Patent US7051352B1 successfully refreshed from Google Patents.
US7051352B1
Expired / Lapsed
US
⏱️ 0.0 years remaining
Adaptive TV program recommender
Assignee: Sisvel SpA
•
Filing: 2000-02-04
•
Granted: 2006-05-23
•
Expires: 2020-02-04
Abstract
Abstract A system for recommending television programs makes use of probabilistic calculations and a viewer profile to create a recommendation. The probabilistic calculations preferably are in the form of Bayesian classifier theory. Modifications to classical Bayesian classifier theory are proposed.
Inventors
- 👤 J. David Schaffer
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-26 20:48
CPC Classifications
H; H04; H04H; H04H60/00; H04H60/35; H04H60/46; H04N; H04N21/00; H04N21/40; H04N21/45; H04N21/466; H04N21/4662; H04N21/4663; H04N21/43; H04N21/4508; H04N21/4532; H04N21/454; H04N21/4667; H04N21/4668; H04N21/47; H04N21/482; H04N21/4826; H04N7/00; H04N7/16; H04N7/162; H04N7/163
Claims Analysis
Total Claims: 47
Claim 1
Independent Claim
210 words
A data processing device comprising: at least one input for receiving data including viewer profile data; and data regarding a television program; a medium readable by the data processing device coupled to the input, said medium storing said viewer profile data; and a processor, the processor being adapted to perform the following: calculating a probability that the television program is a desired one; and supplying a recommendation regarding the television program based on the probability, wherein the processor maintains the viewer profile in accordance with a data structure comprising: a list of feature values; and for each element of the list, a respective number of times programs having that feature value were watched, and a respective number of times programs having that feature value were not watched, and wherein the processor is further arranged to perform the following, each time a user watches a new program, first adding, to the list, feature values or counts of such feature values, associated with that new program; selecting at least one companion program to the new program, the companion program being selected at random from a program schedule, which companion program has not been watched; and second adding, to the list, feature values of the companion program, or counts of such feature values.
Claim 2
Dependent Claim
210 words
A data processing device comprising: at least one input for receiving data including viewer profile data; and data regarding a television program; a medium readable by the data processing device coupled to the input, said medium storing said viewer profile data; and a processor, the processor being adapted to perform the following: calculating a probability that the television program is a desired one; and supplying a recommendation regarding the television program based on the probability, wherein the processor maintains the viewer profile in accordance with a data structure comprising: a list of feature values; and for each element of the list, a respective number of times programs having that feature value were watched, and a respective number of times programs having that feature value were not watched, and wherein the processor is further arranged to perform the following, each time a user watches a new program, first adding, to the list, feature values or counts of such feature values, associated with that new program; selecting at least one companion program to the new program, the companion program being selected at random from a program schedule, which companion program has not been watched; and second adding, to the list, feature values of the companion program, or counts of such feature values.
Claim 3
Dependent Claim
44 words
The data processing device of claim 1 , wherein the processor is further arranged to perform the following, each time a user watches a new program: first adding, to the list, feature values or counts of such feature values, associated with that new program.
Claim 4
Dependent Claim
15 words
The data processing device of claim 1 , wherein the input is a network connection.
Claim 5
Dependent Claim
15 words
The data processing device of claim 1 , wherein calculating comprises using a Bayesian classifier.
Claim 6
Dependent Claim
30 words
The data processing device of claim 4 , wherein the processor is further adapted to subject the viewer profile to a noise threshold calculation prior to using the Bayesian classifier.
Claim 7
Dependent Claim
211 words
A data processing device comprising: at least one input for receiving data including viewer profile data; and data regarding a television program; and a processor, the processor being adapted to perform the following: calculating, using a Bayesian classifier, a probability that the television program is a desired one; and supplying a recommendation regarding the television program based on the probability, wherein the processor is further adapted to subject the viewer profile to a noise threshold calculation prior to using the Bayesian classifier, and wherein the viewer profile data comprises a list of feature values; a respective negative count for each element of the list, the negative count indicating a number of times programs having that feature value have not been watched; a respective positive count for each element of the list, the positive count indicating a number of times programs having that feature value have been watched; the noise threshold calculation comprises selecting a sub-list comprising at least feature values having at least one specific type of feature; choosing the highest negative count in the sub-list as the noise threshold; the recommendation comprises a program selected from a group having at least one feature value having a positive or negative count in the viewer profile, which count exceeds the noise threshold.
Claim 8
Dependent Claim
211 words
A data processing device comprising: at least one input for receiving data including viewer profile data; and data regarding a television program; and a processor, the processor being adapted to perform the following: calculating, using a Bayesian classifier, a probability that the television program is a desired one; and supplying a recommendation regarding the television program based on the probability, wherein the processor is further adapted to subject the viewer profile to a noise threshold calculation prior to using the Bayesian classifier, and wherein the viewer profile data comprises a list of feature values; a respective negative count for each element of the list, the negative count indicating a number of times programs having that feature value have not been watched; a respective positive count for each element of the list, the positive count indicating a number of times programs having that feature value have been watched; the noise threshold calculation comprises selecting a sub-list comprising at least feature values having at least one specific type of feature; choosing the highest negative count in the sub-list as the noise threshold; the recommendation comprises a program selected from a group having at least one feature value having a positive or negative count in the viewer profile, which count exceeds the noise threshold.
Claim 9
Dependent Claim
21 words
The data processing device of claim 6 , wherein the specific type comprises a day and time of day feature type.
Claim 10
Dependent Claim
18 words
The data processing device of claim 6 , wherein the specific type comprises a station identification feature type.
Claim 11
Dependent Claim
34 words
The data processing device of claim 6 , wherein the viewer profile data comprises a plurality of respective counts of programs watched, each respective count indicating how many programs watched had a respective feature.
Claim 12
Dependent Claim
23 words
The data processing device of claim 9 , wherein calculating comprises calculating a probability that the television program is in a particular class.
Claim 13
Dependent Claim
28 words
The data processing device of claim 10 , wherein the class is one of programs the viewer is interested in, and programs the viewer is not interested in.
Claim 14
Dependent Claim
108 words
A data processing device comprising: at least one input for receiving data including viewer profile data; and data regarding a television program; and a processor, the processor being adapted to perform the following: calculating, using a Bayesian classifier, a probability that the television program is a desired one; and supplying a recommendation regarding the television program based on the probability, wherein the processor is further adapted to subject the viewer profile to a noise threshold calculation prior to using the Bayesian classifier, and wherein subjecting the viewer profile to the noise threshold further comprises using observations gathered by a known random process to estimate a reasonable noise threshold.
Claim 15
Dependent Claim
108 words
A data processing device comprising: at least one input for receiving data including viewer profile data; and data regarding a television program; and a processor, the processor being adapted to perform the following: calculating, using a Bayesian classifier, a probability that the television program is a desired one; and supplying a recommendation regarding the television program based on the probability, wherein the processor is further adapted to subject the viewer profile to a noise threshold calculation prior to using the Bayesian classifier, and wherein subjecting the viewer profile to the noise threshold further comprises using observations gathered by a known random process to estimate a reasonable noise threshold.
Claim 16
Dependent Claim
113 words
A data processing device comprising: at least one input for receiving data including viewer profile data; and data regarding a television program; and a processor, the processor being adapted to perform the following: calculating a probability that the television program is a desired one; and supplying a recommendation regarding the television program based on the probability, wherein calculating the probability comprises: computing a prior possibility, of whether a program is desired or not; computing a conditional probability of whether a feature fi will be present if a show is desired or not; and computing a posterior probability of whether program is desired or not, based on the conditional probability and the prior probability.
Claim 17
Dependent Claim
113 words
A data processing device comprising: at least one input for receiving data including viewer profile data; and data regarding a television program; and a processor, the processor being adapted to perform the following: calculating a probability that the television program is a desired one; and supplying a recommendation regarding the television program based on the probability, wherein calculating the probability comprises: computing a prior possibility, of whether a program is desired or not; computing a conditional probability of whether a feature fi will be present if a show is desired or not; and computing a posterior probability of whether program is desired or not, based on the conditional probability and the prior probability.
Claim 18
Dependent Claim
23 words
The data processing device of claim 13 , wherein it is assumed that programs watched are programs that the viewer is interested in.
Claim 19
Dependent Claim
32 words
The data processing device of claim 13 , wherein the processor is further adapted to provide a recommendation regarding an additional item, other than a television program, based on the viewer profile.
Claim 20
Dependent Claim
139 words
A data processing device comprising: at least one input for receiving data including viewer profile data; and data regarding a television program; and a processor, the processor being adapted to perform the following: calculating a probability that the television program is a desired one; and supplying a recommendation regarding the television program based on the probability, wherein the viewer profile comprises a list of features types and values for such feature types; the feature types are selected from at least two sets, including a first set of feature types whose values are deemed non-independent; and a second set of feature types whose values are deemed independent; and calculating a probability comprises applying a Bayesian classifier calculation corresponding to feature types from the second set; and applying a modified Bayesian classifier calculation corresponding to feature types from the first set.
Claim 21
Dependent Claim
139 words
A data processing device comprising: at least one input for receiving data including viewer profile data; and data regarding a television program; and a processor, the processor being adapted to perform the following: calculating a probability that the television program is a desired one; and supplying a recommendation regarding the television program based on the probability, wherein the viewer profile comprises a list of features types and values for such feature types; the feature types are selected from at least two sets, including a first set of feature types whose values are deemed non-independent; and a second set of feature types whose values are deemed independent; and calculating a probability comprises applying a Bayesian classifier calculation corresponding to feature types from the second set; and applying a modified Bayesian classifier calculation corresponding to feature types from the first set.
Claim 22
Dependent Claim
33 words
The data processing device of claim 16 , wherein with respect to features of the first set, the modified Bayesian classifier calculation considers only feature values that match with a show being classified.
Claim 23
Dependent Claim
175 words
A computer readable medium having computer-executable instructions stored thereon for performing the method comprising: calculating a probability that a television program is a desired one, based on a viewer profile and data regarding the television program; and supplying a recommendation regarding the television program based on the probability, wherein the computer readable medium further embodies the viewer profile, the viewer profile being embodied as a data structure comprising: a list of feature values; and for each element of the list, a respective number of times programs having that feature value were watched, and wherein the software is further arranged to perform the following, each time a user watches a new program, first adding, to the list, feature values or counts of such feature values, associated with that new program; selecting at least one companion program to the new program, the companion program being selected at random from a program schedule, which companion program has not been watched; and second adding, to the list, feature values of the companion program, or counts of such feature values.
Claim 24
Dependent Claim
175 words
A computer readable medium having computer-executable instructions stored thereon for performing the method comprising: calculating a probability that a television program is a desired one, based on a viewer profile and data regarding the television program; and supplying a recommendation regarding the television program based on the probability, wherein the computer readable medium further embodies the viewer profile, the viewer profile being embodied as a data structure comprising: a list of feature values; and for each element of the list, a respective number of times programs having that feature value were watched, and wherein the software is further arranged to perform the following, each time a user watches a new program, first adding, to the list, feature values or counts of such feature values, associated with that new program; selecting at least one companion program to the new program, the companion program being selected at random from a program schedule, which companion program has not been watched; and second adding, to the list, feature values of the companion program, or counts of such feature values.
Claim 25
Dependent Claim
45 words
The computer readable medium of claim 18 , wherein the computer-executable instructions is further arranged to perform the following, each time a user watches a new program: first adding, to the list, feature values or counts of such feature values, associated with that new program.
Claim 26
Dependent Claim
20 words
The computer readable medium of claim 18 , wherein the computer readable medium embodies the data regarding the television program.
Claim 27
Dependent Claim
15 words
The computer readable medium of claim 18 , wherein calculating comprises using a Bayesian classifier.
Claim 28
Dependent Claim
31 words
The computer readable medium of claim 21 , wherein the computer-executable instructions, is further adapted to subject the viewer profile to a noise threshold calculation prior to using the Bayesian classifier.
Claim 29
Dependent Claim
202 words
A computer readable medium having computer-executable instructions stored thereon for performing the method comprising: calculating, using a Bayesian classifier, a probability that a television program is a desired one, based on a viewer profile and data regarding the television program; and supplying a recommendation regarding the television program based on the probability, wherein the computer-executable instructions is further adapted to subject the viewer profile to a noise threshold calculation prior to using the Bayesian classifier, and wherein the viewer profile data comprises a list of feature values; a respective negative count for each element of the list, the negative count indicating a number of times programs having that feature value have not been watched; a respective positive count for each element of the list, the positive count indicating a number of times programs having that feature value have been watched; the noise threshold calculation comprises selecting a sub-list comprising at least feature values having at least one specific type of feature; choosing the highest negative count in the sub-list as the noise threshold; the recommendation comprises a program selected from a group having at least one feature value having a positive or negative count in the viewer profile exceeding the noise threshold.
Claim 30
Dependent Claim
202 words
A computer readable medium having computer-executable instructions stored thereon for performing the method comprising: calculating, using a Bayesian classifier, a probability that a television program is a desired one, based on a viewer profile and data regarding the television program; and supplying a recommendation regarding the television program based on the probability, wherein the computer-executable instructions is further adapted to subject the viewer profile to a noise threshold calculation prior to using the Bayesian classifier, and wherein the viewer profile data comprises a list of feature values; a respective negative count for each element of the list, the negative count indicating a number of times programs having that feature value have not been watched; a respective positive count for each element of the list, the positive count indicating a number of times programs having that feature value have been watched; the noise threshold calculation comprises selecting a sub-list comprising at least feature values having at least one specific type of feature; choosing the highest negative count in the sub-list as the noise threshold; the recommendation comprises a program selected from a group having at least one feature value having a positive or negative count in the viewer profile exceeding the noise threshold.
Claim 31
Dependent Claim
21 words
The computer readable medium of claim 23 , wherein the specific type comprises a day and time of day feature type.
Claim 32
Dependent Claim
18 words
The computer readable medium of claim 23 , wherein the specific type comprises a station identification feature type.
Claim 33
Dependent Claim
34 words
The computer readable medium of claim 23 , wherein the viewer profile data comprises a plurality of respective counts of programs watched, each respective count indicating how many programs watched had a respective feature.
Claim 34
Dependent Claim
23 words
The computer readable medium of claim 26 , wherein calculating comprises calculating a probability that the television program is in a particular class.
Claim 35
Dependent Claim
30 words
The computer readable medium of claim 26 , wherein the class comprises at least one of programs the viewer is interested in and programs the viewer is not interested in.
Claim 36
Dependent Claim
101 words
A computer readable medium having computer-executable instructions stored thereon for performing the method comprising: calculating, using a Bayesian classifier, a probability that a television program is a desired one, based on a viewer profile and data regarding the television program; and supplying a recommendation regarding the television program based on the probability, wherein the computer-executable instructions is further adapted to subject the viewer profile to a noise threshold calculation prior to using the Bayesian classifier, and wherein subjecting the viewer profile to the noise threshold further comprises using observations gathered by a known random process to estimate a reasonable noise threshold.
Claim 37
Dependent Claim
101 words
A computer readable medium having computer-executable instructions stored thereon for performing the method comprising: calculating, using a Bayesian classifier, a probability that a television program is a desired one, based on a viewer profile and data regarding the television program; and supplying a recommendation regarding the television program based on the probability, wherein the computer-executable instructions is further adapted to subject the viewer profile to a noise threshold calculation prior to using the Bayesian classifier, and wherein subjecting the viewer profile to the noise threshold further comprises using observations gathered by a known random process to estimate a reasonable noise threshold.
Claim 38
Dependent Claim
103 words
A computer readable medium having computer-executable instructions stored thereon for performing the method comprising: calculating a probability that a television program is a desired one, based on a viewer profile and data regarding the television program; and supplying a recommendation regarding the television program based on the probability, wherein calculating the probability comprises: computing a prior possibility, of whether a program is desired or not; computing a conditional probability of whether a feature fi will be present if a show is desired; and computing a posterior probability of whether program is desired or not, based on the conditional probability and the prior probability.
Claim 39
Dependent Claim
103 words
A computer readable medium having computer-executable instructions stored thereon for performing the method comprising: calculating a probability that a television program is a desired one, based on a viewer profile and data regarding the television program; and supplying a recommendation regarding the television program based on the probability, wherein calculating the probability comprises: computing a prior possibility, of whether a program is desired or not; computing a conditional probability of whether a feature fi will be present if a show is desired; and computing a posterior probability of whether program is desired or not, based on the conditional probability and the prior probability.
Claim 40
Dependent Claim
23 words
The computer readable medium of claim 30 , wherein it is assumed that programs watched are programs that the viewer is interested in.
Claim 41
Dependent Claim
33 words
The computer readable medium of claim 30 , wherein the computer-executable instructions is further arranged to provide a recommendation regarding an additional item, other than a television program, based on the viewer profile.
Claim 42
Dependent Claim
131 words
A computer readable medium having computer-executable instructions stored thereon for performing the method comprising: calculating a probability that a television program is a desired one, based on a viewer profile and data regarding the television program; and supplying a recommendation regarding the television program based on the probability, wherein the viewer profile comprises a list of features types and values for such feature types; the feature types are selected from at least two sets, including a first set of feature types whose values are deemed non-independent; and a second set of feature types whose values are deemed independent; and calculating a probability comprises applying a Bayesian classifier calculation corresponding to feature types from the second set; and applying a modified Bayesian classifier calculation corresponding to feature types from the first set.
Claim 43
Dependent Claim
131 words
A computer readable medium having computer-executable instructions stored thereon for performing the method comprising: calculating a probability that a television program is a desired one, based on a viewer profile and data regarding the television program; and supplying a recommendation regarding the television program based on the probability, wherein the viewer profile comprises a list of features types and values for such feature types; the feature types are selected from at least two sets, including a first set of feature types whose values are deemed non-independent; and a second set of feature types whose values are deemed independent; and calculating a probability comprises applying a Bayesian classifier calculation corresponding to feature types from the second set; and applying a modified Bayesian classifier calculation corresponding to feature types from the first set.
Claim 44
Dependent Claim
32 words
The computer readable medium of claim 33 , wherein with respect to features of the first set, the modified Bayesian classifier calculation considers only values that match with a show being classified.
Claim 45
Dependent Claim
174 words
A data processing method comprising performing the following operations in a data processing device: first receiving data reflecting physical observations, which data includes a list of feature values and observations about feature values, some of which feature values are independent and some of which are not; second receiving data about an item to be classified, the data about the item to be classified including feature values; maintaining a division of the data reflecting physical observations into at least two sets, including a first set including those feature values which are deemed not independent; and a second set including those feature values which are deemed independent; performing a probabilistic calculation on the data reflecting physical observations and the data regarding an item to be classified including: applying a Bayesian classifier calculation with respect to feature values relating to the second set; and applying a modified Bayesian classifier calculation with respect to feature values relating to the first set presenting a conclusion regarding the item to be classified to a user based on the probabilistic calculation.
Claim 46
Dependent Claim
174 words
A data processing method comprising performing the following operations in a data processing device: first receiving data reflecting physical observations, which data includes a list of feature values and observations about feature values, some of which feature values are independent and some of which are not; second receiving data about an item to be classified, the data about the item to be classified including feature values; maintaining a division of the data reflecting physical observations into at least two sets, including a first set including those feature values which are deemed not independent; and a second set including those feature values which are deemed independent; performing a probabilistic calculation on the data reflecting physical observations and the data regarding an item to be classified including: applying a Bayesian classifier calculation with respect to feature values relating to the second set; and applying a modified Bayesian classifier calculation with respect to feature values relating to the first set presenting a conclusion regarding the item to be classified to a user based on the probabilistic calculation.
Claim 47
Dependent Claim
38 words
The method of claim 35 , wherein the modified Bayesian classifier calculation comprises ignoring feature values from the data reflecting physical observations when those feature values are not present in the data regarding the item to be classified.
References Cited (Backward Citations)
US4706121B1
Cited
US4706121A
Cited
EP0572090A2
Cited
WO1994013107A1
Cited
US5444499A
Cited
US5410344A
Cited
EP0669760A2
Cited
US5585865A
Cited
EP0682452A2
Cited
GB2289782A
Cited
US5534911A
Cited
FR2726718A1
Cited
US5758257A
Cited
EP0721253A2
Cited
EP0725539A2
Cited
WO1996027840A1
Cited
EP0735749A2
Cited
US5880768A
Cited
US5768422A
Cited
US5758259A
Cited
US5867226A
Cited
EP0774866A2
Cited
US5790935A
Cited
US5635989A
Cited
US5704017A
Cited
US5828419A
Cited
US5867799A
Cited
US5848396A
Cited
EP0840504A1
Cited
WO1997046006A1
Cited
WO1997048230A1
Cited
WO1997048228A1
Cited
EP0836320A2
Cited
WO1998021878A2
Cited
US5801747A
Cited
EP0854645A2
Cited
GB2325537A
Cited
WO1998053609A1
Cited
WO1998056173A1
Cited
WO1999001984A1
Cited
US6637029B1
Cited
US6005597A
Cited
US6871186B1
Cited
US5987415A
Cited
US6317722B1
Cited
US6412012B1
Cited
US6727914B1
Cited
USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 2009-05-22 | 2009-11-21 | 2010-05-23 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2013-05-22 | 2013-11-21 | 2014-05-23 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2017-05-22 | 2017-11-21 | 2018-05-23 | $7700.00 | Window Closed / Lapsed |