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US 12,632,495 B2Granted
Systems and methods for deriving leading indicators of economic activity using predictive analytics applied to pedestrian attributes to predict behaviors and influence business outcomes
OwnerBrian McCarsoninventor
Life of this patent
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Abstract
Predictive analytics techniques are provided for produce leading indicators of economic activity based on observed pedestrian attributes—e.g., appearance and behavior—and other factors determined from a range of available data sources. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode.
Claims20
A predictive analytics system comprising:
- a plurality of datasets received from a corresponding plurality of data sources, wherein at least one of the plurality of stored datasets includes first sensor data derived from direct observation of activity within an environment, and wherein the plurality of datasets includes a set of pedestrian attributes;
- a data preparation module, including a processing device, configured to assign metadata to the datasets;
- and
- at least one of the following:
- (a) a predictive analytics module, including a processing device, configured to train first and second machine learning models using the datasets as a source of predictor variables based on the assigned metadata for each of the machine learning models, and to use historical information regarding a metric of economic activity as a target variable, wherein the first and second machine learning models differ in at least one of machine learning model type and assigned metadata;
- and
- (b) a hypothesis generating and testing system (HGTS) that trains the first and second machine learning models, selects the predictor variables for the machine learning models, and determines the leading indicator using the assigned metadata for the machine learning models;
- the HGTS including an analytics engine configured to:
- assign first and second statistical metrics to the first and second machine learning models;
- store the assigned metadata, the first machine learning model, and the predictor variables in a primary database in response to determining that the statistical metric assigned to the first machine learning model meets a predetermined level;
- and store the assigned metadata, the second machine learning model, and the predictor variables in a second, probationary database in response to determining that the second machine learning model exhibits a statistical metric does not meet a predetermined level;
- and select between the first and second machine learning models based on their respective statistical metric.
9 more elements
A method for performing predictive analytics comprising:
- receiving a plurality of datasets from a corresponding plurality of data sources, wherein at least one of the plurality of stored datasets includes first sensor data derived from direct observation of activity within an environment, and wherein the plurality of datasets includes a set of pedestrian attributes;
- using a data preparation module, including a processing device, to assign metadata to the datasets;
- and
- performing at least one of the following:
- (a) training, via a predictive analytics module including a processing device, first and second machine learning models using the datasets as a source of predictor variables based on the assigned metadata for each of the machine learning models, and to use historical information regarding a metric of economic activity as a target variable, wherein the first and second machine learning models differ in at least one of machine learning model type and assigned metadata;
- and
- (b) via a hypothesis generating and testing system (HGTS), training first and second machine learning models, selecting the predictor variables for the machine learning models, and determines the leading indicator using the assigned metadata for the machine learning models;
- the HGTS including an analytics engine configured to:
- assign first and second statistical metrics to the first and second machine learning models;
- store the assigned metadata, the first machine learning model, and the predictor variables in a primary database in response to determining that the statistical metric assigned to the first machine learning model meets a predetermined level;
- and store the assigned metadata, the second machine learning model, and the predictor variables in a second, probationary database in response to determining that the second machine learning model exhibits a statistical metric does not meet a predetermined level;
- and select between the first and second machine learning models based on their respective statistical metric.
9 more elements
Dependencies are inferred from the claim text.