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 28):
The computer-implemented method of  claim 21 , comprising:\n retrieving, by the processor, and from the merged meter data, a first portion of each time series of power provision values and of each time series of power consumption values from for a predetermined training time period within the historical time period for use as training data; retrieving, by the processor, and from the merged meter data, a second portion of each time series of power provision values and of each time series of power consumption values from for a predetermined testing time period within the historical time period for use as testing data; retrieving, by the processor, and from at least one of cleansed weather data, calendar data or economic data, a first subset of data values for the training time period for use as further training data and a second subset of data values for the testing time period for use as further testing data, wherein:\n the cleansed weather data comprises data values indicative of weather conditions at multiple weather station locations within a geographic area covered by the power grid throughout the historical time period; \n the calendar data comprises at least one of indications of correlations between dates within the historical time period to days of a week or months of a year, or indications of holidays within the historical time period that are applicable to at least a portion of the geographic area covered by the grid; and \n the economic data comprises data values indicative of economic conditions within the geographic area covered by the power grid throughout the historical time period; \n performing, by the processor, and for each supply meter of the multiple supply meters, further operations comprising:\n using the first portion of the corresponding time series within the training data, along with the further training data, in multiple performances of linear regression to generate a corresponding set of candidate models of power provision by the corresponding supply asset, wherein the corresponding set of candidate models of power provision are of a preselected set of model types; \n using the second portion of the corresponding time series within the testing data, along with the further testing data, to derive a degree of error for each candidate model of the corresponding set of candidate models of power provision; \n analyzing the degree of error of each candidate model of the corresponding set of candidate models of power provision to identify a corresponding champion model of power provision; and \n storing an indication of the corresponding champion model of power provision and an indication of variables to be used as inputs thereto within the storage to enable use of the corresponding champion model of power provision in a prediction of power provision by the corresponding supply asset; and \n performing, by the processor, and for each load meter of the multiple load meters, further operations comprising:\n using the first portion of the corresponding time series within the training data, along with the further training data, in multiple performances of linear regression to generate a corresponding set of candidate models of power consumption by the corresponding endpoint asset, wherein the corresponding set of candidate models of power consumption are of the preselected set of model types; \n using the second portion of the corresponding time series within the testing data, along with the further testing data, to derive a degree of error for each candidate model of the corresponding set of candidate models of power consumption; \n analyzing the degree of error of each candidate model of the corresponding set of candidate models of power consumption to identify a corresponding champion model of power consumption; and \n storing an indication of the corresponding champion model of power consumption and an indication of variables to be used as inputs thereto within the storage to enable use of the corresponding champion model of power consumption in a prediction of power consumption by the corresponding endpoint asset.

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): 0.3542187549796532
- 35 USC 102 Novelty (BERT): 0.4992412122153055
- Combined Prediction Score: 0.3687210007032184
- Mean Citation Score: 180.59717800000004
- Max Citation Score: 192.72542
- Similarity Product: 115.360824276495

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