a profile for any one of a plurality of entities to allow for inclusion or selection in one or more steps of a logistic process. The logistical system may further allow for selection of a driver, provisional of selected qualifications, and determination and/or confirmation of an action, such as delivery.

Abstract: A system and method are disclosed for visualizing and modifying a model of a supply chain network. The method includes performing a polytope analysis using goals and levers, generating a network modeler GUI of a modeled supply chain network associated with the polytope analysis, receiving a selection to edit one or more node icons of the modeled supply chain network displayed on the network modeler GUI, receiving a selection to add one or more transportation lanes to the modeled supply chain network, receiving a selection to add one or more levers to the modeled supply chain network, receiving a selection to modify the modeled supply chain network, updating the polytope analysis based on at least one of the received selections, and generating and displaying a response plan based on the updated polytope analysis.

Abstract: A power supply/demand adjusting method disclosed here is a method for adjusting power supply and demand between a power transmission/distribution system and a storage battery mounted on an electric vehicle. The method includes the steps of: acquiring power selling approval of a storage battery; acquiring battery information of a storage battery for which the power selling approval is acquired based on the acquired power selling approval and power demand information from the power transmission/distribution system; calculating a power selling amount to be transmitted from the storage battery to the power transmission/distribution system, based on the acquired battery information of the storage battery and the acquired power demand information; supplying electric vehicle based on the power selling amount from the storage battery to the power transmission/distribution system; and calculating an incentive to be provided to a user of the electric vehicle based on the power selling amount.

Abstract: Systems, methods, and computer-readable storage media for issue spotting in legal cases, and more specifically to training and using an Artificial Intelligence (AI) model to identify possible causes of action in a legal dispute. A system can receive a plurality of cause of action models, and tune an existing AI model using the plurality of action models, resulting in a tuned AI model. The system can then convert the tuned AI model into an AI algorithm, where inputs to the AI algorithm include a jurisdiction and at least one piece of dispute information associated with the dispute, and outputs of the AI algorithm include a cause of action for the dispute and elements of the cause of action.

Abstract: A computerized travel itinerary recommendation tool includes a traveler monitor, a contextual information parser, and a recommendation engine. The traveler monitor obtains traveler preference information associated with a traveler. The traveler preference information indicates one or more travel criteria for a trip. Travel criteria comprises one or more parameters of travel products available for the trip. The contextual information parser infers one or more travel criteria from contextual information obtained for the trip. The contextual information is information that does not include any travel criterion. The recommendation engine determines a recommended travel itinerary for the trip to the traveler based on the travel preference information and the contextual information. The recommendation engine further causes a display of the recommended travel itinerary to the traveler.

Abstract: A system and method for improving payload delivery utilizing environmental factors is disclosed. The method includes receiving a request for delivery of a payload, the request including a destination location; determining an optimal height for a release of the payload at the destination location, wherein the payload is delivered by a UAV configured to hover at the destination location; generating a navigation plan including a flight path having an origin point and the destination location; and configuring the UAV to execute the navigation plan and release the payload at the destination location from the determined optimal height.

Abstract: Provided are a method and an apparatus for predicting cancellations of reservation services based on fusion of multi-scale time series features, relating to the field of predicting cancellations of reservation services. The method includes: obtaining reservation feature data of a reservation service order to be predicted, where the reservation feature data includes service provider feature information, user reservation feature information, statistical time series data, and statistical non-time series data; performing multi-scale time series feature fusion on various time series in the statistical time series data, and inputting fused data, together with other reservation feature data, into a multi-task learning prediction network for cancellation prediction. The multi-task learning prediction network includes a main cancellation prediction model and an auxiliary rejection prediction model. This application can accurately predict whether a reservation service order to be predicted is canceled during service.

Abstract: The present application describes a payload management system that routes a payload item associated with an individual from an origin to a destination. In some examples, the payload item is routed from the origin to the destination separate from a travel itinerary associated with the individual. For example, the payload item can be routed via a vehicle other than a VTOL aircraft that is assigned to the individual for a multi-modal transportation service.

Abstract: A federated learning framework including household agents configured to continuously learn model parameters for managing charging periods and discharging periods of household batteries, and microgrid agents to maximize use of local energy based on a pricing policy, including accessing power from other microgrids when there is insufficient local energy to cover local demand. and selling surplus energy to the other microgrids when power generation by the microgrid surpasses the local demand. Each household machine learning agent is configured to control household energy demand from and supply to a microgrid which they are connected in order to minimize household energy cost while adapting to changes in the energy price that is determined based on the pricing policy of the microgrid agent that encourages reduction of carbon emission. A federated learning engine combines the model parameters from the household machine learning agents to update a global household machine learning agent.

Abstract: A data input device including a keypad. The data input device includes at least one proximity sensor positioned within a housing formed in an element of the device forming a projection in the vicinity of the keypad, the proximity sensor being oriented toward the keypad, the proximity sensor delivering measurements; and a program for analysing the measurements, delivering information representative of the presumed presence of a spy device on the keypad when the measurements are higher than a predetermined threshold for a predetermined time period.

Abstract: Artificial intelligence (AI) based systems and methods are described for analyzing order data to generate a driver logistics prediction value. Processors of a logistics server receive order data from order data sources corresponding to an order. A dispatch learning model then analyzes the order data to generate a driver logistics prediction value corresponding to the order data. The dispatch learning model is trained or provided with logistics data regarding order data of respective orders, and the dispatch learning model is configured to output one or more driver logistics prediction values corresponding to the order data of the respective orders. The processors generate a driver determination that identifies a driver to fulfill the order based on the driver logistics prediction value, and push the driver determination to a dispatch application. Responsive to receiving the driver determination, the dispatch app displays a graphical user interface (GUI) depicting the driver determination.

Abstract: Systems and methods perform operations that include receiving information regarding an item; obtaining one or more digital images of the item; generating an image dataset, which may be unique, for the item based on the one or more digital images; storing information linking the image data set to the information regarding the item; receiving, from an item disposition system, a request to identify the item, wherein the request includes the image dataset for the item; identifying the item based on receiving the request and the image dataset; and providing, to the item disposition system, the information regarding the item to cause the item disposition system to disposition the item based on the information regarding the item.

Abstract: Techniques for transportation speed prediction and transit time estimation are provided. In an example, a computer system generates a first input to a first machine learning model based on temporal information, road segment information, and attribute information for road segments in a geographic region. The computer system receives a first output of the first machine learning model. The first output indicates a predicted speed value associated with a road segment for a transportation resource. The computer system generates a second input to a second machine learning model based on the first input, the predicted speed value, and properties associated with a transportation load. The computer system receives a second output of the second machine learning model. The second output indicates a distribution of a time length for transporting the transportation load using the transportation resource. The second output is usable in deploying the transportation resource for transporting the transportation load.

Abstract: Systems and methods are provided for facilitating the discovery and presentations of skills and data processed by the skills within blocks of a canvas displayed to a user within a user interface. The systems also identify schemas and data output formats of the relevant skills, and a desired presentation schema based on the user contexts and the application contexts. The systems further identify modules for normalizing the schemas of the relevant skills to the desired presentation schema, and uses the identified modules to normalize data obtained from the relevant skills into the desired presentation schema for presentation within the blocks of the canvas.

Abstract: A computing system is configured to: (i) create a data science model that is configured to (a) receive a value for a metric that provides insight regarding a party's usage of a software tool of a construction management software application on a construction project and (b) based on an evaluation of the received value for the metric, output a prediction of the party's performance on the construction project and, (ii) after creating the data science model, utilize the data science model to produce a prediction of a given party's performance on a given construction project by inputting a given value for the metric into the data science model and thereby causing the data science model to (a) evaluate the given value of the metric, and (b) based on the evaluation, output the prediction of performance on the given construction project.

Abstract: Some embodiments described in this disclosure are directed to a system for determining a deficit or surplus in green energy for use in an industrial process. In some embodiments, the system is configured to predict future available green energy using weather forecasts. In some embodiments, the system is configured to model power demand for an industrial process and determine whether a green energy source such as solar and/or wind will be able to meet the power demand. In some embodiments, the system is configured to suggest alternate energy sources for purchase to make up for a green energy deficit. In some embodiments, the system is configured to suggest a best time to buy or sell energy to optimize a profit or loss.

Abstract: A computer-implemented method, a computer system and a computer program product customize delivery of transactional documents. The method includes receiving a transactional document associated with a user. The method also includes obtaining a preferred document format for the user and a preferred delivery method for the user from a database. The method further includes identifying a current display format of the transactional document. In addition, the method includes modifying the current display format of the transactional document, wherein a modified transactional document has the preferred document format for the user. Lastly, the method includes transmitting the modified transactional document to the user using the preferred delivery method for the user.