Method of and system for analyzing, modeling and valuing elements of a business enterprise
First Claim
1. A computer-implemented network model method, comprising:
- receiving first input data into an initial network model to develop an initial model configuration;
receiving an initial input data set from said initial model configuration and a second input data as inputs into a second model stage to develop and output an improvement to the initial input data set by evolving a plurality of network models using a plurality of genetic algorithms that exchange a plurality of data between successive generations of two or more independent subpopulations, said second input data comprising one of said first input data, data not included in said first input data, and a combination thereof;
receiving said second model stage output as an input into third, induction stage in order to refine and summarize the data selection in the second stage model output and output a refined input data set, andreceiving said induction stage output as an input into a fourth model stage to develop and output a final network model by training a network model using the refined input data set where all input data represents a physical object or substance, andwhere said final network model supports a regression analysis.
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Abstract
An automated system (100) and method for analyzing, modeling and valuing elements of a business enterprise on a specified valuation date. The performance of the elements are analyzed using search algorithms and induction algorithms to determine the value drivers associated with each element. The induction algorithms are also used to create composite variables that relate element performance to enterprise revenue, expenses and changes in capital. Predictive models are then used to determine the correlation between the value drivers and the enterprise revenue, expenses and changes in capital. The correlation percentages for each value driver are then multiplied by capitalized value of future revenue, expenses and changes in capital, the resulting numbers for each value driver associated with each element are then added together to calculate a value for each element.
124 Citations
84 Claims
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1. A computer-implemented network model method, comprising:
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receiving first input data into an initial network model to develop an initial model configuration; receiving an initial input data set from said initial model configuration and a second input data as inputs into a second model stage to develop and output an improvement to the initial input data set by evolving a plurality of network models using a plurality of genetic algorithms that exchange a plurality of data between successive generations of two or more independent subpopulations, said second input data comprising one of said first input data, data not included in said first input data, and a combination thereof; receiving said second model stage output as an input into third, induction stage in order to refine and summarize the data selection in the second stage model output and output a refined input data set, and receiving said induction stage output as an input into a fourth model stage to develop and output a final network model by training a network model using the refined input data set where all input data represents a physical object or substance, and where said final network model supports a regression analysis. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 63, 64, 65, 66)
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13. A machine-readable medium tangibly embodying a program of machine-readable instructions executable by a digital processing apparatus to perform a network model method, comprising:
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receiving first input data into an initial network model to develop an initial model configuration; receiving an initial input data set from said initial model configuration and a second input data as inputs into a second model stage to develop and output an improvement to the initial input data set by evolving a plurality of network model using a plurality of genetic algorithms that exchange a plurality of data between two or more independent subpopulations, said second input data comprising one of said first input data, data not included in said first input data, and a combination thereof; receiving said second model stage output as an input into third, induction stage in order to refine and summarize the data selection in the second stage model output and output a refined input data set, and receiving said induction stage output as an input into a fourth model stage to develop and output a final network model by training a network model where said final network model supports a regression analysis. - View Dependent Claims (14, 15, 16, 17, 18)
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35. A machine-readable medium tangibly embodying a program of machine-readable instructions executable by a digital processing apparatus to perform a neural network model method, comprising:
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receiving first input data into an initial neural network model to develop an initial model configuration; receiving an initial input data set from said initial model configuration and a second input data as inputs into a second model stage to develop and output an improvement to said initial data set by evolving a plurality of network model using a plurality of genetic algorithms that exchange a plurality of data between two or more different generations of two or more subpopulations during evolution, said second input data comprising one of said first input data, data not included in said first input data, and a combination thereof;
receiving said second model stage output as an input into third, induction stage in order to refine and summarize the data selection in the second stage model output and output a refined input data set, andreceiving said induction stage output as an input into a fourth model stage to develop and output a final neural network model by training a predictive network model using the refined input data set where said final neural network model supports a regression analysis. - View Dependent Claims (36, 37, 38, 39, 40)
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57. A machine-readable medium tangibly embodying a program of machine-readable instructions executable by a digital processing apparatus to perform a neural network model method, comprising:
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receiving first input data into an initial neural network model to develop an initial model configuration; receiving an initial input data set from said initial model configuration and a second input data as inputs into a second, model stage to develop and output an improvement to said initial data set by evolving a plurality of network model using a plurality of genetic algorithms that exchange a plurality of data between two or more different generations of two or more subpopulations during evolution, said second input data comprising one of said first input data, data not included in said first input data, and a combination thereof; receiving said second model stage output as an input into third, induction stage in order to refine and summarize the data selection in the second stage model output and output a refined input data set, and receiving said induction stage output as an input into a fourth model stage to develop and output a final neural network model by training a predictive network model where said final neural network model supports a regression analysis. - View Dependent Claims (58, 59, 60, 61, 62)
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67. A network model system, comprising:
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networked computers each with a processor having circuitry to execute instructions;
a storage device available to each processor with sequences of instructions stored therein,which when executed cause the processors to; receive first input data into an initial network model to develop an initial model configuration; receive an initial input data set from said initial model configuration and a second input data as inputs into a second model stage to develop and output an improvement to said initial data set by evolving a plurality of network model using a plurality of genetic algorithms that exchange a plurality of data between two or more different generations of two or more subpopulations during evolution, said second input data comprising one of said first input data, data not included in said first input data, and a combination thereof; receiving said second model stage output as an input into third, induction stage in order to refine and summarize the data selection in the second stage model output and output a refined input data set, and receive said induction stage output as an input into a fourth model stage to develop and output a final neural network model by training a predictive network model using a backpropagation algorithm where said final network model supports a regression analysis. - View Dependent Claims (68, 69, 70, 71, 72)
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73. A neural network model system, comprising:
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a computer with a processor having circuitry to execute instructions;
a storage device available to said processor with sequences of instructions stored therein, which when executed cause the processor to;receive first input data into an initial neural network model to develop an initial model configuration; receive an initial input data set from said initial model configuration and a second input data as inputs into a second model stage to develop and output an improvement to said initial data set by evolving a plurality of network model using a plurality of genetic algorithms that exchange a plurality of data between two or more different generations of two or more subpopulations during evolution, said second input data comprising one of said first input data, data not included in said first input data, and a combination thereof; receiving said second model stage output as an input into third, induction stage in order to refine and summarize the data selection in the second stage model output and output a refined input data set, and receive said induction stage output as an input into a fourth model stage to develop and output a final neural network model by training a predictive neural network model using a backpropagation algorithm where said final neural network model supports a regression analysis. - View Dependent Claims (74, 75, 76, 77, 78)
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79. An advanced network model system, comprising:
- networked computers each with a processor having circuitry to execute instructions;
a storage device available to each processor with sequences of instructions stored therein, which when executed cause the processors to;receive first input data into an initial neural network model to develop an initial model configuration; receive an initial input data set from said initial model configuration and a second input data as inputs into a second model stage to develop and output an improvement to said initial data set by evolving a plurality of network model using a plurality of genetic algorithms that exchange a plurality of data between two or more different generations of two or more subpopulations during evolution, said second input data comprising one of said first input data, data not included in said first input data, and a combination thereof; receiving said second model stage output as an input into third, induction stage in order to refine and summarize the data selection in the second stage model output and output a refined input data set, and receive said induction stage output as an input into a fourth model stage to develop and output a final neural network model by training a predictive network model using the refined input data set where said final neural network model supports a regression analysis. - View Dependent Claims (80, 81, 82, 83, 84)
- networked computers each with a processor having circuitry to execute instructions;
Specification