System and method for hazardous incident decision support and training

• US 7,194,395 B2
• Filed: 02/12/2001
• Issued: 03/20/2007
• Est. Priority Date: 02/23/2000
• Status: Expired due to Fees

• Current Assignee: United States Of America As Represented By The Secretary Of The Army (Government of the United States of America)
• Original Assignee: The United States Of America As Represented By The Secretary Of The Army
• Inventors: Genovese, James A.
• Primary Examiner(s): FERRIS III, FRED O
• Application Number:
  US09/781,512
• Publication Number:
  US 20060085367A1
• Time in Patent Office: 2,227 Days
• Field of Search: 703/6, 703/11, 700/83, 434/218, 434/219, 707/104
• US Class Current: 703/6
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: Y10S 707/99945 Object-oriented database structure processing
• CPC Class Codes: Y10S 707/99948 Application of database or data structure, e.g. distributed, multimedia, or image

Abstract

A system and method for providing hazardous incident decision support and training includes a user interface component that receives situation definition data, a hazard assessment component and a decision aid. Decision support advice and decision prompts are presented by the system in response to the situation definition interface and hazard assessment. The hazard assessment and expert advice of the system are updated with elapsed time. In another embodiment of the present invention, graphical user interfaces are provided to display a set of menu entries wherein individual menu entries represent a hazardous incident characteristic. The user may select one or more of the menu entries to create a situation definition. Such definitions may be used, for example, to identify a hazardous agent based upon user-inputted signs and symptoms data.

Claims

1. A method of providing hazardous incident decision support and training, comprising:

acquiring observed signs and symptoms data from a user interface, wherein said data is input by a user to describe a given situation or condition which may or may not be associated with a hazardous incident;

acquiring agent characteristic data from records in a database, wherein each record includes data representative of a known hazardous agent; and

performing an initial agent identification by comparing the observed signs and symptoms acquired from the user interface with the agent characteristics data for each record in the database, wherein said performing an initial agent identification by comparing the observed signs and symptoms acquired from the user interface with the agent characteristics data for each record in the database comprises using the following algorithm;

Agent Identification=Max [W₁·

(number of matched Signs)+W₂·

(number of matched Symptoms)+W₃·

(number of matched Times-of-Onset)−

W₄·

(number of Mismatches)], wherein W represents the relative weights given to corresponding matches or mismatches, and Max represents a process in which the result or score obtained from applying the foregoing algorithm to each record in said database is evaluated to determine the record with the greatest numerical value.

2. The method of claim 1, wherein said observed signs data includes one or more of color, odor, chemical reaction, dispersion devices, explosive event, property damage, dead animals, or absence of signs, and wherein said observed symptoms data includes one or more of nausea, headache, tunnel vision, chest pain, vomiting, runny nose, reddish skin, or absence of symptoms.

3. A method of providing hazardous incident decision support and training, comprising:

acquiring observed signs and symptoms data from a user interface, wherein said data is input by a user to describe a given situation or condition which may or may not be associated with a hazardous incident;

acquiring agent characteristic data from records in a database, wherein each record includes data representative of a known hazardous agent; and

performing an initial agent identification by comparing the observed signs and symptoms acquired froni the user interface with the agent characteristics data for each record in the database, wherein said performing an initial agent identification by comparing the observed signs and symptoms acquired from the user interface with the agent characteristics data for each record in the database comprises using the following algorithm;

Agent Identification=Max [W₁·

(number of matched Signs)+W₂·

(number of matched Symptoms)+W₃·

(number of matched Times-of-Onset)−

W₄·

(number of Mismatches)+W₅·

(Sensor Input)+W₆·

(Sampling Data)], wherein W represents the relative weights given to corresponding matches or mismatches, and Max represents a process in which the result or score obtained from applying the foregoing algorithm to each record in said database is evaluated to determine the record with the greatest numerical value.

4. The method of claim 3, wherein said observed signs data includes one or more of color, odor, chemical reaction, dispersion devices, explosive event, property damage, dead animals, or absence of signs, and wherein said observed symptoms data includes one or more of nausea, headache, tunnel vision, chest pain, vomiting, runny nose, reddish skin, or absence of symptoms.

5. A method of providing hazardous incident decision support and training, comprising:

acquiring observed signs and symptoms data from a user interface, wherein said data is input by a user to describe a given situation or condition which may or may not be associated with a hazardous incident;

acquiring agent characteristic data from records in a database, wherein each record includes data representative of a known hazardous agent; and

performing an initial agent identification by comparing the observed signs and symptoms acquired from the user interface with the agent characteristics data for each record in the database, wherein said performing an initial agent identification by comparing the observed signs and symptoms acquired from the user interface with the agent characteristics data for each record in the database comprises using the following algorithm;

Agent Identification=Max [W₁·

(number of matched Signs)+W₂·

(number of matched Symptoms)+W₃·

(number of matched Times-of-Onset)−

W₄·

(number of Mismatches)+W₅·

(Sensor Input)+W₆·

(Sampling Data)+W₇·

(Time Data)+W₈·

(Intelligence)+W₉·

(Number of Casualties)+W₁₀·

(Distribution of Casualties)+W₁₁·

(MET Data)], wherein W represents the relative weights given to corresponding matches or mismatches, and Max represents a process in which the result or score obtained from applying the foregoing algorithm to each record in said database is evaluated to determine the record with the greatest numerical value.

6. The method of claim 5, wherein said observed signs data includes one or more of color, odor, chemical reaction, dispersion devices, explosive event, property damage, dead animals, or absence of signs, and wherein said observed symptoms data includes one or more of nausea, headache, tunnel vision, chest pain, vomiting, runny nose, reddish skin, or absence of symptoms.