Disclosed herein are system, method, and computer program product embodiments for performing a regression analysis on lawfully collected personal data records. The analysis enables discovery of individuals likely to perform certain actions based on their personal data records and the personal data records and actions of others. The disclosed system, method, and computer program product may process vast quantities of data, including personal data records with thousands of categories and lawfully stored databases with millions of personal data records. Through the regression analysis, the disclosed system, method, and computer program product learn the most relevant categories for predicting an individual's actions based on input data provided by a user. The analysis then analyzes the categories of personal data records stored in a lawfully stored database to predict actions of individuals associated with those records and outputs results to the user.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system, comprising: a memory; and one or more processors electronically coupled to the memory and configured to: access a training set stored in the memory, the training set having a plurality of personal data training records and a plurality of categories associated with each personal data training record, wherein the plurality of categories comprises an action taken by an individual corresponding to the associated personal data training record; determine a subset of the plurality of categories based on at least a first plurality of personal data training records in the training set; determine a prediction function that outputs an outcome score based on values of the subset of the plurality of categories; test the accuracy of the prediction function based at least on a second plurality of personal data training records in the training set different from the first plurality of personal data training records in the training set; access a data set stored in memory, the data set having a number of personal data records greater than the number of personal data training records in the training set, the data set having the plurality of categories associated with each personal data record; and process a subset of the personal data records from the data set based on the prediction function to determine an outcome score for each personal data record in the subset of personal data records. 2. The system of claim 1, wherein determining the subset of the plurality of categories comprises determining a weight for each category. 3. The system of claim 2, wherein determining a weight for each category comprises: initializing a coefficient; and computing a weight vector that optimizes a function based on the subset of the plurality of categories, the function including the coefficient multiplied by the sum of the absolute values of each element in the weight vector; wherein each element in the weight vector corresponds to a weight for a category. 4. The system of claim 3, wherein the one or more processors are further configured to iterate the steps of determining a prediction function and testing the accuracy of the prediction function using successively smaller coefficients until the difference between successive accuracies is less than a threshold. 5. The system of claim 3, wherein the size of the subset of the plurality of categories is related to the magnitude of the coefficient. 6. The system of claim 1, wherein the prediction function substantially satisfies the equation P = e θ T x 1 + e θ T x ; wherein P is the prediction function, wherein e is Euler's number, wherein θ is a column vector of parameters, and wherein x is a column vector of values corresponding to categories of a personal data record. 7. The system of claim 6, wherein determining the subset of the plurality of categories comprises determining a value for θ that substantially minimizes the equation Y(θ,α1)=−ΣmεMAm log Pm(θ)+(1−Am)log(1−Pm(θ))−α1(α2∥θ∥1+½(1−α2)∥θ∥22) wherein M is the first plurality of personal data training records in the training set, wherein Am is the action taken by an individual corresponding to the personal data record mεM, wherein Pm(θ)=eθTxm/(1+eθTxm) is the outcome score for personal data record m, wherein α1 is a coefficient, and wherein α2 is a constant coefficient. 8. A computer implemented method, comprising: accessing a training set stored in memory, the training set having a plurality of personal data training records and a plurality of categories associated with each personal data training record, and the plurality of categories comprises an action taken by an individual corresponding to the associated personal data training record; determining a subset of the plurality of categories based on the action of at least a first personal data training record in the training set; determining a prediction function that outputs an outcome score based on the subset of the plurality of categories; testing the accuracy of the prediction function based on at least a second personal data training record in the training set different from the first personal data training record in the training set; accessing a data set stored in memory, the data set having a number of personal data records greater than the number of personal data training records in the training set, the data set having a plurality of categories associated with each personal data record; and processing a subset of the personal data records from the data set based on the subset of categories to determine an outcome score for each personal data record in the subset of personal data records. 9. The method of claim 8, further comprising: receiving an action taken by an individual corresponding to at least one personal data record in the subset of personal data records from the data set; replacing the outcome score for the at least one personal data record with the received outcome; and moving the at least one personal data record from the data set to the training set. 10. The method of claim 8, wherein determining the subset of the plurality of categories comprises determining a weight for each category. 11. The method of claim 10, wherein determining a weight for each category comprises: initializing a coefficient; and computing a weight vector that optimizes a function based on the subset of the plurality of categories, the function including the coefficient multiplied by the sum of the absolute values of each element in the weight vector; wherein each element in the weight vector corresponds to a weight for a category. 12. The method of claim 11, wherein the one or more processors are further configured to iterate the steps of determining a prediction function and testing the accuracy of the prediction function using successively smaller coefficients until the difference between successive accuracies is less than a threshold. 13. The method of claim 11, wherein the size of the subset of the plurality of categories is related to the magnitude of the coefficient. 14. The method of claim 8, wherein the prediction function substantially satisfies the equation P = e θ T x 1 + e θ T x ; wherein P is the prediction function, wherein e is Euler's number, wherein θ is a column vector of parameters, and wherein x is a column vector of values corresponding to categories of a personal data record. 15. A tangible computer-readable device having instructions stored thereon that, when executed by at least one computing device, causes the at least one computing device to perform operations comprising: accessing a training set stored in memory, the training set having a plurality of personal data training records and a plurality of categories associated with each personal data training record, and the plurality of categories comprises an action taken by an individual corresponding to the associated personal data training record; determining a subset of the plurality of categories based on the outcome of at least a first personal data training record in the training set; determining a prediction function based on the subset of the plurality of categories; testing the accuracy of the prediction function based on at least a second personal data training record in the training set different from the first personal data training record in the training set; accessing a data set stored in memory, the data set having a number of personal data records greater than the number of personal data training records in the training set, the data set having a plurality of categories associated with each personal data record; and processing a subset of the personal data records from the data set based on the subset of categories to determine an outcome score for each personal data record in the subset of personal data records. 16. The computer-readable device of claim 15, wherein the operation of determining the subset of the plurality of categories comprises determining a weight for each category. 17. The computer-readable device of claim 16, wherein the operation of determining a weight for each category comprises: initializing a coefficient; and computing a weight vector that optimizes a function based on the subset of the plurality of categories, the function including the coefficient multiplied by the sum of the absolute values of each element in the weight vector; wherein each element in the weight vector corresponds to a weight for a category. 18. The computer-readable device of claim 17, wherein the one or more processors are further configured to iterate the steps of determining a prediction function and testing the accuracy of the prediction function using successively smaller coefficients until the difference between successive accuracies is less than a threshold. 19. The computer-readable device of claim 17, wherein the size of the subset of the plurality of categories is related to the magnitude of the coefficient. 20. The computer-readable device of claim 15, wherein the prediction function substantially satisfies the equation P = e θ T x 1 + e θ T x ; wherein P is the prediction function, wherein e is Euler's number, wherein θ is a column vector of parameters, and wherein x is a column vector of values corresponding to categories of a personal data record.