Abstract: Methods and systems for managing inventory at a retail facility include an image capture device having a field of view that includes a product display shelf of the retail facility, an electronic database that stores a planogram of product display shelves at the retail facility and a computing device. The computing device obtains an image of a product display shelf, detects the individual packages of each of the different products captured in the image, defines different product display areas for the different products on the product display shelf captured in the image, and generates virtual boundary lines that surround each of the defined product display areas. The computing device also determines an identity of a product contained located in each defined product display area, and associates the virtual boundary lines surrounding each defined product display area with an identifier unique to the product contained in the defined product display area.

Abstract: Methods and systems for managing inventory at a retail facility include an image capture device having a field of view that includes a product display shelf of the retail facility, an electronic database that stores defined product display areas and assigned unique identifiers of the products on the product display shelves, and a computing device. The computing device obtains an image of a product display shelf, detects the individual packages of the different products captured in the image, identifies the products associated with the packages detected in the image by utilizing reference information stored in the database, determines whether one or more of the products on the product display shelf is associated with an out of stock event, and transmits an out of stock alert to an electronic device of a worker at the retail facility, notifying the worker to replenish the depleted products on the product display shelf.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to detecting a mis-scan of an item. In some embodiments, there is provided a system for detecting a mis-scan of an item for purchase comprising a checkout station; a first staging location; a second staging location; a first area of interest at the checkout station; a second area of interest at the checkout station; a camera; and a control circuit configured to: receive an identifier of a first item; detect a hand of a user purchasing the first item, the first item, and a scanner on a first image captured by the camera; in response to the detection of the hand, the first item, and the scanner, initiate detection of mis-scan items during a checkout process; determine that a payment transaction has been received; and stop the detection of mis-scan items.

Abstract: This disclosure relates to a development and application of a deep-learning neural network (DNN) model for identifying relevance of an information item returned by a search engine in response to a search query by a user, with respect to the search query and a profile for the user. The DNN model includes a set of neural networks arranged to learn correlations between queries, search results, and user profiles using dense numerical word or character embeddings and based on training targets derived from a historical search log containing queries, search results, and user-click data. The DNN model help identifying search results that are relevant to users according to their profiles.

Abstract: A device may obtain, for a set of transactions, a set of transaction values associated with a particular industry. The device may determine one or more sample statistical distributions for a probabilistic transaction model by using one or more machine learning techniques. The one or more sample statistical distributions may be similar to one or more actual statistical distributions that are associated with the set of transaction values. The device may generate simulated transaction information using the probabilistic transaction model. The device may perform one or more actions after generating the simulated transaction information.

Abstract: Mechanisms for processing a knowledge canvassing request receive a request specifying an entity of interest from an originator of the request and analyze the request to extract a feature of the request. The mechanisms determine whether the request is a targeted natural language question to be answered or a knowledge canvassing request, based on the extracted feature. In response to determining that the request is a knowledge canvassing request, the mechanisms process the request by identifying entities represented in a knowledge graph data structure as being related to the entity of interest. The mechanisms output results of the processing of the request to the originator of the request.

Abstract: This disclosure relates to a development and application of a deep-learning neural network (DNN) model for identifying relevance of an information item returned by a search engine in response to a search query by a user, with respect to the search query and a profile for the user. The DNN model includes a set of neural networks arranged to learn correlations between queries, search results, and user profiles using dense numerical word or character embeddings and based on training targets derived from a historical search log containing queries, search results, and user-click data. The DNN model help identifying search results that are relevant to users according to their profiles.

Abstract: A device may obtain, for a set of transactions, a set of transaction values associated with a particular industry. The device may determine one or more sample statistical distributions for a probabilistic transaction model by using one or more machine learning techniques. The one or more sample statistical distributions may be similar to one or more actual statistical distributions that are associated with the set of transaction values. The device may generate simulated transaction information using the probabilistic transaction model. The device may perform one or more actions after generating the simulated transaction information.

Abstract: A device may receive information associated with a user that is to be classified. The information may be associated with multiple data formats. The device may identify a set of entities based on the information associated with the user that is to be classified. The device may generate a first graph data structure based on the set of entities. The device may determine a similarity score associated with the first graph data structure and a second graph data structure. The second graph data structure may be associated with a set of classified users. The device may provide information that identifies the similarity score to permit and/or cause an action to be performed.

Abstract: Mechanisms are provided for processing a knowledge canvassing request. The mechanisms receive a request specifying an entity of interest from an originator of the request and analyze the request to extract a feature of the request. The mechanisms determine whether the request is a targeted natural language question to be answered or a knowledge canvassing request, based on the extracted feature. In response to determining that the request is a knowledge canvassing request, the mechanisms process the request by identifying entities represented in a knowledge graph data structure as being related to the entity of interest. The mechanisms output results of the processing of the request to the originator of the request.

Abstract: Mechanisms are provided for processing a knowledge canvassing request. The mechanisms receive a request specifying an entity of interest from an originator of the request and analyze the request to extract a feature of the request. The mechanisms determine whether the request is a targeted natural language question to be answered or a knowledge canvassing request, based on the extracted feature. In response to determining that the request is a knowledge canvassing request, the mechanisms process the request by identifying entities represented in a knowledge graph data structure as being related to the entity of interest. The mechanisms output results of the processing of the request to the originator of the request.