Adaptive system and method for predicting response times in a service environment
First Claim
1. An apparatus for predicting a prospective response time of a service provider to a current service request, the apparatus comprising:
- a memory for storing historical data, including previous response times to corresponding previous service requests;
a neural network having a plurality of processing elements, each processing element including memory means for storing a respective weight, said neural network trained by a plurality of test data to modify the weights, said neural network being responsive to a plurality of inputs, including the current month of the current service request and historical data input from the memory for a plurality of months of the previous year for generating a plurality of tentative response time predictions; and
means for classifying the current month of the current service request as similar according to predetermined rules to a first month of the plurality of months of the previous year for generating the prospective response time from a weighting of the plurality of tentative response time predictions in a mapping determined from the current month and the first month.
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Abstract
A hybrid fuzzy logic/neural network prediction system and method is disclosed for predicting response times to service requests to a service provider. Data from a historical database of records including customer requests and weather information are input to the hybrid system. The data is filtered to reject faulty data entries and data not necessarily useful for predicting response times to service requests such as customer comments are eliminated. A backpropagation neural network operating in a supervised learning mode is employed to decrease the effects of the inherent system nonlinearities. The prediction error from the neural network is trained to make predictions within a predetermined error limit. The neural network generates a prediction configuration; i.e. a set of neural network characteristics, for every record per geographical area, time frame, and month. A fuzzy logic classifier is used for further data reliability. A fuzzy logic classifier relying upon the Fuzzy Cell Space Predictor (FCSP) method is employed to improve predicted response times from year to year. The fuzzy logic classifier supervises the overall identification scheme and, for every record, computes a prediction configuration for its corresponding month in the preceding year. The fuzzy logic classifier then computes a prediction estimate for its neighboring months in the preceding year and computes the prediction estimate for the next time frame (i.e. morning and evening). The Center of Gravity method is used to smooth the different prediction estimates to obtain a final predicted response time.
119 Citations
9 Claims
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1. An apparatus for predicting a prospective response time of a service provider to a current service request, the apparatus comprising:
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a memory for storing historical data, including previous response times to corresponding previous service requests; a neural network having a plurality of processing elements, each processing element including memory means for storing a respective weight, said neural network trained by a plurality of test data to modify the weights, said neural network being responsive to a plurality of inputs, including the current month of the current service request and historical data input from the memory for a plurality of months of the previous year for generating a plurality of tentative response time predictions; and means for classifying the current month of the current service request as similar according to predetermined rules to a first month of the plurality of months of the previous year for generating the prospective response time from a weighting of the plurality of tentative response time predictions in a mapping determined from the current month and the first month. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method using a neural network for predicting a prospective response time of a service provider to a current service request, the method comprising the steps of:
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storing historical data in a memory, the historical data including previous response times to corresponding previous service requests; training the neural network using the historical data to learn a response time relationship between the previous service requests and the previous response times, the response time relationship being a mapping of the previous service requests to corresponding previous response times; generating a plurality of weights for the neural network from the learned response time relationship; generating a plurality of tentative response times from the learned response time relationship by applying a current service request and the historical data from a plurality of months of the previous year to said neural network configured with said plurality of weights; classifying a current month as similar according to predetermined rules to a first month of the plurality of months; generating the prospective response time from the tentative response time corresponding to the first month; storing the generated prospective response time in the memory; and outputting the prospective response time from the memory. - View Dependent Claims (9)
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Specification