Source: https://patents.google.com/patent/US9294813B2/en
Timestamp: 2019-07-19 01:06:51
Document Index: 144847837

Matched Legal Cases: ['Application No. 11009958', 'Application No. 2', 'Application No. 11009958', 'Application No. 05798239', 'Application No. 05', 'Application No. 05', 'application No. 09013713', 'Application No. 11009958', 'Application No. 200580030202', 'Application No. 553385', 'Application No. 553385', 'Application No. 2010219320', 'Application No. 2010201753', 'Application No. 2005273948', 'Application No. 2006279518', 'Application No. 2010201753', 'Application No. 10', 'Application No. 10', 'art6', 'Application No. 10', 'Application No. 200580030202', 'Application No. 257865', 'Application No. 09101551', 'Application No. 200680036510', 'Application No. 200680036510', 'Application No. 200680036510', 'Application No. 201110288875', 'Application No. 201110288875', 'Application No. 200580030202', 'Application No. 200680036510']

US9294813B2 - Probabilistic methods and apparatus to determine the state of a media device - Google Patents
US9294813B2
US9294813B2 US14/307,152 US201414307152A US9294813B2 US 9294813 B2 US9294813 B2 US 9294813B2 US 201414307152 A US201414307152 A US 201414307152A US 9294813 B2 US9294813 B2 US 9294813B2
US14/307,152
US20140298367A1 (en
2014-06-17 Application filed by Nielsen Co (US) LLC filed Critical Nielsen Co (US) LLC
2014-06-17 Priority to US14/307,152 priority patent/US9294813B2/en
2014-07-03 Assigned to THE NIELSEN COMPANY (US), LLC reassignment THE NIELSEN COMPANY (US), LLC ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: LEE, MORRIS
2014-10-02 Publication of US20140298367A1 publication Critical patent/US20140298367A1/en
2016-03-22 Publication of US9294813B2 publication Critical patent/US9294813B2/en
Probabilistic methods and apparatus to determine the state of a media device are described. An example method includes generating, based on a first output signal from a first sensor, a first parameter value representative of a first property of a media device; generating, based on a second output signal from a second sensor, a second parameter value representative of a second property of the media device; calculating a first state probability that the media device is presenting media provided by a first source based on a first conditional probability associated with the first parameter value and a second conditional probability associated with the second parameter value; calculating a second state probability that the media device is presenting media provided by a second source different than the first source based on a third conditional probability associated with the first parameter value and a fourth conditional probability associated with the second parameter value; and generating a state identifier by comparing the first state probability and the second state probability.
This patent arises from a continuation of U.S. patent application Ser. No. 12/263,150, filed Oct. 31, 2008, now U.S. Pat. No. 8,793,717, which is hereby incorporated herein by reference in its entirety.
In an example, the Naïve Bayes Classifier 150 implements Bayes' Theorem expressed below in equation (1). The example assumes a sample space, which is partitioned into a number of subspaces. In other words, the sample space is a media device and the number of subspaces is the total number of possible states in the media device. Each state is designated by X1, where X1 may be an ON state, X2 may be an OFF state, X3 may be a GAME state, X4 may be a DVD state, etc. If the Naïve Bayes Module 128 monitors a media device in an unknown state with certain known sensor-measured parameters, which are in a sample space Y that includes the entire set of observed values for the parameters, the probability that the media device is in a particular state Xi is given by equation (1):
P ⁡ [ X i ❘ Y ] = P ⁡ [ Y ❘ X i ] · P ⁡ [ X i ] ∑ j ⁢ ⁢ P ⁡ [ Y ❘ X j ] · P ⁡ [ X j ] ( 1 )
In another example implementation, equation (1) may be modified to calculate the natural logarithmic odds for each media state. In this example, the odds the media device is in a certain state are determined by taking the sum of the log of the conditional probabilities of all the parameters for one state, adding the log of the probability of the state itself, and subtracting the log of the probability of the entire known set Y. The state of the media device is then determined by the Naïve Bayes Classifier 150 as the state with the greatest logarithmic odds value. In yet other examples, the equation (1) may not divide by the entire set of observed values Y if Y is common for all possible media device states.
The example process 700 of FIG. 7 shows a training routine for the Naïve Bayes Module 128 of FIGS. 1 and 2 and begins with the determination of the number and types of sensors for monitoring a media device (block 702). Additionally, the types of outputs for each sensor are determined Next, the parameter generator 204 within the Naïve Bayes Modules 128 is configured for the corresponding sensor output types (block 704). The parameter generator 204 is configured so that sensor outputs are converted into the specified digital format and so that the parameter generator 204 routes the resulting parameters to a specified preliminary classifier or the Naïve Bayes Classifier 150.
generating, based on a first output signal from a first sensor, a first parameter value representative of a first property of a media device;
generating, based on a second output signal from a second sensor, a second parameter value representative of a second property of the media device;
calculating a first state probability that the media device is presenting media provided by a first source based on a first conditional probability associated with the first parameter value and a second conditional probability associated with the second parameter value;
calculating a second state probability that the media device is presenting media provided by a second source different than the first source based on a third conditional probability associated with the first parameter value and a fourth conditional probability associated with the second parameter value; and
generating a state identifier by comparing the first state probability and the second state probability.
2. A method as defined in claim 1, wherein generating the state identifier comprising selecting the greater of the first state probability or the second state probability.
3. A method as defined in claim 1, wherein the first sensor is an audio sensor and the second sensor is a light sensor.
4. A method as defined in claim 1, wherein the first conditional probability associated with the first parameter value is calculated during a training session occurring prior to generation of the first output signal.
5. A method as defined in claim 4, wherein the training session includes recording a first set of first parameter values and calculating a probability distribution of the first parameter values in the first set.
6. A method as defined in claim 1, wherein the first source comprises a set-top box and the second source comprises a gaming system.
7. A method as defined in claim 1, further comprising sending the state identifier to a metering entity.
a parameter generator to:
generate, based on a first output signal from a first sensor, a first parameter value representative of a first property of a media device; and
generate, based on a second output signal from a second sensor, a second parameter value representative of a second property of the media device; and
calculate a first state probability that the media device is presenting media provided by a first source based on a first conditional probability associated with the first parameter value and a second conditional probability associated with the second parameter value;
calculate a second state probability that the media device is presenting media provided by a second source different than the first source based on a third conditional probability associated with the first parameter value and a fourth conditional probability associated with the second parameter value; and
generate a state identifier by comparing the first state probability and the second state probability.
9. An apparatus as defined in claim 8, wherein the classifier is to generate the state identifier by selecting the greater of the first state probability or the second state probability.
10. An apparatus as defined in claim 8, wherein the first sensor is an audio sensor and the second sensor is a light sensor.
11. An apparatus as defined in claim 8, wherein the first conditional probability associated with the first parameter value is calculated during a training session occurring prior to generation of the first output signal.
12. An apparatus as defined in claim 11, wherein the training session includes recording a first set of first parameter values and calculating a probability distribution of the first parameter values in the first set.
13. An apparatus as defined in claim 8, wherein the first source comprises a set-top box and the second source comprises a gaming system.
14. An apparatus as defined in claim 8, further comprising a communicator to send the state identifier to a metering entity.
15. A machine readable storage device or storage disc comprising instructions that, when executed, cause a machine to at least:
generate, based on a first output signal from a first sensor, a first parameter value representative of a first property of a media device;
generate, based on a second output signal from a second sensor, a second parameter value representative of a second property of the media device;
16. A machine readable storage device or storage disc as defined in claim 15, wherein the instructions, when executed, cause the machine to generate the state identifier by selecting the greater of the first state probability or the second state probability.
17. A machine readable storage device or storage disc as defined in claim 15, wherein the first sensor is an audio sensor and the second sensor is a light sensor.
18. A machine readable storage device or storage disc as defined in claim 15, wherein the first conditional probability associated with the first parameter value is calculated during a training session occurring prior to generation of the first output signal.
19. A machine readable storage device or storage disc as defined in claim 18, wherein the training session includes recording a first set of first parameter values and calculating a probability distribution of the first parameter values in the first set.
20. A machine readable storage device or storage disc as defined in claim 15, wherein the first source comprises a set-top box and the second source comprises a gaming system.
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