Abstract: A product management computer system that can include one or more processors, and a computer-readable medium comprising instructions stored therein, which when executed by the processors, can cause the processors to: receive data representing a target product of interest to a user of the system, wherein the target product is described by a plurality of variable-value attributes associated; process the received data through a machine-learning-produced algorithm and generating data representing a first set of substitute product candidates for the target product and wherein each substitute product candidate has a corresponding match-score that represents a degree of determined similarity between that substitute product candidate and the target product; transform the generated data that represents the first set of substitute product candidates into data representing a refined set of substitute product candidates by processing the generated data utilizing a constraint-based algorithm; and output data representing

Abstract: A product management computer system that can include one or more processors, and a computer-readable medium comprising instructions stored therein, which when executed by the processors, can cause the processors to: receive data representing a target product of interest to a user of the system, wherein the target product is described by a plurality of variable-value attributes associated; process the received data through a machine-learning-produced algorithm and generating data representing a first set of substitute product candidates for the target product and wherein each substitute product candidate has a corresponding match-score that represents a degree of determined similarity between that substitute product candidate and the target product; transform the generated data that represents the first set of substitute product candidates into data representing a refined set of substitute product candidates by processing the generated data utilizing a constraint-based algorithm; and output data representing

Abstract: A computer implemented method for identifying substitute products for a target product via a distributor's product management computer system, the method includes: receiving, at a distributor's product management computer system, data representing a target product of interest to a user of the system, wherein the target product is described by a plurality of variable-value attributes associated therewith; processing the received data through a machine-learning-produced algorithm and thereby generating data representing a first set of substitute product candidates for the target product and wherein each substitute product candidate has a corresponding match-score that represents a degree of determined similarity between that substitute product candidate and the target product; transforming the generated data that represents the first set of substitute product candidates into data representing a refined set of substitute product candidates by processing the generated data utilizing a constraint-based algorithm;

Abstract: A computer method for identifying product in a distributor's inventory system that fulfills a product request made via a natural language query. the natural language query is received as a product request including multiple words in sequential order. The words are vectorized into word-vectors that are concatenated and used to generate a query embedding. The query embedding is processed utilizing a trained product category classifier that predicts which product category the requested product belongs. Forward and backward sequence vectors are generated from the sequentially ordered words of the query that are concatenated and processed using a trained model specific to the predicted product category. The sequence vectors represent positional relationships between the words of the natural language query. Thereafter, the system identifies product attribute(s) embodied in the natural language query that each correspond to a predetermined key-characteristic of the category.

Abstract: A method and device for execution of deep neural network (DNN) in an internet of things (IoT) edge network are provided. In an embodiment, at least one edge device within communication range of an IoT device are selected. Further, a network for connecting the IoT device with the at least one selected edge device is identified. A split ratio is determined based on an inference time of the DNN and a transmission time required for transmitting output of each layer of DNN from the IoT device to the selected at least one edge device. Finally, a plurality of layers of the DNN are split into a first part and a second part based on the split ratio, and the second part is transmitted to the selected at least one edge device through the identified network. The first part is executed on the IoT device, and the second part is executed on the selected at least one edge device.

Abstract: Provided are a computer program product, moveable robot block, and method for a moveable robot block deployed to form a barrier and sense environmental conditions. A command is received to couple to a location in a coordinate system comprising the barrier formed of a plurality of moveable robot blocks. Movement motors are controlled to cause the moveable robot block to move to the location in the coordinate system to couple to the barrier according to the command. An environmental sensor senses an environmental condition related to water sensed external to the moveable robot block when the moveable robot block is coupled to the barrier. The environmental condition is transmitted to the assembly management system over the network.

Abstract: Provided are a computer program product, system, and method for controlling moveable robot blocks to dynamically form a barrier. A barrier plan is generated indicating a placement of moveable robot blocks at locations in a coordinate system to form a barrier in a geographical area, wherein the coordinates system indicates locations for the moveable robot blocks to form the barrier. Commands are transmitted to the moveable robot blocks deployed in the geographical area where the barrier is to be formed to cause the moveable robot blocks to move to the locations in the coordinate system in the barrier plan to form the barrier.

Abstract: Described herein are techniques for generating and utilizing drivability maps. The techniques including a method comprising receiving a set of vehicle performance records including a first vehicle performance record comprising a vehicle location, a vehicle configuration, and traction control system information. The method further includes determining, from the set of vehicle performance records, a respective Coefficient of Friction (CoF) value for respective road segments including a first CoF value for a first road segment. The method further includes determining, from the set of vehicle performance records and the respective CoF values, a first CoF threshold for a first vehicle configuration. The method further includes generating a drivability map for the first vehicle configuration indicating different levels of drivability for different road segments based on the respective CoF values for respective road segments and the first CoF threshold for the first vehicle configuration.

Abstract: Described herein are techniques for generating and utilizing drivability maps. The techniques including a method comprising receiving a set of vehicle performance records including a first vehicle performance record comprising a vehicle location, a vehicle configuration, and traction control system information. The method further includes determining, from the set of vehicle performance records, a respective Coefficient of Friction (CoF) value for respective road segments including a first CoF value for a first road segment. The method further includes determining, from the set of vehicle performance records and the respective CoF values, a first CoF threshold for a first vehicle configuration. The method further includes generating a drivability map for the first vehicle configuration indicating different levels of drivability for different road segments based on the respective CoF values for respective road segments and the first CoF threshold for the first vehicle configuration.

Abstract: Provided are a computer program product, moveable robot block, and method for a moveable robot block deployed to form a barrier and sense environmental conditions. A command is received to couple to a location in a coordinate system comprising the barrier formed of a plurality of moveable robot blocks. Movement motors are controlled to cause the moveable robot block to move to the location in the coordinate system to couple to the barrier according to the command. An environmental sensor senses an environmental condition related to water sensed external to the moveable robot block when the moveable robot block is coupled to the barrier. The environmental condition is transmitted to the assembly management system over the network.

Abstract: Provided are a computer program product, system, and method for controlling moveable robot blocks to dynamically form a barrier. A barrier plan is generated indicating a placement of moveable robot blocks at locations in a coordinate system to form a barrier in a geographical area, wherein the coordinates system indicates locations for the moveable robot blocks to form the barrier. Commands are transmitted to the moveable robot blocks deployed in the geographical area where the barrier is to be formed to cause the moveable robot blocks to move to the locations in the coordinate system in the barrier plan to form the barrier.