APPARATUS AND METHODS FOR SENSOR FUSION DATA ANALYTICS USING ARTIFICIAL INTELLIGENCE
A method can include receiving historical data, sensor fusion data, and customer profile data about a set of customers. The method can include generating a set of customer embeddings, each including a vector representation of an image of a customer in the historical data. The method can include integrating the customer profile data to the set of customer embeddings and identifying a subset of customer images of a subset of sensor fusion data that matches a subset of customer embeddings. The method can include integrating the subset of sensor fusion data to the subset of customer embeddings from which a set of customer behaviors or a set of customer attributes can be identified. The method can include predicting a demand value or a likely path of a customer from the set of customers toward a location based on the set of customer behaviors or the set of customer attributes.
1 . A method, comprising:
receiving, by a processor, historical data, sensor fusion data, and customer profile data associated with a set of customers;
creating, by the processor using the received data, a set of customer embeddings for the set of customers, each customer embedding including a vector representation of an image of a respective customer from the set of customers;
linking, by the processor, the customer profile data with the set of customer embeddings;
identifying, by the processor, a subset of customer images of a subset of sensor fusion data among a set of customer images of the sensor fusion data that match a subset of customer embeddings in the set of customer embeddings;
linking, by the processor, the subset of sensor fusion data with the subset of customer embeddings;
identifying, by the processor, at least one of a set of customer behaviors or a set of customer attributes based on the subset of customer embeddings; and
predicting, by the processor, at least one of a demand value or a likely path of a customer from the set of customers toward a location based on at least one of the set of customer behaviors or the set of customer attributes.
2 . The method of claim 1 , further comprising:
transmitting, by the processor to an electronic device of at least one customer, a recommendation corresponding to the demand value or the likely path.
3 . The method of claim 1 , wherein the sensor fusion data comprises at least one of video data, location data, beacon data, audio data, or movement data.
4 . The method of claim 1 , wherein the customer profile data comprises at least one of loyalty data, demographic data, or transaction data associated with at least a portion of the set of customers.
5 . The method of claim 1 , wherein the processor extracts the set of customer attributes using a customer attribute mapping model that detects data associated with an object associated with at least one customer based on an image of the customer.
6 . The method of claim 1 , wherein the processor extracts the set of customer behaviors using a customer behavior mapping model that detects data associated with at least one of an emotion, a dwell time, gazing, a pace, an instance of moving with a group of at least one customer of the set of customers.
7 . The method of claim 1 , wherein the demand value corresponds to an affinity towards an item.
8 . The method of claim 1 , wherein the processor executes a machine-learning model to identify the subset of customer images.
9 . The method of claim 1 , wherein predicting at least one of a demand value or a likely path of a customer from the set of customers toward a location is further based on customer profile data.
10 . A computer system comprising:
one or more processors; and
one or more computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving historical data, sensor fusion data, and customer profile data associated with a set of customers;
creating, using the received data, a set of customer embeddings for the set of customers, each customer embedding including a vector representation of an image of a respective customer from the set of customers;
linking the customer profile data with the set of customer embeddings;
identifying a subset of customer images of a subset of sensor fusion data among a set of customer images of the sensor fusion data that match a subset of customer embeddings in the set of customer embeddings;
linking the subset of sensor fusion data with the subset of customer embeddings;
identifying at least one of a set of customer behaviors or a set of customer attributes based on the subset of customer embeddings; and
predicting at least one of a demand value or a likely path of a customer from the set of customers toward a location based on at least one of the set of customer behaviors or the set of customer attributes.
11 . The system of claim 10 , wherein one or more computer-executable instructions further cause the one or more processors to transmit, to an electronic device of at least one customer, a recommendation corresponding to the demand value or the likely path.
12 . The system of claim 10 , wherein the sensor fusion data comprises at least one of video data, location data, beacon data, audio data, or movement data.
13 . The system of claim 10 , wherein the customer profile data comprises at least one of loyalty data, demographic data, or transaction data associated with at least a portion of the set of customers.
14 . The system of claim 10 , wherein the processor extracts the set of customer attributes using a customer attribute mapping model that detects data associated with an object associated with at least one customer based on an image of the customer.
15 . The system of claim 10 , wherein the processor extracts the set of customer behaviors using a customer behavior mapping model that detects data associated with at least one of an emotion, a dwell time, gazing, a pace, an instance of moving with a group of at least one customer of the set of customers.
16 . The system of claim 10 , wherein the demand value corresponds to an affinity towards an item.
17 . The system of claim 10 , wherein the processor executes a machine-learning model to identify the subset of customer images.
18 . The system of claim 10 , wherein predicting at least one of a demand value or a likely path of a customer from the set of customers toward a location is further based on customer profile data.
19 . A computer system comprising:
a data repository; and
a server having a processor configured to:
receive historical data, sensor fusion data, and customer profile data associated with a set of customers;
create, using the received data, a set of customer embeddings for the set of customers, each customer embedding including a vector representation of an image of a respective customer from the set of customers;
link the customer profile data with the set of customer embeddings;
identify a subset of customer images of a subset of sensor fusion data among a set of customer images of the sensor fusion data that match a subset of customer embeddings in the set of customer embeddings;
link the subset of sensor fusion data with the subset of customer embeddings;
identifying, by the processor, at least one of a set of customer behaviors or a set of customer attributes based on the subset of customer embeddings; and
predict at least one of a demand value or a likely path of a customer from the set of customers toward a location based on at least one of the set of customer behaviors or the set of customer attributes.
20 . The system of claim 19 , wherein the processor is further configured to:
transmit to an electronic device of at least one customer, a recommendation corresponding to the demand value or the likely path.