Deep learning is used to identify specific, potential risks to an enterprise (of which product liability is the prime example here) while such risks are still internal electronic communications. The system involves mining and using existing classifications of data (e.g., from an internal litigation database, or from external sources such as customer complaints, and/or warranty claims) to train one or more deep learning algorithms, and then examining the enterprise's internal electronic communications with the trained algorithm, to generate a scored output that will enable enterprise personnel to be alerted to risks and take action in time to prevent the risks from resulting in harm to the enterprise or others.

Deep learning is used to identify a potential risk that a contract will be unenforceable due to a drafting error whereby one or more terms or phrases are ambiguous. The system involves mining and using existing classifications of data (e.g., from a litigation database) to train one or more deep learning algorithms, and then examining the internal electronic drafts of contracts with the trained algorithm, to generate a scored output that will enable enterprise personnel to be alerted to the ambiguity risks and take action in time to prevent the risks from resulting in harm to the enterprise.

Deep learning is used to identify specific, potential risks of missed diagnosis for a patient and reporting the risk to healthcare provider. The system involves mining and using existing electronic health records for specific medical diagnosis to train one or more deep learning algorithms, and then examining the internal electronic health record of the patient with the trained algorithm, to generate a scored output that will enable a healthcare provider to be alerted to potential risks of a missed diagnosis.

Deep learning is used to identify specific, potential financial advantage for an enterprise that are hidden in internal electronic documents. The system involves mining and using existing classifications of data (e.g., from previously sorted documents) to train one or more deep learning algorithms, and then examining internal electronic documents with the trained algorithm, to generate a scored output that will enable enterprise personnel to evaluate the identified documents for a potential financial advantage to the enterprise.

The invention relates in particular to the optimization of routing in a cluster comprising a plurality of nodes and static communication links connecting nodes of the plurality of nodes, said routing being based on load levels associated with the communication links. In order to establish a connection between at least two nodes of the cluster that have been identified (505), at least one route is determined (510) that connects the identified nodes according to the communication links, said route being determined according to the nodes identified, communication links and at least one load level associated with each communication link. A determined route is selected. Subsequently, a value of weight associated with the selected route is estimated (520) and a load level associated with each communication link of the selected route is incremented (525).

In various example embodiments, a system and method for using machine learning to define user controls for image adjustment is provided. In example embodiments, a new image to be adjusted is received. A weight is applied to reference images of a reference dataset based on a comparison of content of the new image to the reference image of the reference dataset. A plurality of basis styles is generated by applying weighted averages of adjustment parameters corresponding to the weighted reference images to the new image. Each of the plurality of basis styles comprises a version of the new image with an adjustment of at least one image control based on the weighted averages of the adjustment parameters of the reference dataset. The plurality of basis styles is provided to a user interface of a display device.

Health-related data is accessed; as is a database of payment card transaction data. At least a portion of the health-related data is linked to at least a portion of the payment card transaction data to obtain linked data. Statistical analysis is carried out on the linked data, and the results of the statistical analysis are made available to at least one appropriate party. Privacy is protected, for example, via an opt-in approach or through data aggregation.

A congestion notification method, a related device, and a system, where the method includes receiving, by a network side device, a congestion status of a target network area sent by a radio access network (RAN) congestion awareness function (RCAF) entity, where the target network area is a network area involved in an application service provided by a target application server; and sending, by the network side device, the congestion status of the target network area to the target application server. Hence, an application server may able to learn in time when congestion occurs, and adjust, according to the congestion situation, a related service in an area corresponding to the congestion situation.

Techniques are described to characterize motion patterns of a group of agents engaging in an activity. An analysis system receives input data associated with spatial and temporal information of at least one element of interest associated with the activity, where the object of interest may be a ball, person, animal or any other object in motion. The analysis system partitions the input data into a plurality of spatiotemporal segments and generates one or more representations of one or more sets of segments of the plurality of spatiotemporal segments based on one or more criteria. The analysis system computes a metric, such as an entropy value, for each of the one or more representations. Partial tracing data, such as ball movements in a sporting event, may be created using an inexpensive input device, such as a tablet computer, making the disclosed techniques available for a wide range of events and activities.

Systems and methods for applying and using context labels for data clusters are provided herein. A method described herein for managing a context model associated with a mobile device includes obtaining first data points associated with a first data stream assigned to one or more first data sources; assigning ones of the first data points to respective clusters of a set of clusters such that each cluster is respectively assigned ones of the first data points that exhibit a threshold amount of similarity and are associated with times within a threshold amount of time of each other; compiling statistical features and inferences corresponding to the first data stream or one or more other data streams assigned to respective other data sources; assigning context labels to each of the set of clusters based on the statistical features and inferences.