Patent Document ID: 7689520
Application ID: 11066514
Patent Flag: 1

Claim One:
1. A document analysis machine learning system using a neural network model to rank data within sets, comprising: at least one processor; a ranking module coupled to the at least one processor and having differentiable parameters, each of the differentiable parameters having a plurality of potential weights, to determine a rank value for an input example based on the weights of differentiable parameters, wherein weights are trained with a training data set that includes pairs of ranked examples; and a cost calculation module coupled to the at least one processor that uses a ranking cost function to determine the weights associated with said differentiable parameters, wherein the cost function is an asymmetric and differentiable function that maps a result of a comparison of a target rank value of a pair of input examples with measured rank values of the pair of input examples to a real number, wherein the target rank value varies for each pair of input examples, and wherein an input example is represented by said differential parameters and the rank value of the input example is generated according to the weights associated with differential variables, wherein the weights are set according to a minimization of the total cost, which comprises the sum of plurality of cost associated with each pair of input examples, wherein the cost of each pair of input example weights are updated according to a difference of two terms, with a first term depending on the first example and a second term depending on the second example, multiplied by the derivative of the cost function; and a final output of the ranking system including a final weight assigned to the differentiable parameters used for ranking documents, wherein ordering a data set based upon the rank value is consistent with a final weight assigned for each of the differentiable parameters selected as a function of the cost function.