Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 va

Abstract: 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 va

Abstract: 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 va

Abstract: A method and structure for automatically producing bridging inferences that join two related input sentences, by applying a lexicon and ontology data structure to a first input sentence to produce first input tagged sentences, applying the lexicon and ontology data structure to a second input sentence to produce second input tagged sentences, matching each first input tagged sentence to first rules, generating first inferred tagged sentences from the first rules, matching the first inferred tagged sentences to second rules, generating second inferred tagged sentences from the second rules, matching the second inferred tagged sentences to third rules, generating third inferred tagged sentences from the third rules, and so on, until a final inferred tagged sentence matches any second input tagged sentence.

Abstract: A method and structure for automatically producing bridging inferences that join two related input sentences, by applying a lexicon and ontology data structure to a first input sentence to produce first input tagged sentences, applying the lexicon and ontology data structure to a second input sentence to produce second input tagged sentences, matching each first input tagged sentence to first rules, generating first inferred tagged sentences from the first rules, matching the first inferred tagged sentences to second rules, generating second inferred tagged sentences from the second rules, matching the second inferred tagged sentences to third rules, generating third inferred tagged sentences from the third rules, and so on, until a final inferred tagged sentence matches any second input tagged sentence.

Abstract: 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 va

Abstract: Disclosed is a method and system that automatically prompts a user or computer program for tasks, subtasks and supertasks of tasks, expected durations to complete tasks, deadlines, and priorities of the tasks. The invention automatically prompts the user or computer program at regular intervals for the current task and the percentage completion of the task, and sends the user or computer program a message to work on the task if the deadline has passed, informing the user or computer program it is taking longer on the task than the expected duration and in this case also sending the user or computer program a message to work on the supertask of the task, asking the user or computer program to wrap up the task if the task is near completion, and reminding the user or computer program to stay on task if the user or computer program is switching between tasks too quickly.

Abstract: A method and structure for automatically producing bridging inferences that join two related input sentences, by applying a lexicon and ontology data structure to a first input sentence to produce first input tagged sentences, applying the lexicon and ontology data structure to a second input sentence to produce second input tagged sentences, matching each first input tagged sentence to first rules, generating first inferred tagged sentences from the first rules, matching the first inferred tagged sentences to second rules, generating second inferred tagged sentences from the second rules, matching the second inferred tagged sentences to third rules, generating third inferred tagged sentences from the third rules, and so on, until a final inferred tagged sentence matches any second input tagged sentence.