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Multidimensional expert behavior emulation systemMultidimensional expert behavior emulation system description/claimsThe Patent Description & Claims data below is from USPTO Patent Application 20090089241, Multidimensional expert behavior emulation system. Brief Patent Description - Full Patent Description - Patent Application Claims
This application is related to commonly assigned, U.S. application Ser. No. ______, entitled “TRAINING A MULTI-DIMENSIONAL, EXPERT BEHAVIOR-EMULATION SYSTEM” by and concurrently filed herewith. This invention relates to a multi-dimensional, long-term behavior, computerized decision method and system. More particularly, the invention relates to providing computer operations to perform routine decisions based on the historical performance of experts in the decision process. There are many routine decision tasks where a person receives input data from a computer and analyzes the data to come to a decision. In some cases, these decision tasks are multi-dimensional in input and cannot easily be grouped into a relatively small number of classes. Because the decisions cannot be so grouped, the decision process does not lend itself to automated or expert processes provided in traditional recognition or classification problems. Further for many of these decision problems, it is difficult to measure the quality of each individual decision made by a person, but it is possible to measure the integral quality of a plurality of decisions as a whole made by the person over a period of time. This integral quality can be compared against other persons making similar decisions over a period of time and the relative expertise of each decision maker can be measured. One example of routine decision tasks based on input data as discussed above is in retail goods allocation tasks. In such tasks a distribution or allocation expert reviews input data on a computer display screen a quantity of retail goods to be allocated in various quantities from warehouses to multiple retail stores selling the goods. Where there are 10 to 1000+ stores in the business and the quantity of goods to be allocated to each store varies from 0 to 100+, the number of possible allocation outcomes can easily exceed several thousand. Such a decision problem is so multi-dimensional it does not lend itself to automated solution based on recognition and classification systems. Further, to measure the quality of an allocation by examining a specific allocation is not meaningful. For example, if a specific set of goods such as swimsuits is allocated to certain stores and turns out not to be profitable for those stores, this result may be due to weather conditions rather than lack of experience by the allocator. On the other hand, if over an entire season all the goods allocated by this same allocator generate the highest total profit or other metric, this same person might be recognized as an expert allocator. In other words, there is no absolutely right or wrong decision for each decision problem, but there are the best (expert) and the poorest decision makers. Another problem in computerizing routine decision tasks of the above type is that best practices in the environment of the decision problem may change over time. For example, in the allocation of retail goods, business practices may change over time because of changes to the competitive environment or changes in the goals of the business entity. In accordance with this invention an expert decision-making method is emulated based on a history of behaviors by experts in a variety of observed situations. A history of behaviors is built up from observations of actions taken by experts in analyzing a plurality of situations. The observations are captured, and behaviors from the observations are constructed. The behaviors indicate an association between situation features and methods with parameters for solving the situations. Situation data representative of a situation to be processed is received, and situation features are extracted from the situation data. A behavior method is recognized from a pattern of situation features. Parameter values for parameters in the recognized behavior method are calculated based on the situation features. The recognized behavior method is executed on the situation data using the parameter values to recommend a solution for the situation. The recommended solution has solution data representing a plan of action to provide the solution and remainder data representing unprocessed situation data. A test detects whether the remainder data is in a target range. If the remainder data is not in the target range, another behavior method from a pattern of situation features from the remainder data is recognized. Parameter values, based on the situation features from the remainder data, are calculated for parameters in this second recognized behavior method. This second recognized behavior method is executed on the remainder data using its parameter values to recommend a solution for the situation. These actions are repeated until the test detects the remainder data is in the target range. A solution strategy is displayed to a user. The strategy display includes all the recognized behavior methods with their parameter values and the solution data and remainder data for each recognized method. Adjustments to the strategy may be received from the user. The adjustments will be changes of behavior methods or changes of parameter values to arrive at the solution. The above calculation of parameter values is accomplished by recognizing parameter calculation rules and calculating the parameter values using the rules. A parameter calculation rule for each parameter in the behavior method is recognized from situation features. Recognizing a parameter calculation rule is based on feature/parameter-calculation-rules separation data in a pattern of features in multidimensional space with each feature associated with a parameter calculation rule used by experts. Similarly, recognizing a behavior method is based on feature/method separation data in a pattern of features in multidimensional space with each feature associated with a method used by experts. The invention may be implemented as a computer process, a computing system or as an article of manufacture such as a computer program product or computer readable media. The computer program product may be a computer storage media readable by a computer system and encoding a computer program of instructions for executing a computer process. The computer program product may also be a propagated signal on a carrier readable by a computing system and encoding a computer program of instructions for executing a computer process. These and various other features as well as advantages, which characterize the present invention, will be apparent from a reading of the following detailed description and a review of the associated drawings. Continue reading about Multidimensional expert behavior emulation system... Full patent description for Multidimensional expert behavior emulation system Brief Patent Description - Full Patent Description - Patent Application Claims Click on the above for other options relating to this Multidimensional expert behavior emulation system patent application. Patent Applications in related categories: 20090299950 - Dynamic categorization of rules in expert systems - Various embodiments include one or more of systems, methods, software, and data structures for dynamic categorization of rules and collections of rules within a rule base, such as a rule base of an expert system. One embodiment provides a computerized method that includes receiving a selection of one or more ... ###  How KEYWORD MONITOR works... a FREE service from FreshPatents1. Sign up (takes 30 seconds). 2. Fill in the keywords to be monitored. 3. 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