PATENT CLAIM ANALYSIS

Application Number: 16445593
Application Type: Utility
Filing Date: 2019-06
Publication Date: 2019-10
Patent Classification: ["706", "052000"]

Abstract:
A computing device computes a weight matrix to compute a predicted value. For each of a plurality of related tasks, an augmented observation matrix, a plug-in autocovariance matrix, and a plug-in covariance vector are computed. A weight matrix used to predict the characteristic for each of a plurality of variables and each of a plurality of related tasks is computed. (a) and (b) are repeated with the computed updated weight matrix as the computed weight matrix until a convergence criterion is satisfied: (a) a gradient descent matrix is computed using the computed plug-in autocovariance matrix, the computed plug-in covariance vector, the computed weight matrix, and a predefined relationship matrix, wherein the predefined relationship matrix defines a relationship between the plurality of related tasks, and (b) an updated weight matrix is computed using the computed gradient descent matrix.

Claim (Index 1):
A non-transitory computer-readable medium having stored thereon computer-readable instructions that when executed by a computing device cause the computing device to:\n for each of a plurality of related tasks,\n compute an augmented observation matrix using an observation matrix and a predefined probability value that a value is missing in the observation matrix, wherein the observation matrix includes a plurality of observation vectors, wherein each observation vector includes a plurality of values, wherein each value of the plurality of values is associated with a variable to define a plurality of variables; \n compute a plug-in autocovariance matrix using the computed augmented observation matrix and a noise value; and \n compute a plug-in covariance vector using a target vector, the computed augmented observation matrix, and the noise value, wherein the target vector includes a target value associated with each of the plurality of observation vectors, the target value is an indicator of a characteristic of the associated observation vector; \n compute a weight matrix used to predict the target value for each of the plurality of variables and each of the plurality of related tasks; (a) compute a gradient descent matrix using the computed plug-in autocovariance matrix, the computed plug-in covariance vector, the computed weight matrix, and a predefined relationship matrix, wherein the predefined relationship matrix defines a relationship between the plurality of related tasks, wherein the gradient descent matrix is computed using {tilde over (W)}=\u0174 t-1 \u2212\u03b7({tilde over (\u2207)}+\u03bb\u0174 t-1 RR T ), where \u0174 t-1  is the computed weight matrix, where \u03b7 is a predefined step size, \u03bb is a predefined sparsity penalization weight value, R is the predefined relationship matrix, and {tilde over (\u2207)}=\u0393 i \u0174 i t-1 \u2212\u03b3 i  for i=1, . . . , K, where K is a number of the of the plurality of related tasks, \u0393 i  is the plug-in autocovariance matrix, and \u03b3 i  is the plug-in covariance vector; (b) compute an updated weight matrix using the computed gradient descent matrix; repeat (a) and (b) with the computed updated weight matrix as the computed weight matrix until a convergence criterion is satisfied; and when the convergence criterion is satisfied,\n read a new observation vector from a scoring dataset; \n compute a predicted target value for each task of the plurality of related tasks using the computed updated weight matrix and the read new observation vector; and \n output the computed predicted target value for each task.

Metadata:
- Claim Count in Document: 62.0
- Percentile: 100.0
- Lexical Diversity: 2.82143
- Patent Class: 706.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15833641', '16162794', '16400157', '16108293', '16221937']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3509669876358754
- 35 USC 102 Novelty (BERT): 0.6042120447054223
- Combined Prediction Score: 0.3762914933428301
- Mean Citation Score: 279.08156799999995
- Max Citation Score: 509.6548
- Similarity Product: 493.198541806984

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test