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 19):
The computer-program product of  claim 18 , wherein the processor is caused to perform operations comprising:\n retrieve, from the merged meter data, a third portion of each time series of power provision values and of each time series of power consumption values from for a predetermined short range training time period within the historical time period for use as short range training data; retrieve, from the merged meter data, a fourth portion of each time series of power provision values and of each time series of power consumption values from for a predetermined long range training time period within the historical time period for use as long range training data; retrieve, from at least the cleansed weather data, a third subset of data values for the short range training time period for use as further short range training data; retrieve, from at least the economic data, a fourth subset of data values for the long range training time period for use as further long range training data; for each supply meter of the multiple supply meters, the processor is caused to:\n use the third portion of the corresponding time series within the short range training data, along with the further short range training data to generate a corresponding second-stage short range model of power provision by the corresponding supply asset to address overfitting of the corresponding champion model of power provision for a short range period of time; \n use the fourth portion of the corresponding time series within the long range training data, along with the further long range training data to generate a corresponding second-stage long range model of power provision by the corresponding supply asset to address overfitting of the corresponding champion model of power provision for a long range period of time; and \n store indications of the corresponding second-stage short range model of power provision and corresponding second-stage long range model of power provision to enable use of the corresponding champion model of power provision in a short range or long range prediction of power provision by the corresponding supply asset; and \n for each load meter of the multiple load meters, the processor is caused to:\n use the third portion of the corresponding time series within the short range training data, along with the further short range training data to generate a corresponding second-stage short range model of power consumption by the corresponding endpoint asset to address overfitting of the corresponding champion model of power consumption for a short range period of time; \n use the fourth portion of the corresponding time series within the long range training data, along with the further long range training data to generate a corresponding second-stage long range model of power consumption by the corresponding endpoint asset to address overfitting of the corresponding champion model of power consumption for a long range period of time; and \n store indications of the corresponding second-stage short range model of power consumption and corresponding second-stage long range model of power consumption to enable use of the corresponding champion model of power consumption in a short range or long range 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): 0.3547197423559418
- 35 USC 102 Novelty (BERT): 0.4970859887040519
- Combined Prediction Score: 0.3689563669907528
- Mean Citation Score: 180.59717800000004
- Max Citation Score: 192.72542
- Similarity Product: 118.34463532083988

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