Privacy-preserving behavior detection
First Claim
1. An apparatus, comprising:
- a sensor to detect a plurality of radio signals from one or more transmitters; and
a processor to;
identify the plurality of radio signals detected by the sensor;
detect a proximity of one or more assets based on the plurality of radio signals, wherein the one or more assets are associated with the one or more transmitters;
identify the one or more assets based on an identity of the one or more transmitters, wherein each transmitter is associated with a particular asset;
identify a plurality of signal characteristics associated with the plurality of radio signals;
detect a proximity of a human based on the plurality of signal characteristics;
detect one or more human-asset interactions based on the plurality of signal characteristics, wherein the one or more human-asset interactions are detected based on a machine learning model, wherein the machine learning model is trained to recognize different human-asset interactions based on the plurality of signal characteristics, and wherein the one or more human-asset interactions comprise a plurality of customer-product interactions between a retail customer and a plurality of clothing items; and
determine, based on the plurality of customer-product interactions;
an order in which the plurality of clothing items are tried on by the retail customer;
one or more groupings of the plurality of clothing items that are tried on together by the retail customer;
an amount of time in which each of the plurality of clothing items is tried on by the retail customer;
ora number of times in which each of the plurality of clothing items is tried on by the retail customer.
1 Assignment
0 Petitions
Accused Products
Abstract
In one embodiment, an apparatus may comprise a sensor to detect a plurality of radio signals from one or more transmitters. The apparatus may further comprise a processor to: identify the plurality of radio signals detected by the sensor; detect a proximity of one or more assets based on the plurality of radio signals, wherein the one or more assets are associated with the one or more transmitters; identify the one or more assets based on an identity of the one or more transmitters, wherein each transmitter is associated with a particular asset; identify a plurality of signal characteristics associated with the plurality of radio signals; detect a proximity of a human based on the plurality of signal characteristics; and detect one or more human-asset interactions based on the plurality of signal characteristics.
36 Citations
23 Claims
-
1. An apparatus, comprising:
-
a sensor to detect a plurality of radio signals from one or more transmitters; and a processor to; identify the plurality of radio signals detected by the sensor; detect a proximity of one or more assets based on the plurality of radio signals, wherein the one or more assets are associated with the one or more transmitters; identify the one or more assets based on an identity of the one or more transmitters, wherein each transmitter is associated with a particular asset; identify a plurality of signal characteristics associated with the plurality of radio signals; detect a proximity of a human based on the plurality of signal characteristics; detect one or more human-asset interactions based on the plurality of signal characteristics, wherein the one or more human-asset interactions are detected based on a machine learning model, wherein the machine learning model is trained to recognize different human-asset interactions based on the plurality of signal characteristics, and wherein the one or more human-asset interactions comprise a plurality of customer-product interactions between a retail customer and a plurality of clothing items; and determine, based on the plurality of customer-product interactions; an order in which the plurality of clothing items are tried on by the retail customer; one or more groupings of the plurality of clothing items that are tried on together by the retail customer; an amount of time in which each of the plurality of clothing items is tried on by the retail customer;
ora number of times in which each of the plurality of clothing items is tried on by the retail customer. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
-
-
11. At least one non-transitory machine accessible storage medium having instructions stored thereon, wherein the instructions, when executed on a machine, cause the machine to:
-
identify a plurality of radio signals received by a sensor from one or more transmitters; detect a proximity of one or more assets based on the plurality of radio signals, wherein the one or more assets are associated with the one or more transmitters; identify the one or more assets based on an identity of the one or more transmitters, wherein each transmitter is associated with a particular asset; identify a plurality of signal characteristics associated with the plurality of radio signals; detect a proximity of a human based on the plurality of signal characteristics; detect one or more human-asset interactions based on the plurality of signal characteristics, wherein the one or more human-asset interactions are detected based on a machine learning model, wherein the machine learning model is trained to recognize different human-asset interactions based on the plurality of signal characteristics, and wherein the one or more human-asset interactions comprise a plurality of customer-product interactions between a retail customer and a plurality of clothing items; and determine, based on the plurality of customer-product interactions; an order in which the plurality of clothing items are tried on by the retail customer; one or more groupings of the plurality of clothing items that are tried on together by the retail customer; an amount of time in which each of the plurality of clothing items is tried on by the retail customer;
ora number of times in which each of the plurality of clothing items is tried on by the retail customer. - View Dependent Claims (12, 13, 14, 15, 16, 17)
-
-
18. A system, comprising:
-
one or more transmitters to transmit a plurality of radio signals, wherein the one or more transmitters are associated with one or more assets; one or more sensors to detect the plurality of radio signals from the one or more transmitters; and a processor to; identify the plurality of radio signals detected by the one or more sensors; detect a proximity of the one or more assets based on the plurality of radio signals; identify the one or more assets based on an identity of the one or more transmitters, wherein each transmitter is associated with a particular asset; identify a plurality of signal characteristics associated with the plurality of radio signals; detect a proximity of a human based on the plurality of signal characteristics; detect one or more human-asset interactions based on the plurality of signal characteristics, wherein the one or more human-asset interactions are detected based on a machine learning model, wherein the machine learning model is trained to recognize different human-asset interactions based on the plurality of signal characteristics, and wherein the one or more human-asset interactions comprise a plurality of customer-product interactions between a retail customer and a plurality of clothing items; and determine, based on the plurality of customer-product interactions; an order in which the plurality of clothing items are tried on by the retail customer; one or more groupings of the plurality of clothing items that are tried on together by the retail customer; an amount of time in which each of the plurality of clothing items is tried on by the retail customer;
ora number of times in which each of the plurality of clothing items is tried on by the retail customer. - View Dependent Claims (19, 20, 21)
-
-
22. A method, comprising:
-
identifying a plurality of radio signals received by a sensor from one or more transmitters; detecting a proximity of one or more assets based on the plurality of radio signals, wherein the one or more assets are associated with the one or more transmitters; identifying the one or more assets based on an identity of the one or more transmitters, wherein each transmitter is associated with a particular asset; identifying a plurality of signal characteristics associated with the plurality of radio signals; detecting a proximity of a human based on the plurality of signal characteristics; detecting one or more human-asset interactions based on the plurality of signal characteristics, wherein the one or more human-asset interactions are detected based on a machine learning model, wherein the machine learning model is trained to recognize different human-asset interactions based on the plurality of signal characteristics, and wherein the one or more human-asset interactions comprise a plurality of customer-product interactions between a retail customer and a plurality of clothing items; and determining, based on the plurality of customer-product interactions; an order in which the plurality of clothing items are tried on by the retail customer; one or more groupings of the plurality of clothing items that are tried on together by the retail customer; an amount of time in which each of the plurality of clothing items is tried on by the retail customer;
ora number of times in which each of the plurality of clothing items is tried on by the retail customer. - View Dependent Claims (23)
-
Specification