Abstract: A method and system for calculating route guidance for a personal transport device based on prosocial costs is described. In one embodiment, the method includes calculating, by a processor associated with the personal transport device, a plurality of routes from a first location to a second location and determining, by the processor, one or more prosocial factors associated with each route of the plurality of routes. The method also includes calculating, by the processor, a prosocial cost associated with each route of the plurality of routes. Based on the prosocial costs, the method further includes providing, by the processor, route guidance to a user of the personal transport device from the first location to the second location along a selected route from the plurality of routes and displaying the selected route on a display associated with the personal transport device.

Abstract: A method and system for implementing real-time feedback to a user of a personal transport device, such as an electric scooter, based on prosocial behavior is provided. In one embodiment, the method includes detecting at least one object located on a path on which the personal transport device is traveling and calculating a prosocial yielding behavior parameter associated with the at least one object and the personal transport device. The method also includes comparing the calculated prosocial yielding behavior parameter to a threshold value and, based on the comparison of the calculated prosocial yielding behavior parameter to the threshold value, providing real-time feedback to the user of the personal transport device.

Abstract: A method and system for modifying range of a battery of a personal transport device based on prosocial behavior is described. In one embodiment, the method includes detecting at least one object located on a path on which the personal transport device is traveling and calculating a prosocial yielding behavior parameter associated with the at least one object and the personal transport device. The method also includes comparing the calculated prosocial yielding behavior parameter to a threshold value. Based on the comparison of the calculated prosocial yielding behavior parameter to the threshold value, access is provided to at least a part of a power reserve portion of a battery of the personal transport device.

Abstract: An automated vehicle (AV) is configured to perform automated travel, and includes vehicle sensors configured to detect a second vehicle in a surrounding environment of the AV. The AV includes at least one of a brake mechanism, an accelerator mechanism, a steering control, and a user interface configured to generate a user response to automated travel by the AV. The AV includes a computing device configured to identify an interaction between the AV and the second vehicle while executing an automated travel path, and receive user responses to automated travel by the AV. The computing device is configured to determine at least one aspect of wellbeing, trust, and satisfaction of the user riding the AV based on the user responses, and determine a learned optimal policy which increases the at least one aspect of wellbeing, trust, and satisfaction based on the user responses.

Abstract: Aspects of the disclosure relate to dynamic model configuration and execution. A computing platform may receive first model data comprising first model execution configuration data and first model output configuration data. The computing platform may generate a first model based on the first model execution configuration data. The computing platform may distribute, to a plurality of computing platforms, the first model, the first model execution configuration data, and the first model output configuration data. The computing platform may receive a second request to execute one or more models from a third computing platform. The computing platform may receive, from the third computing platform, first model execution data. The computing platform may execute the first model based on the first model execution data and the first model execution configuration data to generate a first model output score.

Abstract: Aspects of the disclosure relate to dynamic model configuration and execution. A computing platform may receive first model data comprising first model execution configuration data and first model output configuration data. The computing platform may generate a first model based on the first model execution configuration data. The computing platform may distribute, to a plurality of computing platforms, the first model, the first model execution configuration data, and the first model output configuration data. The computing platform may receive a second request to execute one or more models from a third computing platform. The computing platform may receive, from the third computing platform, first model execution data. The computing platform may execute the first model based on the first model execution data and the first model execution configuration data to generate a first model output score.

Abstract: Systems and methods are disclosed for determining a visual indicator for a package based on a delivery address for the package and a location of the delivery address along a delivery route. An optimized delivery route may be created for packages to be delivered at a given time. At the fulfillment center, a delivery system may label a package prior to loading the package on a delivery vehicle. The delivery system may determine the appropriate visual indicator (e.g., color and/or pattern) or the visual indicator may be randomly selected. The delivery system may generate a label having the visual indicator. The package may be deposited into a certain bin with other packages having a delivery address in close proximity. The delivery system may generate a user interface on a user device that indicates that the package has a certain visual indicator to facilitate sorting and identification of the package.

Abstract: According to one aspect, a system for trust calibration may include a processor and a memory. The memory may store one or more instructions. The processor may execute one or more of the instructions stored on the memory to perform one or more acts, actions, or steps, such as receiving a record of one or more interactions between a user and a first autonomous device, building a trust profile for the user based on one or more of the interactions between the user and the first autonomous device, and operating a target autonomous device based on the trust profile.

Abstract: Systems and methods for determining trust across mobility platforms are provided. In one embodiment, a method includes receiving first mobility data for a first automation experience of a user with a first mobility platform. The method also includes receiving a swap indication for a second automation experience of the user with a second mobility platform after the first automation experience. The method further includes selectively assigning the first mobility platform to a first mobility category and the second mobility platform to a second mobility category different than the first mobility category. The method yet further includes calculating an estimated trust score for the second automation experience by applying a trust model based on the first mobility category, the second mobility category, and a sequence of the first automation experience and the second automation experience. The method includes modifying operation of the second mobility platform based on the estimated trust score.

Abstract: Aspects of the disclosure relate to dynamic model configuration and execution. A computing platform may receive first model data comprising first model execution configuration data and first model output configuration data. The computing platform may generate a first model based on the first model execution configuration data. The computing platform may distribute, to a plurality of computing platforms, the first model, the first model execution configuration data, and the first model output configuration data. The computing platform may receive a second request to execute one or more models from a third computing platform. The computing platform may receive, from the third computing platform, first model execution data. The computing platform may execute the first model based on the first model execution data and the first model execution configuration data to generate a first model output score.

Abstract: Techniques for generating a workflow model are described. In an example, a device presents a window. A selection of an icon presented in the window is received, where the icon corresponds to a node having a node type. Based on rule, the device determines a location where the icon can be added to a workflow. The rule indicates the location based on the node type and a workflow model. The user device presents, in the window, an indicator of the location, receives a placement of the node at the location, and presents, the icon at the location indicating that the node is added to the workflow. The device updates the workflow model to include, based on the placement, a reference to executable code that corresponds to the node, and to include, based on the location, information about an execution sequence of executable codes referenced in the workflow model.

Abstract: A method for risk scoring a blockchain transaction includes: storing blockchain data associated with a blockchain, the data including a plurality of blocks, each block including a block header and transaction values, each transaction value including a sending address, recipient address, and transaction amount; receiving a new transaction value related to a proposed blockchain transaction and a node identifier associated with a node included in a blockchain network associated with the blockchain, the new transaction value including a user address, transacting address, and blockchain amount; identifying transaction values in the blockchain where the sending address or recipient address is associated with a transacting cryptographic key pair also associated with the transacting address; determining a risk score for the proposed blockchain transaction based on at least the data included in each of the identified transaction values and the new transaction value; and transmitting the determined risk score.

Abstract: Aspects of the disclosure relate to dynamic model configuration and execution. A computing platform may receive first model data comprising first model execution configuration data and first model output configuration data. The computing platform may generate a first model based on the first model execution configuration data. The computing platform may distribute, to a plurality of computing platforms, the first model, the first model execution configuration data, and the first model output configuration data. The computing platform may receive a second request to execute one or more models from a third computing platform. The computing platform may receive, from the third computing platform, first model execution data. The computing platform may execute the first model based on the first model execution data and the first model execution configuration data to generate a first model output score.

Abstract: Methods, systems and computer program products for selectively providing authenticated data corresponding to a payment card holder at a point-of-sale (POS) terminal are described. The method can include receiving from a terminal device, payment card account information comprising a payment card account identifier and user authentication information. Responsive to the received user authentication information matching one of a plurality of predefined authentication keys associated with the payment card account identifier, a predefined function is selected from among at least first and second predefined functions associated with the payment card account identifier—wherein the selection of the predefined function from among the first and second predefined functions is based on an association between the selected predefined function and an authentication key that has been matched with the received user authentication information.

Abstract: Aspects of the disclosure relate to dynamic model configuration and execution. A computing platform may receive first model data comprising first model execution configuration data and first model output configuration data. The computing platform may generate a first model based on the first model execution configuration data. The computing platform may distribute, to a plurality of computing platforms, the first model, the first model execution configuration data, and the first model output configuration data. The computing platform may receive a second request to execute one or more models from a third computing platform. The computing platform may receive, from the third computing platform, first model execution data. The computing platform may execute the first model based on the first model execution data and the first model execution configuration data to generate a first model output score.

Abstract: Techniques for generating a workflow model are described. In an example, a device presents a window. A selection of an icon presented in the window is received, where the icon corresponds to a node having a node type. Based on rule, the device determines a location where the icon can be added to a workflow. The rule indicates the location based on the node type and a workflow model. The user device presents, in the window, an indicator of the location, receives a placement of the node at the location, and presents, the icon at the location indicating that the node is added to the workflow. The device updates the workflow model to include, based on the placement, a reference to executable code that corresponds to the node, and to include, based on the location, information about an execution sequence of executable codes referenced in the workflow model.

Abstract: A method for validating a message recipient includes: storing, in a memory of a processing server, a device profile, wherein the device profile is related to a mobile computing device and includes at least a device identifier, and token validation data; receiving, by a receiver of the processing server, a data signal from an external system that is encoded with a message packet, wherein the message packet includes at least the device identifier, a device token, and a content message; validating, by a processing device of the processing server, the device token using at least the token validation data; and electronically transmitting, by a transmitter of the processing server, the content message to the mobile computing device.

Abstract: The present invention relates to the field of electronic transactions, and provides methods and systems for reducing user interventions necessary for authentication of transactions. In an embodiment the invention implements an authentication server configured to receive from a terminal device an electronic payment transaction request and device IDs corresponding to devices having wireless communication capability that are detected within a wireless communication range of the terminal device. The authentication server selects a payment account for implementing the requested electronic payment transaction, wherein selection of the payment account is based on a determination that one or more of the device IDs received from the terminal device are associated with the identified payment account, and thereafter generates a payment transaction authentication decision.

Abstract: The present disclosure generally relates to an electronic system, a computerized method, and a non-transitory computer-readable storage medium for advertisement pricing. The system comprises a host server configured for performing steps of the method comprising: generating an electronic map representation of a geographical area; demarcating the electronic map representation into a plurality of demarcated regions; retrieving, from a transaction database, transaction data associated with merchant transactions in the plurality of demarcated regions; receiving, from a telecommunications service provider, mobile traffic data for generating consumer traffic data indicative of consumer traffic activity in the plurality of demarcated regions; and performing an advertisement pricing process to determine advertisement prices for each demarcated region based on at least the transaction data and consumer traffic data.

Abstract: A method for validating a message recipient includes: storing, in a memory of a processing server, a device profile, wherein the device profile is related to a mobile computing device and includes at least a device identifier, and token validation data; receiving, by a receiver of the processing server, a data signal from an external system that is encoded with a message packet, wherein the message packet includes at least the device identifier, a device token, and a content message; validating, by a processing device of the processing server, the device token using at least the token validation data; and electronically transmitting, by a transmitter of the processing server, the content message to the mobile computing device.