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 20):
The computer-program product of  claim 19 , wherein the processor is caused to perform operations comprising:\n receive a request from a requesting device for a prediction that covers a specified portion of the power grid for a specified prediction time period; retrieve asset data comprising at least one of:\n identifiers of supply assets and endpoint assets of the power grid; \n indications of locations of supply assets and endpoint assets within a hierarchy of electrical pathways of the power grid; \n indications of geographic locations of supply locations of supply assets within a geographic area covered by the power grid; and \n indications of geographic locations of endpoint locations of endpoint assets within the geographic area covered by the power grid; \n use the asset data to identify one or more supply assets and one or more endpoint assets within the specified portion of the power grid; retrieve, from the merged meter data, a portion of the time series of power provision values for each supply meter that corresponds to an asset of the identified one or more supply assets; for each supply meter that corresponds to a supply asset of the identified one or more supply assets, use the corresponding champion model of power provision with the retrieved portion of the corresponding time series of power provision values to generate a corresponding base prediction of power provision; retrieve, from the merged meter data, a portion of the time series of power consumption values for each load meter that corresponds to an endpoint asset of the identified one or more endpoint assets; for each load meter that corresponds to an endpoint asset of the identified one or more endpoint assets, use the corresponding champion model of power consumption with the retrieved portion of the corresponding time series of power consumption values to generate a corresponding base prediction of power consumption; analyze the specified prediction time period to determine whether the requested prediction comprises a short range prediction, a mid range prediction or a long range prediction; in response to a determination that the requested prediction comprises a short range prediction, the processor is caused to:\n for each supply meter that corresponds to a supply asset of the identified one or more supply assets:\n use the corresponding second-stage short range model of power provision with the retrieved portion of the corresponding time series of power provision values to generate a corresponding short range refinement of the corresponding base prediction of power provision; \n apply the corresponding short range refinement to the corresponding base prediction of power provision; and \n provide the corresponding base prediction of power provision, after refinement, in response to the request; and \n \n for each load meter that corresponds to an endpoint asset of the identified one or more load assets:\n use the corresponding second-stage short range model of power consumption with the retrieved portion of the corresponding time series of power consumption values to generate a corresponding short range refinement of the corresponding base prediction of power consumption; \n apply the corresponding short range refinement to the corresponding base prediction of power consumption; and \n provide the corresponding base prediction of power consumption, after refinement, in response to the request; \n \n in response to a determination that the requested prediction comprises a long range prediction, the processor is caused to:\n for each supply meter that corresponds to a supply asset of the identified one or more supply assets:\n use the corresponding second-stage long range model of power provision with the retrieved portion of the corresponding time series of power provision values to generate a corresponding long range refinement of the corresponding base prediction of power provision; \n apply the corresponding long range refinement to the corresponding base prediction of power provision; and \n provide the corresponding base prediction of power provision, after refinement, in response to the request; and \n \n for each load meter that corresponds to an endpoint asset of the identified one or more load assets:\n use the corresponding second-stage long range model of power consumption with the retrieved portion of the corresponding time series of power consumption values to generate a corresponding long range refinement of the corresponding base prediction of power consumption; \n apply the corresponding long range refinement to the corresponding base prediction of power consumption; and \n provide the corresponding base prediction of power consumption, after refinement, in response to the request; or \n \n in response to a determination that the requested prediction comprises a mid range prediction, the processor is caused to:\n for each supply meter that corresponds to a supply asset of the identified one or more supply assets, provide the corresponding base prediction of power provision in response to the request; and \n for each load meter that corresponds to an endpoint asset of the identified one or more load assets: provide the corresponding base prediction of power consumption in response to the request.

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): 0.3567489464810954
- 35 USC 102 Novelty (BERT): 0.4883726229772461
- Combined Prediction Score: 0.3699113141307105
- Mean Citation Score: 180.59717800000004
- Max Citation Score: 192.72542
- Similarity Product: 130.41044449325324

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