Patent ID: 11966933
Assignee: TRUECAR, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A system, comprising:
a data system comprising:
a processor;
a data store storing user data for a set of users who have interacted with the data system and a set of historical transaction data comprising sales data for a set of sales for vehicles, the user data for the set of users and the historical transaction data for the set of sales comprising a set of data items;
a non-transitory computer readable medium, comprisng instructions for:
a matching engine adapted for:
determining a first set of potential matches from the set of sales and the set of users, a match of the first set of potential matches comprising a matching user of the set of users for an associated sale of the set of sales, wherein each potential match is determined by comparing, based on a set of matching rules, the user data for the set ofusers and the transation data for the set of sales to determine the matching user for the associated sale of the set of sales, each matching rule of the set of matching rules corresponding to a data item of the matching user and the associated sale; and

a scoring engine comprising a machine learning engine with a machine learning model, the scoring engine adapted for:
for the potential match of the first set of potential matches:
determining values for each of a set of features for that potential match based on applying a feature extraction function, wherein each features corresponds to a data item of the matching user and the associated sale for the potential match;
applying a prediction function of the machine learning engine to the values for the set of features to generate a confidence score for the potential match wherein the scoring engine was trained by:
creating a first training set of data by determining a historical set from the set of users who have interacted with the vehicle data system and the set of historical transaction data and selecting a set of approved historical sage pairs from the historical pair set;
creating a second training set of data by selecting a set of non-sale pairs from the determined historical pair set; and
training the machine learning model of the machine learning engine at a first time using a training frunction that accepts an array of feature records by determining values for the set of features for the first training set of data and the second training set of data using the feature extraction function and training the machine learning model of the scoring engine to provide the prediction function based on the determined values for the set of features for the first training set of data and the second training set of data, wherein the set of features and parameters for the prediction function are determined by training the machine learning engine using known matches of users and sales; and
refining the first training set of data, including updating the set of approved historical sale pairs based on a confirmation of the historical pairs; and
iteratively repeating the training the machine learning model of the scoring engine at one or more times.