Patent application number | Description | Published |
20090055342 | METHOD AND KNOWLEDGE STRUCTURES FOR REASONING ABOUT CONCEPTS, RELATIONS, AND RULES - A system and method for reasoning about concepts, relations and rules having a semantic network comprising at least one node from a predetermined set of node types, at least one link from a predetermined set of link types, and zero or more rules from a predetermined set of rule types, a subset of the rule types being matching rule types, each node and each link being associated with a set of zero or more rules; a network reasoning data structure having a reasoning type database having at least one regular expression, each of the regular expressions being a class of sequences having at least three node types and two link types, wherein the network reasoning data structure further has a context being a set of rules; and a reasoning engine having an activator for activating one or more activated paths in the semantic network, the set of activated paths having a common starting node in the semantic network, wherein the reasoning engine further has a validator for selecting a subset of the activated paths being valid paths, each rule from the set of rule matching types that is associated with one or more path elements on each valid path being matched by one or more rules in the context and wherein the reasoning engine further has a legal inferencer for selecting a subset of the set of valid paths being legal and valid paths, the legal and valid paths matching at least one of the regular expressions. | 02-26-2009 |
20110301965 | DYNAMICALLY PREDICTING PATIENT INFLUX INTO AN EMERGENCY DEPARTMENT - Current patients in an emergency department of a hospital are described according to their quantity, their triage classification levels, and their wait times to calculate a current patient backlog. A sum of weight-adjusted triage classification levels of all of the current patients is calculated. Current patient arrival rates in the emergency department are tracked to calculate a current change in patient arrival rates, which are compared with historical changes in patient arrival rates. A size of an imminent influx of arriving patients into the emergency department is then predicted based on the current patient backlog, the sum of weight-adjusted triage classification levels of patients currently in the emergency department, and the current change in patient arrival rates. | 12-08-2011 |
20110307264 | DYNAMICALLY ADJUSTING TRIAGE CLASSIFICATION LEVELS - An initial triage level classification for a latest patient to arrive at an emergency department (ED) is received. Availability levels of resources needed to treat the latest patient are electronically collected, along with triage level classifications for all other patients currently in the ED. The initial triage level classification of the latest patient is adjusted upward or downward based on the availability levels of resources needed to treat the latest patient and based on the triage level classifications for the patients in the ED. The triage level classifications for all patients currently in the ED are summed up. If a sum of all triage level classifications exceeds a first predetermined threshold, other resources are reallocated in order to provide the resources needed to treat the latest patient to arrive at the ED. If the sum of all triage level classifications exceeds a second predetermined threshold, then a disaster plan is implemented. | 12-15-2011 |
20120078062 | DECISION-SUPPORT APPLICATION AND SYSTEM FOR MEDICAL DIFFERENTIAL-DIAGNOSIS AND TREATMENT USING A QUESTION-ANSWERING SYSTEM - A decision-support system for medical diagnosis and treatment comprises software modules embodied on a computer readable medium, and the software modules comprise an input/output module and a question-answering module. The method receives patient case information using the input/output module, and generates a medical diagnosis or treatment query based on the patient case information and also generates a plurality of medical diagnosis or treatment answers for the query using the question-answering module. The method also calculates numerical values for multiple medical evidence dimensions from medical evidence sources for each of the answers using the question-answering module and also calculates a corresponding confidence value for each of the answers based on the numerical value of each evidence dimension using the question-answering module. The method further outputs the medical diagnosis or treatment answers, the corresponding confidence values, and the numerical values of each medical evidence dimension for one or more selected medical diagnosis or treatment answers using the input/output module. | 03-29-2012 |
20120078837 | DECISION-SUPPORT APPLICATION AND SYSTEM FOR PROBLEM SOLVING USING A QUESTION-ANSWERING SYSTEM - A decision-support system for problem solving comprises software modules embodied on a computer readable medium, and the software modules comprise an input/output module and a question-answering module. The method receives problem case information using the input/output module, generates a query based on the problem case information, and generates a plurality of answers for the query using the question-answering module. The method also calculates numerical values for multiple evidence dimensions from evidence sources for each of the answers using the question-answering module and calculates a corresponding confidence value for each of the answers based on the numerical value of each evidence dimension using the question-answering module. Further, the method outputs the answers, the corresponding confidence values, and the numerical values of each evidence dimension for one or more selected answers using the input/output module. | 03-29-2012 |
20120173243 | Expert Conversation Builder - An expert conversation builder contains a knowledge database that includes a plurality of dialogues having nodes and edges arranged as directed acyclic graphs. Users and authors of the system interface with the knowledge database through a graphical interface to author dialogues and to create expert conversations as threads traversing the node in the dialogues. | 07-05-2012 |
20140164303 | METHOD OF ANSWERING QUESTIONS AND SCORING ANSWERS USING STRUCTURED KNOWLEDGE MINED FROM A CORPUS OF DATA - In a method of answering questions and scoring answers, a title and at least one topical field are identified for a document. A field name and field content associated with the topical field is identified, and a title-oriented document is created by combining the title, the field name, and the field content associated with the topical field. For each title-oriented document, a term in the title is matched to previously established categories to produce a title concept identifier. The topical field is synthesized to produce a field concept identifier and a field content concept identifier. A question is received. The question topic term and the question content identifier are used to identify at least one question-matching relation instance. The title concept identifier of each question-matching relation instance is identified as a candidate answer to the question. Each candidate answer and a corresponding answer score is output. | 06-12-2014 |
20140164304 | METHOD OF ANSWERING QUESTIONS AND SCORING ANSWERS USING STRUCTURED KNOWLEDGE MINED FROM A CORPUS OF DATA - In a method of answering questions and scoring answers, a title and at least one topical field are identified for a document. A field name and field content associated with the topical field is identified, and a title-oriented document is created by combining the title, the field name, and the field content associated with the topical field. For each title-oriented document, a term in the title is matched to previously established categories to produce a title concept identifier. The topical field is synthesized to produce a field concept identifier and a field content concept identifier. A question is received. The question topic term and the question content identifier are used to identify at least one question-matching relation instance. The title concept identifier of each question-matching relation instance is identified as a candidate answer to the question. Each candidate answer and a corresponding answer score is output. | 06-12-2014 |