US 11,886,511 B2 · Machine-learned desking vehicle recommendation

Abstract

Systems and methods are disclosed herein for machine-learned vehicle desking operations. A vehicle recommendation system receives a request to determine similarities between vehicles. The request can indicate an identifier of a user-specified vehicle associated with vehicle attribute values (e.g., white color, sedan body style, 2020 manufacturing year, etc.). A machine learning model can determine respective embeddings for the vehicle attribute values and the respective embeddings can be concatenated, where the concatenated embeddings represent the user-specified vehicle in one embedding. The system can determine similarity metrics of the concatenated embeddings against reference embeddings. For example, a cosine similarity value can be determined for the concatenated embedding of the user-specified vehicle and the respective reference embeddings. Each similarity metric can represent a measure of similarity between the user-specified vehicle and a given vehicle. The vehicle recommendation system provides for display identifiers of vehicles that are ranked based on the determined similarity metrics.

Claims20

Dependent claims

A non-transitory computer-readable medium comprising memory with instructions encoded thereon, the instructions, when executed, causing one or more processors to perform operations, the instructions comprising instructions to:

receive a request to determine similarities between candidate vehicles and a user-specified vehicle associated with a plurality of vehicle attributes corresponding to a plurality of vehicle attribute values;
calculate a conditional probability between a vehicle attribute of the plurality of vehicle attributes and a valuation amount of the vehicle attribute;
select a subset of the plurality of vehicle attribute values using the conditional probability;
determine, using a machine learning model and the subset of the plurality of vehicle attribute values, a plurality of similarity metrics representative of similarities between the user-specified vehicle and the candidate vehicles;
and
provide for display identifiers of one or more candidate vehicles ranked based on the plurality of similarity metrics.

3 more elements

A system comprising:

memory with instructions encoded thereon;
and
one or more processors that, when executing the instructions, are caused to perform operations comprising:
receiving a request to determine similarities between candidate vehicles and a user-specified vehicle associated with a plurality of vehicle attributes corresponding to a plurality of vehicle attribute values;
calculating a conditional probability between a vehicle attribute of the plurality of vehicle attributes and a valuation amount of the vehicle attribute;
selecting a subset of the plurality of vehicle attribute values using the conditional probability;
determining, using a machine learning model and the subset of the plurality of vehicle attribute values, a plurality of similarity metrics representative of similarities between the user-specified vehicle and the candidate vehicles;
and
providing for display identifiers of one or more candidate vehicles ranked based on the plurality of similarity metrics.

6 more elements

Dependencies are inferred from the claim text.

Machine-learned desking vehicle recommendation

Life of this patent

Machine-learned desking vehicle recommendation

Life of this patent

Abstract

Claims20