Abstract: Systems and methods for evaluating reliability of a model are disclosed, including a processor that may include a data augmentor and a model evaluator. The data augmentor may receive a task data pertaining to information related to a pre-defined task to be performed by the model. The data augmentor may augment the task data to obtain an augmented aspect data. The model evaluator may evaluate a trained model based on the augmented aspect data to obtain aspect evaluation metrics. The model may be an artificial intelligence (AI) model that may be trained using the task data. The evaluation may enable to assess performance of the trained model by computing a performance score based on the aspect evaluation metrics. The performance score may help evaluate the reliability of the model in a pre-defined domain.

Abstract: An Artificial Intelligence (AI)-based data matching and alignment system identifies similar data sources for a target data source from a data corpus and generates a knowledge graph that enables downstream applications seamless access to data in the data corpus. The system extracts column features at different levels for the target data source and a plurality of data sources from the data corpus. Feature matrices are built from the features of the target data source and the plurality of data sources. Candidate data sources similar to the target data source are filtered from the plurality of data sources using the feature matrices. The tree-based similarity is estimated and K Nearest Neighbor (KNN) graphs are built to identify columns from the candidate data sources that are similar to columns of the target data source to build the knowledge graph.

Abstract: In some examples, artificial intelligence based music playlist reordering and song performance assessment may include ascertaining listening data for a plurality of tracks, and generating a plurality of sessions. Tracks that have been played more than a specified play threshold may be identified and retained. Sessions that are greater than a minimum session length threshold and less than a maximum session length threshold may be retained. Input-output track sequences may be generated for the retained sessions. Unique identifiers may be assigned to each of the tracks present across the retained sessions. Each input-output track sequence may be vectorized based on associated unique identifiers. A neural network model may be trained based on the vectorized input-output track sequences. For an input playlist, the trained neural network model may be used to generate a modified playlist. Additionally or alternatively, a user-song interaction graph may be used to generate another modified playlist.

Abstract: In some examples, artificial intelligence based music playlist curation may include ascertaining listening data for a plurality of tracks, and generating a plurality of embeddings that represent the plurality of tracks. A replacement track for an existing track in an input playlist may be generated. Alternatively or additionally, at least one additional track may be added to the input playlist. Alternatively or additionally, based on a seed set of tracks, an output playlist that includes a specified number of tracks that is greater than a number of tracks in the seed set of tracks may be generated. Alternatively or additionally, based on a plurality of specified attributes, the plurality of embeddings may be partitioned into a plurality of clusters corresponding to the plurality of specified attributes.

Abstract: In some examples, artificial intelligence based music playlist curation may include ascertaining listening data for a plurality of tracks, and generating a plurality of embeddings that represent the plurality of tracks. A replacement track for an existing track in an input playlist may be generated. Alternatively or additionally, at least one additional track may be added to the input playlist. Alternatively or additionally, based on a seed set of tracks, an output playlist that includes a specified number of tracks that is greater than a number of tracks in the seed set of tracks may be generated. Alternatively or additionally, based on a plurality of specified attributes, the plurality of embeddings may be partitioned into a plurality of clusters corresponding to the plurality of specified attributes.

Abstract: In some examples, artificial intelligence based music playlist reordering and song performance assessment may include ascertaining listening data for a plurality of tracks, and generating a plurality of sessions. Tracks that have been played more than a specified play threshold may be identified and retained. Sessions that are greater than a minimum session length threshold and less than a maximum session length threshold may be retained. Input-output track sequences may be generated for the retained sessions. Unique identifiers may be assigned to each of the tracks present across the retained sessions. Each input-output track sequence may be vectorized based on associated unique identifiers. A neural network model may be trained based on the vectorized input-output track sequences. For an input playlist, the trained neural network model may be used to generate a modified playlist. Additionally or alternatively, a user-song interaction graph may be used to generate another modified playlist.

Abstract: Image analysis techniques, including object recognition analysis, are applied to images obtained by one or more image capture devices deployed within inventory environments. The object recognition analysis provides object recognition data (that may include one or more recognized product instances) based on stored product (training) images. In turn, a variety of functionalities may be enabled based on the object recognition data. For example, a planogram may be extracted and compared to a target planogram, or at least one product display parameter for a product can be determined and used to assess presence of the product within the inventory environment, or to determine compliance of display of the product with a promotional objective. In yet another embodiment, comparisons may be made within a single image or between multiple images over time to detect potential conditions requiring response. In this manner, efficiency and effectiveness of many previously manually-implemented tasks may be improved.

Abstract: Image analysis techniques, including object recognition analysis, are applied to images obtained by one or more image capture devices deployed within inventory environments. The object recognition analysis provides object recognition data (that may include one or more recognized product instances) based on stored product (training) images. In turn, a variety of functionalities may be enabled based on the object recognition data. For example, a planogram may be extracted and compared to a target planogram, or at least one product display parameter for a product can be determined and used to assess presence of the product within the inventory environment, or to determine compliance of display of the product with a promotional objective. In yet another embodiment, comparisons may be made within a single image or between multiple images over time to detect potential conditions requiring response. In this manner, efficiency and effectiveness of many previously manually-implemented tasks may be improved.

Abstract: Image analysis techniques, including object recognition analysis, are applied to images obtained by one or more image capture devices deployed within inventory environments. The object recognition analysis provides object recognition data (that may include one or more recognized product instances) based on stored product (training) images. In turn, a variety of functionalities may be enabled based on the object recognition data. For example, a planogram may be extracted and compared to a target planogram, or at least one product display parameter for a product can be determined and used to assess presence of the product within the inventory environment, or to determine compliance of display of the product with a promotional objective. In yet another embodiment, comparisons may be made within a single image or between multiple images over time to detect potential conditions requiring response. In this manner, efficiency and effectiveness of many previously manually-implemented tasks may be improved.

Abstract: Image analysis techniques, including object recognition analysis, are applied to images obtained by one or more image capture devices deployed within inventory environments. The object recognition analysis provides object recognition data (that may include one or more recognized product instances) based on stored product (training) images. In turn, a variety of functionalities may be enabled based on the object recognition data. For example, a planogram may be extracted and compared to a target planogram, or at least one product display parameter for a product can be determined and used to assess presence of the product within the inventory environment, or to determine compliance of display of the product with a promotional objective. In yet another embodiment, comparisons may be made within a single image or between multiple images over time to detect potential conditions requiring response. In this manner, efficiency and effectiveness of many previously manually-implemented tasks may be improved.

Abstract: Image analysis techniques, including object recognition analysis, are applied to images obtained by one or more image capture devices deployed within inventory environments. The object recognition analysis provides object recognition data (that may include one or more recognized product instances) based on stored product (training) images. In turn, a variety of functionalities may be enabled based on the object recognition data. For example, a planogram may be extracted and compared to a target planogram, or at least one product display parameter for a product can be determined and used to assess presence of the product within the inventory environment, or to determine compliance of display of the product with a promotional objective. In yet another embodiment, comparisons may be made within a single image or between multiple images over time to detect potential conditions requiring response. In this manner, efficiency and effectiveness of many previously manually-implemented tasks may be improved.

Abstract: Image analysis techniques, including object recognition analysis, are applied to images obtained by one or more image capture devices deployed within inventory environments. The object recognition analysis provides object recognition data (that may include one or more recognized product instances) based on stored product (training) images. In turn, a variety of functionalities may be enabled based on the object recognition data. For example, a planogram may be extracted and compared to a target planogram, or at least one product display parameter for a product can be determined and used to assess presence of the product within the inventory environment, or to determine compliance of display of the product with a promotional objective. In yet another embodiment, comparisons may be made within a single image or between multiple images over time to detect potential conditions requiring response. In this manner, efficiency and effectiveness of many previously manually-implemented tasks may be improved.

Abstract: Image analysis techniques, including object recognition analysis, are applied to images obtained by one or more image capture devices deployed within inventory environments. The object recognition analysis provides object recognition data (that may include one or more recognized product instances) based on stored product (training) images. In turn, a variety of functionalities may be enabled based on the object recognition data. For example, a planogram may be extracted and compared to a target planogram, or at least one product display parameter for a product can be determined and used to assess presence of the product within the inventory environment, or to determine compliance of display of the product with a promotional objective. In yet another embodiment, comparisons may be made within a single image or between multiple images over time to detect potential conditions requiring response. In this manner, efficiency and effectiveness of many previously manually-implemented tasks may be improved.

Abstract: Image analysis techniques, including object recognition analysis, are applied to images obtained by one or more image capture devices deployed within inventory environments. The object recognition analysis provides object recognition data (that may include one or more recognized product instances) based on stored product (training) images. In turn, a variety of functionalities may be enabled based on the object recognition data. For example, a planogram may be extracted and compared to a target planogram, or at least one product display parameter for a product can be determined and used to assess presence of the product within the inventory environment, or to determine compliance of display of the product with a promotional objective. In yet another embodiment, comparisons may be made within a single image or between multiple images over time to detect potential conditions requiring response. In this manner, efficiency and effectiveness of many previously manually-implemented tasks may be improved.

Abstract: A cargo security sensing system includes a smart container having a sensor, wherein the smart container is a standard shipping cargo container that includes the sensor. The sensor may be any suitable type of sensing device capable of sensing changes in environmental conditions. The sensing system includes a plurality of dumb containers in corresponding relation to the smart container, wherein, the dumb container may be any suitable shipment container, including the same type of cargo shipment container or other type, as the smart container, excluding the sensor. The system for cargo security provides for the smart container to detect an event occurring with respect to either the smart container itself and/or the nearby dumb containers. An event may be an occurrence detected by the sensor, such that the smart container may notify of any potential security breaches in any of the containers.

Abstract: To facilitate the recognition and interpretation of actions undertaken within an environment, the environment is associated with a precision positioning system (PPS) and a controller in communication with the PPS. Within the environment, an entity moves about in furtherance of one or more tasks to be completed within the environment. The PPS determines position data corresponding to at least a portion of the entity, which position data is subsequently compared with at least one known action corresponding to a predetermined task within the environment. Using a state-based task model, recognized actions may be interpreted and used to initiate at least one system action based on the current state of the task model and correspondence of the position data to the at least one known action. Furthermore, based on the recognized action, the controller may provide information to the entity or captured event data may be associated with the recognized action.

Abstract: “Context” of an environment or area is considered to be one or more characteristics of the environment or area, manifested by the presence of various types of equipment or devices. Detection of various types of devices and equipment in a localized area is by way of short-range wireless signals over which data about the devices is transmitted to one or more subscriber units. The subscriber units can forward context-determinative data and information to a service provider which can vicariously use the equipment or devices in the area via the subscriber device.

Abstract: A cargo security sensing system includes a smart container having a sensor, wherein the smart container is a standard shipping cargo container that includes the sensor. The sensor may be any suitable type of sensing device capable of sensing changes in environmental conditions. The sensing system includes a plurality of dumb containers in corresponding relation to the smart container, wherein, the dumb container may be any suitable shipment container, including the same type of cargo shipment container or other type, as the smart container, excluding the sensor. The system for cargo security provides for the smart container to detect an event occurring with respect to either the smart container itself and/or the nearby dumb containers. An event may be an occurrence detected by the sensor, such that the smart container may notify of any potential security breaches in any of the containers.

Abstract: Enhanced delivery of services are provided through a wireless mobile device and one or more location service channels, preferably at the direction of the user of the mobile device requesting the desired service through common context-based user interfaces. The mobile device communicates with local and remote service providers. The context is determined by accessing information in the user profile, by sensors on the mobile device identifying items of interest, or by identifying the location of the mobile device. An aggregator server maintains a registry of location service channels and manages the access to the channels by the mobile device users and the service providers.

Abstract: A method and system for using individualized customer models when operating a retail establishment is provided. The individualized customer models may be generated using statistical analysis of transaction data for the customer, thereby generating sub-models and attributes tailored to customer. The individualized customer models may be used in any aspect of a retail establishment's operations, ranging from supply chain management issues, inventory control, promotion planning (such as selecting parameters for a promotion or simulating results of a promotion), to customer interaction (such as providing a shopping list or providing individualized promotions).