Automatic user identification from button presses recorded in a feature vector
First Claim
1. A method, comprising:
- receiving a user identifier at a user device;
training the device by constructing a profile of the user associated with the user identifier, wherein the profile comprises a feature vector that is a function of the user'"'"'s pressing of buttons on the device, the function represented by
button_isf_vec[i]=log((float)(last 13 session+2)/(button_cum_vec[i]+1))wherebutton _isf_vec is the feature vector,i is the index of the feature vector, such that 0 ≦
i <
the number of buttons on the device,last_session is an index referring to the number of sessions that the user has spent interacting with the device, andbutton_cum vec[i] is the number of times that the user has pressed the ith button of the device,where the function incorporates an inverse frequency of the presses of each button;
associating the profile with the user identifier; and
subsequent to said training, predicting the user identifier, given one or more instances of the user'"'"'s pressing of buttons on the device, given the profile, and given the association of the profile with the user identifier.
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Abstract
Methods, systems and computer program products to allow a user to identify himself to a content provider, without having to explicitly perform a log in process or other identification and authentication process. By manipulating a user device such as a remote control, a profile of the user may be constructed, where the profile includes a representation of how the individual user typically manipulates the device. The profile includes a feature vector that is a function of the number of times that individual buttons are pressed. The construction of the feature vector may be viewed as a training or learning phase. Once the profile and feature vector are constructed, the user'"'"'s interaction with the device in a subsequent session may be captured and compared with the profile. This may allow identification of the user, in turn allowing content to be tailored in a manner specific to this user.
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Citations
15 Claims
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1. A method, comprising:
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receiving a user identifier at a user device; training the device by constructing a profile of the user associated with the user identifier, wherein the profile comprises a feature vector that is a function of the user'"'"'s pressing of buttons on the device, the function represented by
button_isf_vec[i]=log((float)(last 13 session+2)/(button_cum_vec[i]+1))where button _isf_vec is the feature vector, i is the index of the feature vector, such that 0 ≦
i <
the number of buttons on the device,last_session is an index referring to the number of sessions that the user has spent interacting with the device, and button_cum vec[i] is the number of times that the user has pressed the ith button of the device, where the function incorporates an inverse frequency of the presses of each button; associating the profile with the user identifier; and subsequent to said training, predicting the user identifier, given one or more instances of the user'"'"'s pressing of buttons on the device, given the profile, and given the association of the profile with the user identifier. - View Dependent Claims (2, 3, 4, 5)
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6. A system, comprising:
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a processor; and a memory in communication with said processor, wherein said memory stores a plurality of processing instructions configured to direct said processor to receive a user identifier at a user device; train the device by constructing a profile of the user associated with the user identifier, wherein the profile comprises a feature vector that is a function of the user'"'"'s pressing of buttons on the device, the function represented by
button_isf_vec[i]=log((float)(last13 session+2)/(button_cum _vec[i]+1))where button_isf_vec is the feature vector, i is the index of the feature vector, such that 0 ≦
i <
the number of buttons on the device,last_session is an index referring to the number of sessions that the user has spent interacting with the device, and button_cum_vec[i] is the number of times that the user has pressed the ith button of the device, where the function incorporates an inverse frequency of the presses of each button; associate the profile with the user identifier; and subsequent to said training, predict the user identifier, given one or more instances of the user'"'"'s pressing of buttons on the device, given the profile, and given the association of the profile with the user identifier. - View Dependent Claims (7, 8, 9, 10)
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11. A computer program product including a non-transitory computer readable medium having computer program logic stored therein, the computer program logic comprising:
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logic to cause a processor to receive a user identifier at a user device; logic to cause the processor to train the device by constructing a profile of the user associated with the user identifier, wherein the profile comprises a feature vector that is a function of the user'"'"'s pressing of buttons on the device, the function represented by
button_isf_vec[i]=log((float)(last13 session+2)/(button_cum_vec[i]+1))where button_isf_vec is the feature vector, i is the index of the feature vector, such that 0≦
i <
the number of buttons on the device,last13session is an index referring to the number of sessions that the user has spent interacting with the device, and button_cum_vec[i] is the number of times that the user has pressed the ith button of the device, where the function incorporates an inverse frequency of the presses of each button; logic to cause the processor to associate the profile with the user identifier; and logic to cause the processor to predict the user identifier, subsequent to said training, given one or more instances of the user'"'"'s pressing of buttons on the device, given the profile, and given the association of the profile with the user identifier. - View Dependent Claims (12, 13, 14, 15)
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Specification