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 .
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schwittmann (“Identifying TV Channels & On-Demand Videos using Ambient Light Sensors”, 2016), in view of Mafoodh et al (US PG Pub No. 2019/0341001).
Regarding claim 1, Schwittmann et al teaches a method (Abstract) comprising:
monitoring changes in ambient light over a period of time within a space encompassing a visual media-presentation device (i.e. ambient light can stem from reflections of the wall if a mobile device is not pointed at the tv screen) (Figure 1), and generating ambient-light data representing the monitored changes (i.e. data retrieved by sampling the ambient light sensor) (Abstract, Introduction: 2nd paragraph page 364);
providing the generated ambient-light data as input (i.e. data retrieved by sampling the ambient light sensor and providing said data to a server) (Figure 2; Introduction: 2nd paragraph page 364);
based on the provided ambient-light data, a prediction of whether the visual media-presentation device was presenting visual media content during the period of time (i.e. the server can identify the video being played) (Abstract, Introduction: 2nd paragraph page 364, Approach: 1st Paragraph Page 365); and
using the received prediction as a basis to control media-exposure measurement (i.e. data retrieved by sampling the ambient light sensor, providing said data to a server and the server can identify the video being played) (Introduction: 2nd paragraph page 364, Approach: 1st Paragraph Page 365).
The reference is unclear with respect to a trained machine-learning model and receiving from the machine-learning model.
In similar field of endeavor, Mafoodh et al teaches a trained machine-learning model and receiving from the machine-learning model (Figure 5; Para. 0027-28). Therefore, it would have been obvious to one of ordinary skill in the art to modify the reference before the effectively filing date of the claimed invention for the purpose of easily implementing self-adjustment functions of screen brightness based on the change of the ambient light.
Claim 2 is rejected wherein the visual media-presentation device is a television (Figure 1; Introduction: 1st Paragraph).
Claim 3 is rejected wherein using the received prediction as a basis to control media-exposure measurement comprises using the received prediction as a basis to control reporting of media-exposure data to a media-measurement platform and/or as a basis to control generating of media-exposure data (i.e. data retrieved by sampling the ambient light sensor, providing said data to a server and the server can identify the video being played) (Introduction: 2nd paragraph page 364, Approach: 1st Paragraph Page 365).
Claim 4 is rejected wherein monitoring the changes in ambient light over the period of time comprises sampling the ambient light at a sampling rate that is at least two times a frame rate of the television (i.e. Sample every 250ms to achieve a high recognition ration) (Section 8.3: Server-Side load).
Claim 5 is rejected wherein monitoring the changes in ambient light over the period of time comprises sampling the ambient light, and wherein generating the ambient-light data based on the monitoring comprises, for each sample of the ambient light, generating at least one corresponding data value indicating at least one change in the ambient light from an immediately preceding sample of the ambient light (Figure 5; Section 8.3: Server-Side load, Page 367: Deferred correlations: 2nd paragraph).
Claim 6 is rejected wherein the at least one data value indicates a change in intensity of the ambient light (i.e. a steep change in the illuminance of the measurement) (Figure 5; Section 8.3: Server-Side load, Page 367: Deferred correlations: 2nd paragraph).
Claim 7 is rejected wherein the at least one data value indicates whether the intensity of the ambient light increased, decreased, or was unchanged (i.e. a steep change in the illuminance of the measurement) (Figure 5; Section 8.3: Server-Side load, Page 367: Deferred correlations: 2nd paragraph).
Claim 8 is rejected wherein the at least one data value indicates a delta in intensity of the ambient light (i.e. a steep change in the illuminance of the measurement) (Figure 5; Section 8.3: Server-Side load, Page 367: Deferred correlations: 2nd paragraph).
Regarding claim 9, Schwittmann and Mafoodh, the combination teaches at least one data value indicates a change in color temperature of the ambient light (Mafoodh: Para. 0016, 0026-27).
Regarding claim 10, Schwittmann and Mafoodh, the combination teaches at least one data value indicates a change in color of the ambient light (Mafoodh: Para. 0016, 0026-27).
Claim 11 is rejected wherein repeating, on a sliding time window basis, the monitoring, providing, receiving, and using (Section 8.3: Server-Side load).
Claim 12 is rejected wherein the method is carried out by a computing system within the space (Figure 1; Abstract, Introduction: 2nd paragraph page 364, Approach: 1st Paragraph Page 365)
Regarding claim 13, Schwittmann and Mafoodh, the combination teaches limitations discussed with respect to claim 1. The combination teaches a computing system comprising: at least one ambient-light sensor; at least one processor; non-transitory data storage; and program instructions stored in the non-transitory data storage and executable by the at least one processor to carry out operations (Schwittmann: Abstract and Mafoodh: Figure 1) including:
receiving, from the ambient-light sensor, signaling representing the ambient light sensed over time in a space encompassing a visual media-presentation device, based on the signaling, monitoring changes in the ambient light over the period of time, and generating ambient-light data representing the monitored changes (i.e. data retrieved by sampling the ambient light sensor) (Schwittmann: Abstract, Introduction: 2nd paragraph page 364);
providing the generated ambient-light data as input to a trained machine-learning model (i.e. data retrieved by sampling the ambient light sensor and providing said data to a server) (Schwittmann: Figure 2; Introduction: 2nd paragraph page 364 and Mafoodh: Para. 0027-28);
receiving from the machine-learning model, based on the provided ambient-light data, a prediction of whether the visual media-presentation device was presenting visual media content during the period of time (i.e. the server can identify the video being played) (Schwittmann: Abstract, Introduction: 2nd paragraph page 364, Approach: 1st Paragraph Page 365 and Mafoodh: Para. 0027-28), and using the received prediction as a basis to control media-exposure measurement (i.e. data retrieved by sampling the ambient light sensor, providing said data to a server and the server can identify the video being played) (Schwittmann: Introduction: 2nd paragraph page 364, Approach: 1st Paragraph Page 365).
Claim 14 corresponds to claim 2.
Claim 15 corresponds to claim 3.
Claim 16 corresponds to claim 4.
Claim 17 corresponds to claims 5-6, 9-10.
Claim 18 corresponds to claim 1.
Claim 19 corresponds to claims 2 and 4.
Claim 20 corresponds to claims 5-6 and 9-10.
Conclusion
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/KUNAL LANGHNOJA/Primary Examiner, Art Unit 2425
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