Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This Office Action is in response to CLAIMS entered for patent application 18/947,867 filed on November 14, 2024.
Claims 1-19 are pending.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No.: 12,177,503. Although the claims at issue are not identical, they are not patentably distinct from each other because both disclose receiving input data comprising an end-user type, a content descriptor of a target piece of content, a target content-provider network, and a time descriptor indicating a projected time at which a transmission of the target piece of content by the target content-provider network is to begin; identifying a sub-plurality of the end-users according to the end-user type, applying a machine-learning (ML) model to the input data and viewing/consumption data to determine, for each respective end-user of the sub-plurality, a respective set of parameters for determining how much of the target piece of content that the respective end-user is expected to view/consume during each of a sequence of time intervals starting at the projected time; wherein the viewing/consumption data is for the plurality of end-users, wherein the viewing/consumption data includes piece of content information for each piece of content comprising transmission time and duration, content-provider network, and characterization of the piece of content, and wherein the viewing/consumption data further includes end-user information comprising data characterizing end-users and their previous consumption activities; for each respective end-user of the sub-plurality, using the respective set of parameters to make a viewing/consumption determination of (i) temporal-fraction values of the target piece of content the respective end-user is expected to view/consume during each of the time intervals, and (ii) for each time interval, conditioning values used to condition the viewing/consumption determination for a next time interval; for each respective end-user of the sub-plurality, determining projected subtotals of viewing/consumption time of the target piece of content, based on the temporal-fraction values determined for all the time intervals; and determining a projected total amount of viewing/consumption time of the target piece of content based on the projected subtotals of all of the end-users of the sub-plurality.
Instant Application:
1. A tangible, non-transitory computer readable medium comprising instructions that, when executed, cause at least one processor to perform a set of operations comprising: receiving input data comprising an end-user type, a content descriptor of a target media content, a target content-provider network, and a time descriptor indicating a projected time at which a transmission of the target media content by the target content-provider network is to begin; identifying a sub-plurality of a plurality of end-users according to the end-user type, wherein the plurality of end-users have received previous media content transmissions over one or more content-provider networks; applying a machine-learning (ML) model to the input data and media consumption data to determine, for each respective end-user of the sub-plurality, a respective set of parameters for determining how much of the target media content that the respective end-user is expected to consume during each of a sequence of time intervals starting at the projected time, wherein the media consumption data is for the plurality of end-users, wherein the media consumption data includes media content information for each media content comprising transmission time and duration, content-provider network, and characterization of the media content, and wherein the media consumption data further includes end-user information comprising data characterizing end-users and their previous consumption activities; for each respective end-user of the sub-plurality, using the respective set of parameters to make a consumption determination of (i) temporal-fraction values of the target media content the respective end-user is expected to consume during each of the time intervals, and (ii) for each time interval, conditioning values used to condition the consumption determination for a next time interval; for each respective end-user of the sub-plurality, determining projected subtotals of consumption time of the target media content, based on the temporal-fraction values determined for all the time intervals; and determining a projected total amount consumption time of the target media content based on the projected subtotals of all of the end-users of the sub-plurality.
2. The tangible, non-transitory computer readable medium of claim 1, wherein the input data further comprises a projected fraction of the sub-plurality of the end-users that are projected to be receiving media content from the target content-provider network at the projected time, and wherein the set of operations further comprises: determining, for each respective end-user of the sub-plurality, based on their previous consumption activities and the projected fraction, content-reach projections of whether or not the respective end-user is expected to be consuming any media content at the projected time; determining, for each respective end-user of the sub-plurality, based on their previous consumption activities and the projected fraction, network-reach projections of whether or not the respective end-user is expected to be consuming the target content-provider network at the projected time; and for each respective end-user of the sub-plurality, conditioning the consumption determination for the first time interval based on the content-reach projections and the network-reach projections.
3. The tangible, non-transitory computer readable medium of claim 1, wherein the transmission time and duration comprises a time slot descriptor that specifies at least one of a day of week, time of day, month, or year, and further comprises a duration descriptor that specifies a number of consecutive time segments and a duration of each time segment, wherein the characterization of the media content comprises program metadata associated with the media content, the metadata including at least genre, wherein the data characterizing end-users and their previous consumption activities comprise, for each respective end-user of the plurality, demographic information and a consumption history over a multiplicity of consecutive time segments spanning a consumption timeline, and indicating, for each given time segment of the multiplicity, a fractional amount of the given time segment the respective end-user consumed any media content, and what network and media content was consumed for any non-zero fractional amount, and wherein the end-user type comprises one or more categories of demographic information, a content classification descriptor comprises one or more categories of program metadata, and the projected time comprises a projected time slot that specifies at least one of a projected day of week, a projected time of day, a projected month, or a projected year.
4. The tangible, non-transitory computer readable medium of claim 1, wherein applying the ML model to the input data and the media consumption data to determine the respective set of parameters for each respective end-user of the sub-plurality comprises, for each respective end-user of the sub-plurality: for each given time interval of the sequence, determining a media content parameter of a first Bernoulli probability distribution for predicting whether or not the respective end-user will consume any media content during the given time interval; for each given time interval of the sequence, determining a program parameter of a second Bernoulli probability distribution for predicting whether or not the respective end-user will consume any of the target media content during the given time interval; for each given time interval of the sequence, determining a total-program parameter of a third Bernoulli probability distribution for predicting whether or not the respective end-user will consume all of that portion of the target media content transmitted during the given time interval; and for each given time interval of the sequence, determining a parameter pair of a Beta probability distribution for predicting a fractional amount of the portion of the target media content transmitted during the given time interval that the respective end-user will consume.
5. The tangible, non-transitory computer readable medium of claim 4, wherein using the respective set of parameters to make the consumption determination of the temporal-fraction values comprises: performing a Monte Carlo simulation to generate an integer number M samples of binary values for each of the first, second, and third Bernoulli probability distributions; performing a Monte Carlo simulation to generate M samples of fractional values for the Beta probability distribution; and on a sample-by-sample basis, applying the M samples of binary values of the first, second, and third Bernoulli probability distributions as conditions to the M samples of fractional values of the Beta probability distribution to compute M samples of the temporal-fraction values.
6. The tangible, non-transitory computer readable medium of claim 5, wherein, for each given time interval of the sequence, each of the media content parameter, the program parameter, the total-program parameter, and the parameter pair take on predetermined values according to a lead-in condition specified by at least one of: whether or not the respective end-user is consuming any media content at the start of the given time interval, whether or not the respective end-user is consuming the target content-provider network at the start of the given time interval, or whether or not the respective end-user is consuming the target media content at the start of the given time interval, and wherein using the respective set of parameters to make the consumption determination of the temporal-fraction values further comprises, for each given time interval of the sequence, receiving M lead-in conditions that select, on a sample-by-sample basis, particular ones of the predetermined values of the parameters applied in the Monte Carlo simulations.
7. The tangible, non-transitory computer readable medium of claim 1, wherein the respective set of parameters comprises parameters of probability distributions predictive of a consumption fraction of the target media content that the respective end-user is expected to consume during each time interval of the sequence, wherein using the respective set of parameters to make the consumption determination of the temporal-fraction values comprises using probability distributions to compute a multiplicity of sample predictions of the consumption fraction in each of the time intervals, for each respective end-user of the sub-plurality, wherein determining the projected subtotals of consumption time of the target media content comprises: multiplying all the sample predictions of the consumption fraction by a common duration of all the time intervals to convert all the sample predictions of consumption fraction into sample predictions of consumption time; and for each respective end-user of the sub-plurality, on a sample-by-sample basis across corresponding multiplicities of the time intervals, summing sample predictions of consumption time across all of the time intervals to generate a multiplicity of aggregate consumption time predictions, and wherein determining the projected total amount consumption time of the target media content comprises, on a sample-by-sample basis, computing a weighted average of the aggregate consumption time predictions of all the end-users of the sub-plurality.
8. A method comprising: receiving input data comprising an end-user type, a content descriptor of a target media content, a target content-provider network, and a time descriptor indicating a projected time at which a transmission of the target media content by the target content-provider network is to begin; identifying a sub-plurality of a plurality of end-users according to the end-user type, wherein the plurality of end-users have received previous media content transmissions over one or more content-provider networks; applying a machine-learning (ML) model to the input data and media consumption data to determine, for each respective end-user of the sub-plurality, a respective set of parameters for determining how much of the target media content that the respective end-user is expected to consume during each of a sequence of time intervals starting at the projected time, wherein the media consumption data is for the plurality of end-users, wherein the media consumption data includes media content information for each media content comprising transmission time and duration, content-provider network, and characterization of the media content, and wherein the media consumption data further includes end-user information comprising data characterizing end-users and their previous consumption activities; for each respective end-user of the sub-plurality, using the respective set of parameters to make a consumption determination of (i) temporal-fraction values of the target media content the respective end-user is expected to consume during each of the time intervals, and (ii) for each time interval, conditioning values used to condition the consumption determination for a next time interval; for each respective end-user of the sub-plurality, determining projected subtotals of consumption time of the target media content, based on the temporal-fraction values determined for all the time intervals; and determining a projected total amount consumption time of the target media content based on the projected subtotals of all of the end-users of the sub-plurality.
9. The method of claim 8, wherein the input data further comprises a projected fraction of the sub-plurality of the end-users that are projected to be receiving media content from the target content-provider network at the projected time, and wherein the method further comprises: determining, for each respective end-user of the sub-plurality, based on their previous consumption activities and the projected fraction, content-reach projections of whether or not the respective end-user is expected to be consuming any media content at the projected time; determining, for each respective end-user of the sub-plurality, based on their previous consumption activities and the projected fraction, network-reach projections of whether or not the respective end-user is expected to be consuming the target content-provider network at the projected time; and for each respective end-user of the sub-plurality, conditioning the consumption determination for the first time interval based on the content-reach projections and the network-reach projections.
10. The method of claim 8, wherein the transmission time and duration comprises a time slot descriptor that specifies at least one of a day of week, time of day, month, or year, and further comprises a duration descriptor that specifies a number of consecutive time segments and a duration of each time segment, wherein the characterization of the media content comprises program metadata associated with the media content, the metadata including at least genre, wherein the data characterizing end-users and their previous consumption activities comprise, for each respective end-user of the plurality, demographic information and a consumption history over a multiplicity of consecutive time segments spanning a consumption timeline, and indicating, for each given time segment of the multiplicity, a fractional amount of the given time segment the respective end-user consumed any media content, and what network and media content was consumed for any non-zero fractional amount, and wherein the end-user type comprises one or more categories of demographic information, a content classification descriptor comprises one or more categories of program metadata, and the projected time comprises a projected time slot that specifies at least one of a projected day of week, a projected time of day, a projected month, or a projected year.
11. The method of claim 8, wherein applying the ML model to the input data and the media consumption data to determine the respective set of parameters for each respective end-user of the sub-plurality comprises, for each respective end-user of the sub-plurality: for each given time interval of the sequence, determining a media content parameter of a first Bernoulli probability distribution for predicting whether or not the respective end-user will consume any media content during the given time interval; for each given time interval of the sequence, determining a program parameter of a second Bernoulli probability distribution for predicting whether or not the respective end-user will consume any of the target media content during the given time interval; for each given time interval of the sequence, determining a total-program parameter of a third Bernoulli probability distribution for predicting whether or not the respective end-user will consume all of that portion of the target media content transmitted during the given time interval; and for each given time interval of the sequence, determining a parameter pair of a Beta probability distribution for predicting a fractional amount of the portion of the target media content transmitted during the given time interval that the respective end-user will consume.
12. The method of claim 11, wherein using the respective set of parameters to make the consumption determination of the temporal-fraction values comprises: performing a Monte Carlo simulation to generate an integer number M samples of binary values for each of the first, second, and third Bernoulli probability distributions; performing a Monte Carlo simulation to generate M samples of fractional values for the Beta probability distribution; and on a sample-by-sample basis, applying the M samples of binary values of the first, second, and third Bernoulli probability distributions as conditions to the M samples of fractional values of the Beta probability distribution to compute M samples of the temporal-fraction values.
13. The method of claim 12, wherein, for each given time interval of the sequence, each of the media content parameter, the program parameter, the total-program parameter, and the parameter pair take on predetermined values according to a lead-in condition specified by at least one of: whether or not the respective end-user is consuming any media content at the start of the given time interval, whether or not the respective end-user is consuming the target content-provider network at the start of the given time interval, or whether or not the respective end-user is consuming the target media content at the start of the given time interval, and wherein using the respective set of parameters to make the consumption determination of the temporal-fraction values further comprises, for each given time interval of the sequence, receiving M lead-in conditions that select, on a sample-by-sample basis, particular ones of the predetermined values of the parameters applied in the Monte Carlo simulations.
14. The method of claim 8, wherein the respective set of parameters comprises parameters of probability distributions predictive of a consumption fraction of the target media content that the respective end-user is expected to consume during each time interval of the sequence, wherein using the respective set of parameters to make the consumption determination of the temporal-fraction values comprises using probability distributions to compute a multiplicity of sample predictions of the consumption fraction in each of the time intervals, for each respective end-user of the sub-plurality, wherein determining the projected subtotals of consumption time of the target media content comprises: multiplying all the sample predictions of the consumption fraction by a common duration of all the time intervals to convert all the sample predictions of consumption fraction into sample predictions of consumption time; and for each respective end-user of the sub-plurality, on a sample-by-sample basis across corresponding multiplicities of the time intervals, summing sample predictions of consumption time across all of the time intervals to generate a multiplicity of aggregate consumption time predictions, and wherein determining the projected total amount consumption time of the target media content comprises, on a sample-by-sample basis, computing a weighted average of the aggregate consumption time predictions of all the end-users of the sub-plurality.
15. A computing device comprising: at least one processor; and tangible, non-transitory computer readable medium comprising instructions that, when executed, cause the at least one processor to perform a set of operations comprising: receiving input data comprising an end-user type, a content descriptor of a target media content, a target content-provider network, and a time descriptor indicating a projected time at which a transmission of the target media content by the target content-provider network is to begin; identifying a sub-plurality of a plurality of end-users according to the end-user type, wherein the plurality of end-users have received previous media content transmissions over one or more content-provider networks; applying a machine-learning (ML) model to the input data and media consumption data to determine, for each respective end-user of the sub-plurality, a respective set of parameters for determining how much of the target media content that the respective end-user is expected to consume during each of a sequence of time intervals starting at the projected time, wherein the media consumption data is for the plurality of end-users, wherein the media consumption data includes media content information for each media content comprising transmission time and duration, content-provider network, and characterization of the media content, and wherein the media consumption data further includes end-user information comprising data characterizing end-users and their previous consumption activities; for each respective end-user of the sub-plurality, using the respective set of parameters to make a consumption determination of (i) temporal-fraction values of the target media content the respective end-user is expected to consume during each of the time intervals, and (ii) for each time interval, conditioning values used to condition the consumption determination for a next time interval; for each respective end-user of the sub-plurality, determining projected subtotals of consumption time of the target media content, based on the temporal-fraction values determined for all the time intervals; and determining a projected total amount consumption time of the target media content based on the projected subtotals of all of the end-users of the sub-plurality.
16. The computing device of claim 15, wherein the input data further comprises a projected fraction of the sub-plurality of the end-users that are projected to be receiving media content from the target content-provider network at the projected time, and wherein the set of operations further comprises: determining, for each respective end-user of the sub-plurality, based on their previous consumption activities and the projected fraction, content-reach projections of whether or not the respective end-user is expected to be consuming any media content at the projected time; determining, for each respective end-user of the sub-plurality, based on their previous consumption activities and the projected fraction, network-reach projections of whether or not the respective end-user is expected to be consuming the target content-provider network at the projected time; and for each respective end-user of the sub-plurality, conditioning the consumption determination for the first time interval based on the content-reach projections and the network-reach projections.
17. The computing device of claim 15, wherein the transmission time and duration comprises a time slot descriptor that specifies at least one of a day of week, time of day, month, or year, and further comprises a duration descriptor that specifies a number of consecutive time segments and a duration of each time segment, wherein the characterization of the media content comprises program metadata associated with the media content, the metadata including at least genre, wherein the data characterizing end-users and their previous consumption activities comprise, for each respective end-user of the plurality, demographic information and a consumption history over a multiplicity of consecutive time segments spanning a consumption timeline, and indicating, for each given time segment of the multiplicity, a fractional amount of the given time segment the respective end-user consumed any media content, and what network and media content was consumed for any non-zero fractional amount, and wherein the end-user type comprises one or more categories of demographic information, a content classification descriptor comprises one or more categories of program metadata, and the projected time comprises a projected time slot that specifies at least one of a projected day of week, a projected time of day, a projected month, or a projected year.
18. The computing device of claim 15, wherein applying the ML model to the input data and the media consumption data to determine the respective set of parameters for each respective end-user of the sub-plurality comprises, for each respective end-user of the sub-plurality: for each given time interval of the sequence, determining a media content parameter of a first Bernoulli probability distribution for predicting whether or not the respective end-user will consume any media content during the given time interval; for each given time interval of the sequence, determining a program parameter of a second Bernoulli probability distribution for predicting whether or not the respective end-user will consume any of the target media content during the given time interval; for each given time interval of the sequence, determining a total-program parameter of a third Bernoulli probability distribution for predicting whether or not the respective end-user will consume all of that portion of the target media content transmitted during the given time interval; and for each given time interval of the sequence, determining a parameter pair of a Beta probability distribution for predicting a fractional amount of the portion of the target media content transmitted during the given time interval that the respective end-user will consume.
19. The computing device of claim 18, wherein using the respective set of parameters to make the consumption determination of the temporal-fraction values comprises: performing a Monte Carlo simulation to generate an integer number M samples of binary values for each of the first, second, and third Bernoulli probability distributions; performing a Monte Carlo simulation to generate M samples of fractional values for the Beta probability distribution; and on a sample-by-sample basis, applying the M samples of binary values of the first, second, and third Bernoulli probability distributions as conditions to the M samples of fractional values of the Beta probability distribution to compute M samples of the temporal-fraction values.
Pat. No.: 12,177,503
1. A system comprising: persistent storage having stored thereon television (TV) viewing data of a plurality of end-users who have received previous TV program transmissions over one or more content-provider networks, wherein the TV viewing data includes program information for each TV program comprising transmission time and duration, content-provider network, and characterization of the TV program, and wherein the TV viewing data further includes end-user information comprising data characterizing end-users and their previous viewing activities; one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the system to carry out operations including: receiving input data comprising an end-user type, a content descriptor of a target TV program, a target content-provider network, and a time descriptor indicating a projected time at which a transmission of the target TV program by the target content-provider network is to begin; identifying a sub-plurality of the end-users according to the end-user type; applying a machine-learning (ML) model to the input data and the TV viewing data to determine, for each respective end-user of the sub-plurality, a respective set of parameters for determining how much of the target TV program that the respective end-user is expected to view during each of a sequence of time intervals starting at the projected time; for each respective end-user of the sub-plurality, using the respective set of parameters to make a viewing determination of (i) temporal-fraction values of the target TV program the respective end-user is expected to view during each of the time intervals, and (ii) for each time interval, conditioning values used to condition the viewing determination for a next time interval; for each respective end-user of the sub-plurality, determining projected subtotals of viewing time of the target TV program, based on the temporal-fraction values determined for all the time intervals; and determining a projected total amount viewing time of the target TV program based on the projected subtotals of all of the end-users of the sub-plurality.
2. The system of claim 1, wherein the input data further comprise a projected fraction of the sub-plurality of the end-users that are projected to be receiving content from the target content-provider network at the projected time, and wherein the operations further include: determining, for each respective end-user of the sub-plurality, based on their previous viewing activities and the projected fraction, TV-reach projections of whether or not the respective end-user is expected to be viewing any TV content at the projected time; determining, for each respective end-user of the sub-plurality, based on their previous viewing activities and the projected fraction, network-reach projections of whether or not the respective end-user is expected to be viewing the target content-provider network at the projected time; and for each respective end-user of the sub-plurality, conditioning the viewing determination for the first time interval based on the TV-reach projections and the network-reach projections.
3. The system of claim 1, wherein the transmission time and duration comprises a time slot descriptor that specifies at least one of a day of week, time of day, month, or year, and further comprises a duration descriptor that specifies a number of consecutive time segments and a duration of each time segment, wherein the characterization of the TV program comprises program metadata associated with the TV program, the metadata including at least genre, wherein the data characterizing end-users and their previous viewing activities comprise, for each respective end-user of the plurality, demographic information and a viewing history over a multiplicity of consecutive time segments spanning a viewing timeline, and indicating, for each given time segment of the multiplicity, a fractional amount of the given time segment the respective end-user viewed any TV programming, and what network and TV program was viewed for any non-zero fractional amount, and wherein the end-user type comprises one or more categories of demographic information, a content classification descriptor comprises one or more categories of program metadata, and the projected time comprises a projected time slot that specifies at least one of a projected day of week, a projected time of day, a projected month, or a projected year.
4. The system of claim 1, wherein applying the ML model to the input data and the TV viewing data to determine the respective set of parameters for each respective end-user of the sub-plurality comprises, for each respective end-user of the sub-plurality: for each given time interval of the sequence, determining a TV parameter of a first Bernoulli probability distribution for predicting whether or not the respective end-user will view any TV programming during the given time interval; for each given time interval of the sequence, determining a program parameter of a second Bernoulli probability distribution for predicting whether or not the respective end-user will view any of the target TV program during the given time interval; for each given time interval of the sequence, determining a total-program parameter of a third Bernoulli probability distribution for predicting whether or not the respective end-user will view all of that portion of the target TV program transmitted during the given time interval; and for each given time interval of the sequence, determining a parameter pair of a Beta probability distribution for predicting a fractional amount of the portion of the target TV program transmitted during the given time interval that the respective end-user will view.
5. The system of claim 4, wherein using the respective set of parameters to make the viewing determination of the temporal-fraction values comprises: performing a Monte Carlo simulation to generate an integer number M samples of binary values for each of the first, second, and third Bernoulli probability distributions; performing a Monte Carlo simulation to generate M samples of fractional values for the Beta probability distribution; and on a sample-by-sample basis, applying the M samples of binary values of the first, second, and third Bernoulli probability distributions as conditions to the M samples of fractional values of the Beta probability distribution to compute M samples of the temporal-fraction values.
6. The system of claim 5, wherein, for each given time interval of the sequence, each of the TV parameter, the program parameter, the total-program parameter, and the parameter pair take on predetermined values according to a lead-in condition specified by at least one of: whether or not the respective end-user is viewing any TV programming at the start of the given time interval, whether or not the respective end-user is viewing the target content-provider network at the start of the given time interval, or whether or not the respective end-user is viewing the target TV program at the start of the given time interval, and wherein using the respective set of parameters to make the viewing determination of the temporal-fraction values further comprises, for each given time interval of the sequence, receiving M lead-in conditions that select, on a sample-by-sample basis, particular ones of the predetermined values of the parameters applied in the Monte Carlo simulations.
7. The system of claim 6, wherein using the respective set of parameters to make the viewing determination of the conditioning values comprises, on a sample-by-sample basis, setting each of the M lead-in conditions for the next time interval to one or more of the binary values of the first, second, or third Bernoulli probability distributions of the current time interval.
8. The system of claim 6, wherein the input data further comprise a projected fraction of the sub-plurality of the end-users that are projected to be receiving content from the target content-provider network at the projected time, and wherein the operations further include: for each respective end-user of the sub-plurality, determining M lead-in conditions for the first time interval of the sub-plurality, based on the previous viewing activities of the respective end-user and the projected fraction.
9. The system of claim 1, wherein the respective set of parameters comprises parameters of probability distributions predictive of a viewing fraction of the target TV program that the respective end-user of the sub-plurality is expected to view during each time interval of the sequence, wherein using the respective set of parameters to make the viewing determination of the temporal-fraction values comprises using probability distributions to compute a multiplicity of sample predictions of the viewing fraction in each of the time intervals, for each respective end-user of the sub-plurality, wherein determining the projected subtotals of viewing time of the target TV program comprises: multiplying all the sample predictions of the viewing fraction by a common duration of all the time intervals to convert all the sample predictions of viewing fraction into sample predictions of viewing time; and for each respective end-user of the sub-plurality, on a sample-by-sample basis across corresponding multiplicities of the time intervals, summing sample predictions of viewing time across all of the time intervals to generate a multiplicity of aggregate viewing time predictions, and wherein determining the projected total amount viewing time of the target TV program comprises, on a sample-by-sample basis, computing a weighted average of the aggregate viewing time predictions of all the end-users of the sub-plurality.
10. A method carried out by a computing system having access to persistent storage having stored thereon television (TV) viewing data of a plurality of end-users who have received previous TV program transmissions over one or more content-provider networks, wherein the TV viewing data includes program information for each TV program comprising transmission time and duration, content-provider network, and characterization of the TV program, and wherein the TV viewing data further includes end-user information comprising data characterizing end-users and their previous viewing activities, wherein the method comprises: receiving input data comprising an end-user type, a content descriptor of a target TV program, a target content-provider network, and a time descriptor indicating a projected time at which a transmission of the target TV program by the target content-provider network is to begin; identifying a sub-plurality of the end-users according to the end-user type; applying a machine-learning (ML) model to the input data and the TV viewing data to determine, for each respective end-user of the sub-plurality, a respective set of parameters for determining how much of the target TV program that the respective end-user is expected to view during each of a sequence of time intervals starting at the projected time; for each respective end-user of the sub-plurality, using the respective set of parameters to make a viewing determination of (i) temporal-fraction values of the target TV program the respective end-user is expected to view during each of the time intervals, and (ii) for each time interval, conditioning values used to condition the viewing determination for a next time interval; for each respective end-user of the sub-plurality, determining projected subtotals of viewing time of the target TV program, based on the temporal-fraction values determined for all the time intervals; and determining a projected total amount viewing time of the target TV program based on the projected subtotals of all of the end-users of the sub-plurality.
11. The method of claim 10, wherein the input data further comprise a projected fraction of the sub-plurality of the end-users that are projected to be receiving content from the target content-provider network at the projected time, and wherein the method further comprises: determining, for each respective end-user of the sub-plurality, based on their previous viewing activities and the projected fraction, TV-reach projections of whether or not the respective end-user is expected to be viewing any TV content at the projected time; determining, for each respective end-user of the sub-plurality, based on their previous viewing activities and the projected fraction, network-reach projections of whether or not the respective end-user is expected to be viewing the target content-provider network at the projected time; and for each respective end-user of the sub-plurality, conditioning the viewing determination for the first time interval based on the TV-reach projections and the network-reach projections.
12. The method of claim 10, wherein the transmission time and duration comprises a time slot descriptor that specifies at least one of a day of week, time of day, month, or year, and further comprises a duration descriptor that specifies a number of consecutive time segments and a duration of each time segment, wherein the characterization of the TV program comprises program metadata associated with the TV program, the metadata including at least genre, wherein the data characterizing end-users and their previous viewing activities comprise, for each respective end-user of the plurality, demographic information and a viewing history over a multiplicity of consecutive time segments spanning a viewing timeline, and indicating, for each given time segment of the multiplicity, a fractional amount of the given time segment the respective end-user viewed any TV programming, and what network and TV program was viewed for any non-zero fractional amount, and wherein the end-user type comprises one or more categories of demographic information, a content classification descriptor comprises one or more categories of program metadata, and the projected time comprises a projected time slot that specifies at least one of a projected day of week, a projected time of day, a projected month, or a projected year.
13. The method of claim 10, wherein applying the ML model to the input data and the TV viewing data to determine the respective set of parameters for each respective end-user of the sub-plurality comprises, for each respective end-user of the sub-plurality: for each given time interval of the sequence, determining a TV parameter of a first Bernoulli probability distribution for predicting whether or not the respective end-user will view any TV programming during the given time interval; for each given time interval of the sequence, determining a program parameter of a second Bernoulli probability distribution for predicting whether or not the respective end-user will view any of the target TV program during the given time interval; for each given time interval of the sequence, determining a total-program parameter of a third Bernoulli probability distribution for predicting whether or not the respective end-user will view all of that portion of the target TV program transmitted during the given time interval; and for each given time interval of the sequence, determining a parameter pair of a Beta probability distribution for predicting a fractional amount of the portion of the target TV program transmitted during the given time interval that the respective end-user will view.
14. The method of claim 13, wherein using the respective set of parameters to make the viewing determination of the temporal-fraction values comprises: performing a Monte Carlo simulation to generate an integer number M samples of binary values for each of the first, second, and third Bernoulli probability distributions; performing a Monte Carlo simulation to generate M samples of fractional values for the Beta probability distribution; and on a sample-by-sample basis, applying the M samples of binary values of the first, second, and third Bernoulli probability distributions as conditions to the M samples of fractional values of the Beta probability distribution to compute M samples of the temporal-fraction values.
15. The method of claim 14, wherein, for each given time interval of the sequence, each of the TV parameter, the program parameter, the total-program parameter, and the parameter pair take on predetermined values according to a lead-in condition specified by at least one of: whether or not the respective end-user is viewing any TV programming at the start of the given time interval, whether or not the respective end-user is viewing the target content-provider network at the start of the given time interval, or whether or not the respective end-user is viewing the target TV program at the start of the given time interval, and wherein using the respective set of parameters to make the viewing determination of the temporal-fraction values further comprises, for each given time interval of the sequence, receiving M lead-in conditions that select, on a sample-by-sample basis, particular ones of the predetermined values of the parameters applied in the Monte Carlo simulations.
16. The method of claim 15, wherein using the respective set of parameters to make the viewing determination of the conditioning values comprises, on a sample-by-sample basis, setting each of the M lead-in conditions for the next time interval to one or more of the binary values of the first, second, or third Bernoulli probability distributions of the current time interval.
17. The method of claim 15, wherein the input data further comprise a projected fraction of the sub-plurality of the end-users that are projected to be receiving content from the target content-provider network at the projected time, and wherein the method further comprises: for each respective end-user of the sub-plurality, determining M lead-in conditions for the first time interval of the sub-plurality, based on the previous viewing activities of the respective end-user and the projected fraction.
18. The method of claim 10, wherein the respective set of parameters comprises parameters of probability distributions predictive of a viewing fraction of the target TV program that the respective end-user is expected to view during each time interval of the sequence, wherein using the respective set of parameters to make the viewing determination of the temporal-fraction values comprises using probability distributions to compute a multiplicity of sample predictions of the viewing fraction in each of the time intervals, for each respective end-user of the sub-plurality, wherein determining the projected subtotals of viewing time of the target TV program comprises: multiplying all the sample predictions of the viewing fraction by a common duration of all the time intervals to convert all the sample predictions of viewing fraction into sample predictions of viewing time; and for each respective end-user of the sub-plurality, on a sample-by-sample basis across corresponding multiplicities of the time intervals, summing sample predictions of viewing time across all of the time intervals to generate a multiplicity of aggregate viewing time predictions, and wherein determining the projected total amount viewing time of the target TV program comprises, on a sample-by-sample basis, computing a weighted average of the aggregate viewing time predictions of all the end-users of the sub-plurality.
19. A non-transitory computer-readable medium having instructions stored thereon that, when carried out by one or more processors of a computing system comprising persistent storage having stored thereon television (TV) viewing data of a plurality of end-users who have received previous TV program transmissions over one or more content-provider networks, wherein the TV viewing data includes program information for each TV program comprising transmission time and duration, content-provider network, and characterization of the TV program, and wherein the TV viewing data further includes end-user information comprising data characterizing end-users and their previous viewing activities, cause the computing system to carry out operations including: receiving input data comprising an end-user type, a content descriptor of a target TV program, a target content-provider network, and a time descriptor indicating a projected time at which a transmission of the target TV program by the target content-provider network is to begin; identifying a sub-plurality of the end-users according to the end-user type; applying a machine-learning (ML) model to the input data and the TV viewing data to determine, for each respective end-user of the sub-plurality, a respective set of parameters for determining how much of the target TV program that the respective end-user is expected to view during each of a sequence of time intervals starting at the projected time; for each respective end-user of the sub-plurality, using the respective set of parameters to make a viewing determination of (i) temporal-fraction values of the target TV program the respective end-user is expected to view during each of the time intervals, and (ii) for each time interval, conditioning values used to condition the viewing determination for a next time interval; for each respective end-user of the sub-plurality, determining projected subtotals of viewing time of the target TV program, based on the temporal-fraction values determined for all the time intervals; and determining a projected total amount viewing time of the target TV program based on the projected subtotals of all of the end-users of the sub-plurality.
20. The non-transitory computer-readable medium of claim 19, wherein the input data further comprise a projected fraction of the sub-plurality of the end-users that are projected to be receiving content from the target content-provider network at the projected time, and wherein the operations further include: determining, for each respective end-user of the sub-plurality, based on their previous viewing activities and the projected fraction, TV-reach projections of whether or not the respective end-user is expected to be viewing any TV content at the projected time; determining, for each respective end-user of the sub-plurality, based on their previous viewing activities and the projected fraction, network-reach projections of whether or not the respective end-user is expected to be viewing the target content-provider network at the projected time; and for each respective end-user of the sub-plurality, conditioning the viewing determination for the first time interval based on the TV-reach projections and the network-reach projections.
Conclusion
Claims 1-19 are rejected.
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/Joshua D Taylor/Primary Examiner, Art Unit 2426 June 12, 2026
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