Abstract: Disclosed is a method for delivering decision-supported patient data to a clinician to aid the clinician with the diagnosis and treatment of a medical condition. The method including presenting a patient with questions generated by a decision-support module and gathering patient data indicative of the responses to the questions. Each question presented to the patient is based upon the prior questions presented to and the patient data gathered from the patient. Upon receiving the patient data from the client module, evaluating the patient data at the module to generate decision-supported patient data, this supported patient data includes medical condition diagnoses, pertinent medical parameters for the medical condition, and medical care recommendations for the medical condition. At the client module or a clinician's client module, presenting the clinician with this patient data in either a standardized format associated with a progress note or a format selected by the clinician.

Abstract: The present invention relates to a system and methodology for extending and making more appropriate the interactive decisions made by automated services by taking into consideration estimates of the time required by users for the cognitive processing of problems, visualizations, and content based on several factors, including the competency and familiarity of the user with the output and nature of the sequences of output, the complexity of the output and overall tasks being considered, and the context of the user. The methods allow automated applications to control the rate at which the applications interact with users. Portions of automated services are controlled in view of limited human processor capabilities in design/operation of such services, and/or visualizations/output from the services (e.g., amount of dwell time provided/considered before a next automated sequence is displayed/invoked).

Abstract: A system is disclosed that provides a goal based learning system utilizing a rule based expert training system to provide a cognitive educational experience. The system provides the user with a simulated environment that presents a business opportunity to understand and solve optimally. Mistakes are noted and remedial educational material presented dynamically to build the necessary skills that a user requires for success in the business endeavor. The system utilizes an artificial intelligence engine driving individualized and dynamic feedback with synchronized video and graphics used to simulate real-world environment and interactions. Multiple “correct” answers are integrated into the learning system to allow individual learning experiences in which navigation through the system is at a pace controlled by the learner.

Abstract: A system and method is provided for identifying useful content in a knowledge repository accessed by a plurality of users. The system and method includes the operation of identifying each unique user that accesses a document in the knowledge repository. Another operation is tracking an amount of time each unique user has a document open to create a set of document open time values. Document usefulness is then determined based on a comparison of the document open time values for the unique users.

Abstract: A system for operating/observing a monitoring device of a process controller device of/in a remote operator unit. An additional function block is provided in/on the monitoring device, which intervenes in the communication between the monitoring device and the connected operator units, at least partially takes over the functions that are to be executed by one or more of the operator units and processes the information to be displayed on the operator unit in such a way that the information can be directly displayed.

Abstract: An input data group is converted into an output data group in accordance with coefficients. For each coefficient, a current coefficient value, a coefficient value greater than the current coefficient value and a coefficient value smaller than the current coefficient value may be computed. One of the current, greater and smaller coefficient values is selected on the basis of errors between the output data computed with the current, greater and smaller coefficient values and an ideal output data group corresponding to the input data group.

Abstract: A fuzzy rough classification section 7 classifies an input pattern into a plurality of category groups and calculates their attribution factors. A plurality of fine classification sections 8 calculate similarities in respective category groups. In each fine classification section 8, a weight change amount control section 82 controls the weight change amount in an associated similarity calculating section 81 in accordance with the group attribution factor selected by a second group selecting section (10), thereby enabling a cooperative learning operation among plural fine classification sections 8. A force learning control section 17 performs a supplemental learning operation, if the input pattern is rejected regardless of its correctness, until its reject judge value is reduced below a predetermined reject threshold.