Method of and system for analyzing, modeling and valuing elements of a business enterprise
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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.
160 Citations
161 Claims
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1-78. -78. (canceled)
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79. A computer based method of building predictive models from transaction data, comprising:
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aggregating data from a plurality of transaction systems covering a series of time periods for one or more elements of value and one or more aspects of financial performance; transforming said element of value data in accordance with one or more pre-programmed functions; establishing a plurality of input nodes, a plurality of hidden nodes and an output node for a neural network model for each aspect of financial performance; inputting the raw and transformed transaction data into each neural network model using a separate input node for untransformed transaction data and each pre-programmed transformation function by element of value for all time periods in the series; training each neural network model using said inputs until an error function associated with an output value that corresponds to an aspect of financial performance is minimized; and using one or more weights from the trained neural network models to identify a set of raw and transformed transaction data by element of value and output that will be used as an input to an element of value summary for each of one or more predictive models normalizing each of the one or more sets of raw and transformed transaction data by element of value, refining the sets of raw and transformed transaction data by element of value, creating a summary of the refined transaction data set for each element of value, and using the element of value summaries as inputs to a predictive model for each of the one or more aspects of enterprise financial performance where the aspects of financial performance are selected from the group consisting of revenue, expense, capital change, cash flow and combinations thereof, and where the predictive models of aspects of financial performance are useful for completing tasks selected from the group consisting of optimizing a current operation financial performance for a business, predicting an impact of one or more changes to a current operation financial performance, calculating a value for an element of value and combinations thereof. - View Dependent Claims (80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91)
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92. A program storage device readable by a computer, tangibly embodying a program of instructions executable by at least one computer to perform the steps in method, comprising:
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aggregating data from a plurality of transaction systems covering a series of time periods for one or more elements of value and one or more aspects of financial performance; transforming said element of value data in accordance with one or more pre-programmed functions; establishing a plurality of input nodes, a plurality of hidden nodes and an output node for a neural network model for each aspect of financial performance;
inputting the raw and transformed transaction data into each neural network model using a separate input node for untransformed transaction data and each pre-programmed transformation function by element of value for all time periods in the series;training each neural network model using said inputs until an error function associated with an output value that corresponds to an aspect of financial performance is minimized; and using one or more weights from the trained neural network models to identify a set of raw and transformed transaction data by element of value and output that will be used as an element of value summary for use as an input to each of one or more predictive models normalizing each of the one or more sets of raw and transformed transaction data by element of value, refining the sets of raw and transformed transaction data by element of value, creating a summary of the refined transaction data set for each element of value, and using the element of value summaries as inputs to a predictive model for each of the one or more aspects of enterprise financial performance where the aspects of financial performance are selected from the group consisting of revenue, expense, capital change, cash flow and combinations thereof, and where the predictive models of aspects of financial performance are useful for completing tasks selected from the group consisting of optimizing a current operation financial performance for a business, predicting an impact of one or more changes to a current operation financial performance, calculating a value for an element of value and combinations thereof. - View Dependent Claims (93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104)
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105. An apparatus for building predictive models from transaction data, comprising:
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a plurality of transaction systems, means for preparing data from said systems for use in processing for a series of time periods for one or more elements of value and one or more aspects of financial performance; means for transforming said element of value data in accordance with one or more pre-programmed functions; means for establishing a plurality of input nodes, a plurality of hidden nodes and an output node for a neural network model for each aspect of financial performance; means for inputting the raw and transformed transaction data into each neural network model using a separate input node for untransformed transaction data and each pre-programmed transformation function by element of value for all time periods in the series; means for training each neural network model using said inputs until an error function associated with an output value that corresponds to an aspect of financial performance is minimized; and means for using one or more weights from the trained neural network models to identify a set of raw and transformed transaction data by element of value and output that will be used as an element of value summary for use as an input to each of one or more predictive models normalizing each of the one or more sets of raw and transformed transaction data by element of value, refining the sets of raw and transformed transaction data by element of value, creating a summary of the refined transaction data set for each element of value, and using the element of value summaries as inputs to a predictive model for each of the one or more aspects of enterprise financial performance where the aspects of financial performance are selected from the group consisting of revenue, expense, capital change, cash flow and combinations thereof. - View Dependent Claims (106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118)
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119. A data processing method, comprising:
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organizing business transaction data by enterprise into one or more components of value and two or more elements of value where at least one element of value is intangible; determining a relative contribution of each of two or more elements of value to a value of a business by analyzing at least a portion of the data; and reporting the relative contribution of each element of value and the value of the business. - View Dependent Claims (120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133)
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134. A program storage device having sequences of instructions stored therein, which when executed causes the processor in a computer to perform a data processing method, comprising:
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organizing business data by enterprise into one or more components of value and two or more elements of value where at least one element of value is intangible; determining a relative contribution of each of two or more elements of value to a value of the business by analyzing at least a portion of the data; and reporting the relative contribution of each element of value and the value of the business. - View Dependent Claims (135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148)
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149. A financial 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;integrate transaction data from a plurality of enterprise management systems, analyze at least a portion of the integrated data to identify one or more events that drive enterprise value creation and a business context that is associated with said events, and using transaction data associated with said events to develop a computational model of enterprise financial performance. - View Dependent Claims (150, 151, 152, 153, 154, 155)
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156. A program storage device readable by a computer, tangibly embodying a program of instructions executable by at least one computer to perform the steps in a neural network development method, comprising:
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a) preparing a plurality of input data and ouput data for a population for use in neural network processing, b) defining a structure for a neural network comprising a plurality of input nodes, a plurality of hidden nodes, an output node, a connection between each input node and each hidden node and a connection between each hidden node and the output node, c) assigning a random weight value to the connections between each node and a target fitness level d) creating a plurality of chromosomes that encode the weights between each node, e) generating a successor set of weight values from said initial set of weight values by evolving the chromosomes with a genetic algorithm, the input data and the output data until the target fitness level is achieved, f) implementing said neural network with the set of weight values that achieved the target fitness level where the population being analyzed is partitioned into a plurality of subpopulations, with each subpopulation being processed by a genetic algorithm independently of the others and where a selective crossover produces a chromosome exchange between the subpopulations, and where the selective crossover occurs between two or more successive generations. - View Dependent Claims (157, 158)
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159. A computer implemented neural network modeling method, comprising:
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a) preparing a plurality of input data and ouput data for a population for use in neural network processing, b) defining a structure for a neural network comprising a plurality of input nodes, a plurality of hidden nodes, an output node, a connection between each input node and each hidden node and a connection between each hidden node and the output node, c) assigning a random weight value to the connections between each node and a target fitness level d) creating a plurality of chromosomes that encode the weights between each node, e) generating a successor set of weight values from said initial set of weight values by evolving the chromosomes with a genetic algorithm, the input data and the output data until the target fitness level is achieved, f) implementing said neural network with the set of weight values that achieved the target fitness level where the population being analyzed is partitioned into a plurality of subpopulations, with each subpopulation being processed by a genetic algorithm independently of the others and where a selective crossover produces a chromosome exchange between the subpopulations, and where the selective crossover occurs between two or more successive generations. - View Dependent Claims (160, 161)
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Specification