Abstract: Methods and systems for improving user generated ratings by machine classification of an entity are disclosed. Customer rating systems can be analyzed and the corresponding entity interaction on social media networks can be observed. A humanness rating (H value) can be assigned to an entity. The humanness rating can be determined from a multivariate function. The function's variables can be measurements of the entity's behavior on one or more social networks. The variables can be intrinsic to the entity. The variables can be based on account activity information. The variables can be based on social network information. The multivariate function can be implemented as a Bayesian classifier. The multivariate function can be implemented as a neural net. A calculated H value can be used to weigh ratings by an entity.

Abstract: Methods and systems for indexing patent related prior art citations are disclosed. Electronic documents can be obtained from one or more patent information systems. An OCR process can be performed on some of the electronic documents. Citations within the documents can be identified and compared to a trusted records list. The citations can be associated with one or more predetermined categories. For example, citations can be categorized into groups such as when a reference is cited (e.g., with original filing, pre and post allowance), who provided the reference (e.g., cited by the applicant, or the examiner), and how the reference is characterized (e.g., statutory basis, combination of references). The citations and corresponding categories can be output to a user or made available for subsequent processing.

Abstract: Disclosed is an apparatus and method for a mobile device to detect unexpected behavior associated with the mobile device. The mobile device may include a memory and a processor coupled to the memory. The processor may be configured to monitor at least one of physical conditions and/or device actions associated with the mobile device. If a call or a data transmission is requested, the processor may be configured to compare the monitored physical conditions and/or device actions to an anomaly threshold value and may execute an anomaly exception routine if the anomaly threshold value is exceeded.

Abstract: Disclosed is an apparatus and method for a mobile device to detect unexpected behavior associated with the mobile device. The mobile device may include a memory and a processor coupled to the memory. The processor may be configured to monitor at least one of physical conditions and/or device actions associated with the mobile device. If a call or a data transmission is requested, the processor may be configured to compare the monitored physical conditions and/or device actions to an anomaly threshold value and may execute an anomaly exception routine if the anomaly threshold value is exceeded.