DETAILED ACTION
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/2/2025 has been entered.
Claim Status
Claims 1-3, 5-13, 15-18, and 20 are pending in this Office Action.
Claims 1, 5-6, 11, and 16 are amended.
Claims 4, 14, and 19 are cancelled.
Response to Arguments
Applicant’s arguments with respect to claims 1, 11, and 16 have been fully considered, but are not persuasive.
Applicant argues Harsh and Brown are silent as to at least the particular newly-amended claim features of: obtaining subsets of known and unknown viewing parameters; and predicting whether active viewing is taking place at the playback device in at least one of a current time or a specified future time by using a process of querying a plurality of survival curves based on candidate ranges of discrete possible values that are determined for each of the subset of unknown parameters for current viewing data. Applicant further cites par. 45 of the specification, to conclude that the new amendments are not taught by the cited prior art references.
The examiner respectfully disagrees. Harsh teaches known parameters include day of the week, time of day, season, channels, type of programs (par. 22 and 24). Unknown parameters include whether the set top box is powered on or off (par. 23 and 34). This demonstrates obtaining subsets of known and unknown viewing parameters. Harsh further teaches estimating viewership of a playback device based on the tune data and the survival curves. For example, if the playback device had a tuning event at 11:00 p.m. it can be estimated from the survival curve whether the playback device is likely powered on or off at a current or future time. If the device is powered off, the viewer is not watching the program or channel, while when the device is powered on the viewer is likely watching the program or channel (par. 12-13, 15, 17-23, and 31, Fig. 1, 2A and 6). This demonstrates predicting whether active viewing is taking place at the playback device in at least one of a current time or a specified future time. Harsh further teaches a candidate range is determined, such as the set top box is on or off and based on this a survival curve is selected that begins nearest the first tuning event (par. 34). This demonstrates using a process of querying a plurality of survival curves based on candidate ranges of discrete possible values that are determined for each of the subset of unknown parameters for current viewing data.
In response to applicant's argument regarding par. 45 of the specification, that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies are not recited in the rejected claim(s). For example, “Although the end time of the tune is unknown, a candidate range of discrete possible tune lengths can be determined by the processor 11 to support the predictive probability assessments. For example, if the ad server is asking for a tune that is currently four hours long, the processor 11 may assess probabilities between four hours through six hours). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 5, 9-13, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Harsh et al. (US 2012/0260280) in view of Brown (US 2017/0302997).
Regarding claims 1, 11, and 16, Harsh teaches: A method, apparatus, and non-transitory computer-readable medium comprising:
receiving historical viewing data including a plurality of viewing parameters [receiving tune data for a period of time, such as three months and parsing the data (par. 25, Fig. 1 and 3). The tune data including parameters, such as day of the week, time of day, and channel (par. 20, 22 and 25)]
generating a plurality of probability distribution functions of the historical viewing data, wherein each of the plurality of probability distribution functions associated with a different one of the plurality of viewing parameters [determining from tune data the probability versus time that a video playback device is powered off at any particular time using different parameters, such as different days of the week, hours of the day, or channels (par. 13, 22-25, and 28). In other words, a probability distribution function (par. 32)]
determining, based on the plurality of probability distribution functions, a plurality of survival curves, wherein each of the plurality of survival curves is associated with a different one of the plurality of probability distribution functions [Constructing one or more survival curves based on the calculated probability distribution functions, such as one for each day of the week, hour of the day, or different channels. The survival curve 100 plots the probability versus time that a video playback device is powered off at any particular time (par. 22-24, and 28, Fig. 2A)]
determining a dimensional probability framework and one or more parameters related to whether active viewing is taking place at a playback device, wherein the one or more parameters map to the dimensional probability framework [survival curves 120 can be constructed for each day of the week starting at each hour of the day (par. 24, Fig. 2A and 2B). Survival curves indicate a probability versus time that a video playback device is powered on, and thus viewing is taking place. Different parameters may be used, such as day of the week, time of day, different channels, types of programs (par. 22)]
receiving current viewing data [receiving tune data from set top boxes used to determine if a playback device is likely powered off. The tune data may appear as if the subscriber is watching, suggesting the tune data is current (abstract, par. 12, 20-22, and 31)],
wherein a first subset of the one or more parameters are known for the current viewing data, and wherein a second subset of the one or more parameters are unknown for the current viewing data [Known parameters include day of the week, time of day, season, channels, type of programs (par. 22 and 24). Unknown parameters include whether the set top box is powered on or off (par. 23 and 34)]
predicting, based on at least one of the plurality of survival curves and the current viewing data, whether active viewing is taking place at the playback device in at least one of a current time or a specified future time [estimating viewership of a playback device based on the tune data and the survival curves. For example, if the playback device had a tuning event at 11:00 p.m. it can be estimated from the survival curve whether the playback device is likely powered on or off at a current or future time. If the device is powered off, the viewer is not watching the program or channel, while when the device is powered on the viewer is likely watching the program or channel (par. 12-13, 15, 17-23, and 31, Fig. 1, 2A and 6)],
wherein a candidate range of discrete possible values is determined for each of the second subset of the one or more parameters that are unknown for the current viewing data, and wherein the predicting further comprises querying the plurality of survival curves based on the candidate range of discrete possible values determined for each of the second subset of the one or more parameters that are unknown for the current viewing data [A candidate range is determined, such as the set top box is on or off and based on this a survival curve is selected that begins nearest the first tuning event (par. 34)] and
determining, based on a prediction that active viewing is taking place at the playback device, to output a targeted advertisement [If the playback device is predicted to be powered on, the viewer is likely watching the program or channel, which improves estimation of viewership. Improved viewership information allows advertisers to better target desired audiences with advertisements (par. 1, 12-13, 15, 17-23, and 31, Fig. 1, 2A and 6)].
Harsh does not explicitly disclose: output a targeted, addressable advertisement to the playback device during an advertisement insertion opportunity
Brown teaches: output a targeted, addressable advertisement to the playback device during an advertisement insertion opportunity [insert addressable advertisements that are targeted to the user actively viewing content at that moment based on real-time content viewership report data (par. 90)].
It would have been obvious to one of ordinary skill in the art, having the teachings of Harsh and Brown before the effective filing date of the claimed invention to modify the method of Harsh by incorporating outputting a targeted, addressable advertisement to the playback device during an advertisement insertion opportunity as disclosed by Brown. The motivation for doing so would have been to improve targeted advertising to provide advertisements not only based on household information, but also based on which user is actively viewing content at that moment (Brown - par. 19 and 90). Therefore, it would have been obvious to combine the teachings of Harsh and Brown to obtain the invention as specified in the instant claim.
Regarding claims 2, 12, and 17, Harsh and Brown teach the method of claim 1; Harsh further teaches: the plurality of survival curves estimate a likelihood that the playback device remains powered on or is powered off between two consecutive tuning events in the historical viewing data [the survival curves estimate the likelihood that a video playback device remains powered on at any time between two consecutive tuning events (par. 22 and 34, Fig. 6)].
Regarding claims 3, 13, and 18, Harsh and Brown teach the method of claim 1; Harsh further teaches: predicting whether active viewing is taking place at the playback device further comprises predicting, based on the plurality of survival curves and the associated plurality of probability distribution functions, and based on the current viewing data, whether the playback device is powered on or is powered off [determining the probability versus time that a video playback device is powered off (par. 13, 22-25, and 28). Constructing one or more survival curves based on the calculated probability versus time (par. 23-24, and 28, Fig. 2A). Predict whether a video playback device is powered on or is powered off, and thus the user is likely viewing or not viewing, based on the previously-generated survival curve constructed based on the probability versus time, and the tune data (par. 12-13, 15, 19-23, and 31, Fig. 1, 2A and 6)].
Regarding claim 5, Harsh and Brown teach the method of claim 1; Harsh further teaches: identifying the one or more parameters with which to query the dimensional probability framework [identifying parameters to search for, such as cable channel 40 at 8 p.m. (par. 22 and 33-34, Fig. 2A and 2B)].
Regarding claim 9, Harsh and Brown teach the method of claim 1; Harsh further teaches: the historical viewing data indicates at least one of a channel change, a playback of a program, a recording of a program, or a change in playback of a program [The tuning events can represent such things as channel changes, recording or playing back programs that are transmitted to a set top box, and changes in play back (par. 17)].
Regarding claim 10, Harsh and Brown teach the method of claim 1; Harsh further teaches: the plurality of viewing parameters comprise at least one of a day, a daypart, a network, a network genre, or a type of content distribution service [the current tune data includes parameters, such as the day of the week, time of day, and channel (network) (par. 12, 17-18, 20, 22-24, 33-34)].
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Harsh et al. (US 2012/0260280) in view of Brown (US 2017/0302997) and further in view of Lee (US 2010/0114527).
Regarding claim 6, Harsh and Brown teach the method of claim 1; Harsh and Brown do not explicitly disclose: the dimensional probability framework is a Bayesian probability framework.
Lee teaches: the dimensional probability framework is a Bayesian probability framework [using a Bayesian probability classifier on different parameters to predict whether a media device is turned on or off (par. 28, 77, and 99, Fig. 1-2 and 4-6)].
It would have been obvious to one of ordinary skill in the art, having the teachings of Harsh, Brown, and Lee before the effective filing date of the claimed invention to modify the method of Harsh and Brown by incorporating the dimensional probability framework is a Bayesian probability framework as disclosed by Lee. The motivation for doing so would have been to improve the accuracy of media usage information to increase value for advertisers, media device producers, media device manufacturers, and/or any other interested groups (Lee - par. 19-21). Therefore, it would have been obvious to combine the teachings of Harsh, Brown, and Lee to obtain the invention as specified in the instant claim.
Claims 7, 15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Harsh et al. (US 2012/0260280) in view of Brown (US 2017/0302997) and further in view of Fiderer et al. (US 2017/0236150).
Regarding claims 7, 15, and 20, Harsh and Brown teach the method of claim 1; Harsh and Brown further teach: a prediction that active viewing is not taking place at the playback device [Harsh - estimating viewership of a playback device, such as the device is powered off, the viewer is not watching the program or channel (par. 12-13, 15, 17-23, and 31, Fig. 1, 2A and 6). Brown – deducing when users stop or are no longer viewing the content because they have left the vicinity of the media device (i.e., are no longer viewing) (par. 57)].
Harsh and Brown do not explicitly disclose: determining, based on the prediction that active viewing is not taking place, to output a non-addressable advertisement to the playback device during the advertisement insertion opportunity.
Fiderer teaches: determining, based on the prediction that active viewing is not taking place, to output a non-addressable advertisement to the playback device during the advertisement insertion opportunity [spots may be filled with conventional, non-addressable ads for a variety of reasons (par. 4 and 6-7). Determining a level of interest or lack thereof by the audience, such as some viewers may have muted the content or not be present and engaged (par. 14)].
It would have been obvious to one of ordinary skill in the art, having the teachings of Harsh, Brown, and Fiderer before the effective filing date of the claimed invention to modify the method of Harsh and Brown by incorporating the teaching of Fiderer to output a non-addressable advertisement based on the prediction that active viewing is not taking place. The motivation for doing so would have been to generate revenues or meet other objectives of the network operator, such as to sell full spots rather than audience segments for business or other reasons (Fiderer - par. 2 and 7). Therefore, it would have been obvious to combine the teachings of Harsh, Brown, and Fiderer to obtain the invention as specified in the instant claim.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Harsh et al. (US 2012/0260280) in view of Brown (US 2017/0302997) and further in view of Collart et al. (US 2009/0150553).
Regarding claim 8, Harsh and Brown teach the method of claim 1; Harsh and Brown do not explicitly disclose: determining whether to deploy a firmware update or to execute other maintenance activities on the playback device.
Collart teaches: determining whether to deploy a firmware update or to execute other maintenance activities on the playback device [detect a firmware version of a media playback device, receive new firmware, and determine to replace the first firmware with the new firmware (par. 11 and 14)].
It would have been obvious to one of ordinary skill in the art, having the teachings of Harsh, Brown, and Collart before the effective filing date of the claimed invention to modify the method of Harsh and Brown by incorporating determining whether to deploy a firmware update or to execute other maintenance activities on the playback device as disclosed by Collart. The motivation for doing so would have been to avoid receiving a firmware error when playing media (Collart - par. 14). Therefore, it would have been obvious to combine the teachings of Harsh, Brown, and Collart to obtain the invention as specified in the instant claim.
Conclusion
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/ALEXANDER BOYD/Examiner, Art Unit 2424
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