PATENT CLAIM ANALYSIS

Application Number: 16128667
Application Type: Utility
Filing Date: 2018-09
Publication Date: 2019-03
Patent Classification: ["700", "291000"]

Abstract:
An apparatus includes a processor to: assign each value of each set of values of an initial supply meter data and of an initial load meter data to one of multiple buckets based on weather conditions and/or time and date; for each bucket, generate upper and lower bounds of power provision and power consumption values, and use the upper and lower bounds to identify outlier values assigned to the bucket; for each set of values within the initial supply meter data and within the initial load meter data, generate a naive model from the non-outlier values, and use interpolation and the naive model to fill in gaps, thereby generating cleansed supply meter data and cleansed load meter data; and store the cleansed supply meter data and cleansed load meter data together as merged meter data for use in making predictions.

Claim (Index 21):
A computer-implemented method comprising:\n retrieving, by a processor, and from a storage, initial supply meter data, wherein:\n the initial supply meter data comprises, for each supply meter of multiple supply meters of a power grid, a set of power provision values for a corresponding supply asset at a corresponding supply location; and \n each power supply value is representative of a measure of power provision taken at a time and date by a supply meter during a predetermined historical time period; \n retrieving, by the processor, and from the storage, initial load meter data, wherein:\n the initial load meter data comprises, for each load meter of multiple load meters of the power grid, a set of power consumption values for a corresponding endpoint asset at a corresponding endpoint location; and \n each power consumption value is representative of a measure of power consumption taken at a time and date by a load meter during the predetermined historical time period; \n assigning each power provision value of the initial supply meter data to one of multiple buckets based on at least a weather condition at the corresponding supply location at the corresponding time and date; assigning each power consumption value of the initial load meter data to one of the multiple buckets based on at least a weather condition at the corresponding endpoint location at the corresponding time and date; performing, for each bucket, further operations comprising:\n deriving, by the processor, upper and lower bounds of power provision from the power provision values assigned to the bucket; \n using the upper and lower bounds of power provision to identify outlier power provision values among the power provision values assigned to the bucket; \n deriving, by the processor, upper and lower bounds of power consumption from the power consumption values assigned to the bucket; and \n use the upper and lower bounds of power consumption to identify outlier power consumption values among the power consumption values assigned to the bucket; \n performing, for each set of power provision values within the initial supply meter data, further operations to generate cleansed supply meter data from the initial supply meter data in which each set of power provision values defines a complete time series, the further operations comprising:\n generating, by the processor, a naive power provision model from the power provision values of the set of power provision values that are not identified as outliers; \n identifying missing power provision values, wherein each power provision value identified as missing or an outlier in the set of power provision values defines at least part of a gap that prevents the set of power provision values from defining a complete time series; and \n employing, by the processor, and for each missing or outlier power provision value in each gap that exceeds a predetermined threshold quantity of power provision values, a naive power provision model of the corresponding bucket to generate a power provision value to fill the gap; \n performing, for each set of power consumption values within the initial load meter data, further operations to generate cleansed load meter data from the initial load meter data in which each set of power consumption values defines a complete time series, the further operations comprising:\n generating, by the processor, a naive power consumption model from the power consumption values of the set of power consumption values that are not identified as outliers; \n identifying missing power consumption values, wherein each power consumption value identified as missing or an outlier in the set of power consumption values defines at least part of a gap that prevents the set of power consumption values from defining a complete time series; and \n employing, by the processor, and for each missing or outlier power consumption value in each gap that exceeds a predetermined threshold quantity of power consumption values, a naive power consumption model of one of the corresponding bucket to generate a power consumption value to fill the gap; \n merging the cleansed load meter data and the cleansed supply meter data into merged meter data; and storing, by the processor, the merged meter data within the storage to enable generation of power provision models and power consumption models of the power grid.

Metadata:
- Claim Count in Document: 80.0
- Percentile: 97.0
- Lexical Diversity: 2.30769
- Patent Class: 700.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15360335', '15473240', '13774994', '14518412', '15189173']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3589040613474574
- 35 USC 102 Novelty (BERT): 0.4791505248541091
- Combined Prediction Score: 0.3709287076981226
- Mean Citation Score: 180.59717800000004
- Max Citation Score: 192.72542
- Similarity Product: 143.19169607774617

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 0
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test