DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 6 and 13 are objected to because they recite “the most recently received request” and appears it may be intended to read “the most recently received requests.”
Claims 4, 5, 11, 12, 18, and 19 are objected to as being dependent upon a rejected base claim, but would be allowable (subject to the below §112 rejection of claims 5, 12 and 19) if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 3, 5, 10, 12, 17, and 19 are rejected under 35 U.S.C. 112(b), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. The claims recite “the timing measure” without antecedent basis.
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, 6-10, 13-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Abeysooriya et al., US 9,336,483 in view of Fan, US 2007/0002743.
1, 8, 15. Abeysooriya teaches a method performed by a communication device, wherein the method comprises:
a memory, processor, and instructions [Figs. 1, 2, 5, col. 29] to:
detecting, by the communication device [e.g. content management server, Figs. 1, 4, 7], a series of requests for streaming media content [uses’ previous interactions are detected/recorded over a time interval, Figs. 4, 7, col. 15, 36-65; col. 19, 1-14; col. 21, 4-27; col. 23, 26-55];
generating, by the communication device, a set of feature values based on times that the requests for the streaming media content were issued [data structure is generated; e.g. for an on-demand media system the set of feature values includes demographics, past selections, usage times, etc.; steps 701-703, Fig. 7; cols. 15, 36-65; col. 18, 29-48; col. 19, 1-14, col. 21, 40-54];
providing, by the communication device, the set of feature values as input to a machine learning model, wherein the machine learning model has been trained to predict a time that a future request for media content will be issued based on input data indicating times that a sequence of previous requests for media content were issued [Figs. 7, 8, e.g. step 703; cols. 15, 36-65; col. 19, 1-14; cols. 21-22, ll. 40-17];
receiving, by the communication device, output that the machine learning model generated based on input of the set of feature values, the output indicating a predicted time of a subsequent request for the streaming media content or a predicted time to request bandwidth allocation for the subsequent request [output includes predicts timing of future user request and bandwidth usage times/patterns, Fig. 8, e.g. steps 801-803; cols. 15, 36-65; col. 10, 1-14; col. 19, 1-14; cols. 21-22, ll. 40-17].
Abeysooriya is silent on sending a bandwidth allocation request in based on predictive output. Fan teaches a system wherein, based on the output generated by a predictive model, sending, by the communication device, a bandwidth allocation request to allocate bandwidth to transmit data in a wireless network [allocation request is sent in response to evaluating predicted future bandwidth, Fig. 4, paras. 13, 19].
Before the effective filing date of the claimed invention, it would have been obvious to one skilled in the art to combine the references, sending allocation requests that correspond to anticipated bandwidth requirements in order to schedule content accordingly. For example, content can be pre-downloaded to clients during periods when bandwidth usage is generally lower according to predicted patterns.
2, 9, 16. Abeysooriya teaches the method of claim 1, wherein the communication device provides network connectivity to a client device during playback of the streaming media content [e.g. CMS 102 manages content/communication with clients, Fig. 1, 4, 7]; and
wherein the series of requests comprises multiple requests for the streaming media content from the client device that are spaced apart in time [predictive data, e.g. user requests, are spread over a periodic time interval; col. 15, 36-65; col. 23, 26-55], and wherein the set of feature values indicates a timing measure for each request in a group of consecutive requests from the series of requests [values tracked include usage patterns, i.e. a group of requests with relative timing, col. 15, 36-65; col. 23, 26-55], wherein the group of consecutive requests includes the request for the streaming media content from the client device issued most recently before generating the set of feature values [most recent batch of input data is provided, col. 23, 26-55].
3, 10, 17. Abeysooriya the method of claim 1, wherein the timing measure for a particular request comprises a measure of an amount of time that elapsed between the particular request and a reference time [e.g. the time of a previous usage (e.g. col. 19, 10-29) is inherently, necessarily measured relative to some reference time; e.g. a request with a recorded time of 4:15 pm means 4.25 hours later than noon, while 5:15 is 5.25 hours greater than noon; without a reference time (e.g. the previous midnight or GMT), measured time values would be meaningless and could not be said to create a “usage pattern” as described in Abeysooriya; for these reasons the generally recited “reference time”—without any further definition—is inherently taught in Abeysooriya; this would have been recognized by anyone familiar with timing principles].
6, 13, 20. Abeysooriya teaches the method of claim 1, wherein the set of features values indicates a timing measure for each of a predetermined number of consecutive requests in the most recently received request for the streaming media content [usage patterns (timing data) are tracked over a predetermined time interval and the most recent requests are included, col. 15, 36-65, col. 19, 1-14; col. 23, 26-55].
7, 14. Abeysooriya teaches the method of claim 1, wherein the streaming media content is a video [col. 4, 36-49]; and
wherein the communication device is configured to repeatedly predict the timing of future requests for content of the video during a session of playback of the video [output includes predicts timing of future user request and bandwidth usage times/patterns, Fig. 8, e.g. steps 801-803; cols. 15, 36-65; col. 10, 1-14; col. 19, 1-14; cols. 21-22, ll. 40-17], including by:
detecting when requests for content of the video are issued [uses’ previous interactions are detected/recorded over a time interval, Figs. 4, 7, col. 15, 36-65; col. 19, 1-14; col. 21, 4-27; col. 23, 26-55]; and
for each request detected, using the machine learning model to predict (i) a time that a next request for content of the video will be issued during the session of playback of the video [output includes predicts timing of future user request and bandwidth usage times/patterns, Fig. 8, e.g. steps 801-803; cols. 15, 36-65; col. 10, 1-14; col. 19, 1-14; cols. 21-22, ll. 40-17] or (ii) a time to send a next bandwidth allocation request.
Conclusion
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/TIMOTHY R NEWLIN/ Examiner, Art Unit 2424