Disclosed is a mobile and web-based personal history capture-store-retrieval process and system intended to be used by individuals or groups (including companies and educational institutions) to record personal historical information in multimedia file format from any source, such as the individual's smartphone, tablet, or personal computer. The system will assist individuals in the recording and storage and retrieval of the individual's (or group's) personal histories. The system employs an artificial intelligence engine to analyze user content (e.g., information, data, metadata, and historical content associated with such user) and design prompts to the user to input additional information. The system also provides a method for creating an e-book from the content, either by a single user or by collaboration among multiple users. Disclosed also is a computer implemented method and system and related computer program product for capturing, storing, retrieving and disseminating personal and/or group legacy and history information.

Robotic task program synthesis embodiments are presented that generally synthesize a robotic task program based on received examples of repositioning tasks. In one implementation, the exemplary repositioning tasks are human demonstrations of object manipulation in an actual or displayed robot workspace. A domain specific language (DSL) designed for object repositioning tasks is employed for the robotic control program. In general, candidate robotic task programs are generated from the example tasks. Each candidate program includes instructions for causing the robot to reposition objects, and represents a different permutation of instructions consistent with the received example tasks. The candidate programs are ranked, and whenever the top ranking program accomplishes the repositioning specified in each example task, it is designated as the synthesized robotic task program.

A recommender system (18) is described which is capable of being controlled using training data provided by an external controller (16). The controller (16) is arranged to control the input of training data from client devices (22) to a recommender system (18) to drive the recommender system's performance towards a set level.

A method for extracting information from electronic documents, including: learning terms and term variants from a training corpus, wherein the terms and the term variants correspond to a specialized dictionary related to the training corpus; generating a list of negative indicators found in the training corpus; performing a partial match of the terms and the term variants in a set of electronic documents to create initial match results; and performing a negation test using the negative indicators and a positive terms test using the terms and the term variants on the initial match results to remove matches from the initial match results that fail either the negation test or the positive terms test, resulting in final match results.

A diagnosis support apparatus which supports diagnosis based on information associated with a diagnosis name in advance is provided. In the diagnosis support apparatus, an acquisition unit acquires the diagnosis name set by a user. A providing unit provides negative information for the diagnosis name set by the user based on the information. With the above arrangement, the diagnosis support apparatus selects and presents information influencing the diagnosis name expected by the user, thereby efficiently presenting information required by the user.

A software application that is able to manage a collection of robot repairing resources can be used to assist technicians in repairing and solving hardware or software malfunctions within an electro-mechanical robot. The software application is able to simultaneous monitor multiple different electro-mechanical robots by receiving diagnostic information from them. The software application can identify a malfunction within one of the electro-mechanical robots by comparing its diagnostic information against a set of robot repair manuals. The software application will then select an optimal AI algorithm that has the best chance of repairing the hardware or software malfunction. The software application continues by implementing the optimal AI algorithm with a set of cloud accessible robot repairing applications, a technician's intervention, or a combination thereof.

In one embodiment, a learning data processor determines a plurality of machine learning features in a computer network to collect. Upon receiving data corresponding to the plurality of features, the learning data processor may aggregate the data, and pushes the aggregated data for select features to interested learning machines associated with the computer network.

A system for providing a magnetic resonance treatment may include components that provide the system with an ability to treat pain and relieve symptoms of the subject. In another embodiment, a method of providing a magnetic resonance treatment includes components that provide the system with an ability to treat pain, relieve symptoms, provide relaxation, and improve the overall comfort and well-being of the subject.

A method and a system are disclosed for predicting sales of item listings on a network-based system. For example, historical transaction data generated by the network-based system is accessed to create a prediction model. A feature predictive of an item being sold through the network-based system is selected. A training set is created by extracting the predictive feature from the historical transaction data. The prediction model is trained based on the training data set to predict the probability of an item listing being sold through the network-based system. The prediction model is used to rank search results, and the search results can be presented through the network-based system.

Communication networks in general may benefit from appropriate policy based management. More particularly, heterogeneous networks or HetNets may benefit from methods for policy based management. A method according to certain embodiments includes detecting a reportable event. The method also includes determining whether or how to report the event based on a probability criterion. The method further includes taking an action with respect to reporting the event based on whether the probability criterion is met.