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

Claim 10:
11. A non-transitory computer readable medium, comprising instructions for:
obtaining user data for a set of users who have interacted with a data system and a set of historical transaction data comprising data for a set of sales of 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;
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 a matching rules, the user data for the set of users and the transaction 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; for each of the first set of potential matches:
determining, by a scoring engine comprising a machine learning engine including a machine learning model, values for each feature of a set of features for that potential match by applying a feature extraction function, wherein each feature corresponds to a data item of the matching user and the associated sale for the potential match; and
applying, by the socring engine, a predication 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 pair 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 sale pairs from the historical pair set;
creating a first training set of data by determining a historical pair 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 sale 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 matching learning model of the machine learning engine at a first time using a training function that accepts an array of feature recorded 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 second times.