PATENT CLAIM ANALYSIS

Application Number: 16161339
Application Type: Utility
Filing Date: 2018-10
Publication Date: 2019-02
Patent Classification: ["707", "756000"]

Abstract:
A system provides analysis of distributed data and grouping of variables in support of analytics. Policy parameter values that define thresholds are received. A first computation of a cardinality value and of a number of observations having a non-missing value is requested for each variable of a plurality of variables included in the distributed data by each worker computing device. A number of observation vectors having the non-missing value and the cardinality value are computed by each worker computing device for each variable in response to the first computation request. Each respective worker computing device computes the number of observation vectors having the non-missing value and the cardinality value from a subset of the input dataset distributed to the respective worker computing device by reading each observation vector from the subset once. Each variable is assigned a category based on a comparison between computed values and the policy parameter values.

Claim (Index 28):
A method of providing analysis of data and grouping of variables in support of analytics, the method comprising:\n receiving a first policy parameter value that defines a cardinality ratio threshold for identifying the variable as a nominal variable type; receiving a second policy parameter value that defines a number of unique values threshold for identifying a variable as a high-cardinality variable type; receiving a third policy parameter value that defines a threshold for a first categorization value; requesting, by a computing device, a first computation of a cardinality value and of a number of observations having a non-missing value for each variable of a plurality of variables included in an input dataset by each worker computing device of a plurality of worker computing devices, wherein the input dataset is distributed across the plurality of worker computing devices, wherein the input dataset includes a plurality of observation vectors, wherein each observation vector of the plurality of observation vectors includes a plurality of values, wherein each value of the plurality of values is associated with a different variable to define the plurality of variables, wherein the cardinality value of a variable indicates a number of unique values associated with the variable; computing, by each worker computing device of the plurality of worker computing devices, the number of observation vectors having the non-missing value and the cardinality value for each variable of the plurality of variables in response to the first computation request, wherein each respective worker computing device computes the number of observation vectors having the non-missing value and the cardinality value from a subset of the input dataset distributed to the respective worker computing device by reading each observation vector from the subset once; combining, by the computing device, for each variable of the plurality of variables, the number of observation vectors having the non-missing value computed by each worker computing device of the plurality of worker computing devices; combining, by the computing device, for each variable of the plurality of variables, the cardinality value computed by each worker computing device of the plurality of worker computing devices; computing, by the computing device, a missing rate value for each variable of the plurality of variables using the combined number of observation vectors having the non-missing value and a total number of observation vectors included in the input dataset for each variable of the plurality of variables; computing, by the computing device, a cardinality ratio value for each variable of the plurality of variables using the combined cardinality value and the combined number of observation vectors having the non-missing value computed for each variable of the plurality of variables; for each variable of the plurality of variables,\n comparing, by the computing device, the computed cardinality ratio value of a respective variable to the received first policy parameter value; and \n identifying, by the computing device, the respective variable as the nominal variable type or as an interval variable type based on the comparison between the computed cardinality ratio value and the received first policy parameter value; \n for each variable of the plurality of variables identified as the nominal variable type,\n comparing, by the computing device, the combined cardinality value of the respective variable to the received second policy parameter value; and \n identifying, by the computing device, the the respective variable as a high-cardinality nominal variable type or as a non-high-cardinality nominal variable type based on the comparison between the combined cardinality value and the received second policy parameter value; \n comparing, by the computing device, the computed missing rate value of each variable of the plurality of variables to the received third policy parameter value; assigning, by the computing device, each variable of the plurality of variables identified as the high-cardinality nominal variable type to a first category or to a second category based on the comparison between the computed missing rate value and the received third policy parameter value; assigning, by the computing device, each variable of the plurality of variables identified as the non-high-cardinality nominal variable type to a third category or to a fourth category based on the comparison between the computed missing rate value and the received third policy parameter value; assigning, by the computing device, each variable of the plurality of variables identified as the interval variable type to a fifth category or to a sixth category based on the comparison between the computed missing rate value and the received third policy parameter value; and outputting, by the computing device, an assigned category for each variable of the plurality of variables.

Metadata:
- Claim Count in Document: 80.0
- Percentile: 97.0
- Lexical Diversity: 2.36364
- Patent Class: 707.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['16033851', '15833641', '14924893', '15583067', '15185277']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2185390467097774
- 35 USC 102 Novelty (BERT): 0.5275847086992942
- Combined Prediction Score: 0.2494436129087291
- Mean Citation Score: 265.361368
- Max Citation Score: 367.88138
- Similarity Product: 258.8813526498151

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