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
Claims 1-20 are pending.
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 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.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/4/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Nonstatutory 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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-22 of US Pat. 11,800,167. Although the conflicting claims are not identical, they are not patentably distinct from each other because they are directed to substantially similar methods, systems, and media. For example, contrast instant claims 1 and 2 with claim 1 of ‘167:
Instant Claim 1
‘167 claim 1
A computer-implemented method for adaptive bitrate selection, comprising:
A method performed by a system having at least a processor and a memory therein, wherein the method comprises:
receiving, by at least one computer processor, a data streaming request;
receiving, by a client device, a data streaming request;
predicting, by a speed predictive machine learning model and based on one or more streaming parameters, a current sustainable network bandwidth, wherein the speed predictive machine learning model is trained using a training data set comprising a history of the one or more streaming parameters;
predicting, by a speed predictive machine learning model and based on one or more streaming parameters, a current sustainable network bandwidth, wherein the speed predictive machine learning model is trained using a training data set comprising a history of the one or more streaming parameters;
predicting, by a rebuffer predictive machine learning model and based on the current sustainable network bandwidth, a buffer level of a data buffer, and N available discrete bitrates, a candidate bitrate at which a likelihood of rebuffering of the data buffer occurs less than a threshold percentage;
(claim 2) wherein the predicting the candidate bitrate further comprises: predicting, by the rebuffer predictive machine learning model and based further on a chunk duration, the candidate bitrate at which the likelihood of rebuffering of the data buffer occurs less than the threshold percentage.
predicting, by a rebuffer predictive machine learning model and based on the current sustainable network bandwidth, a buffer level of a data buffer, a chunk duration, and N available discrete bitrates, a candidate bitrate at which a likelihood of rebuffering of the data buffer occurs less than a threshold percentage;
selecting, based on the candidate bitrate, a download bitrate to complete the data streaming request;
selecting, based on the candidate bitrate, a download bitrate to complete the data streaming request;
and downloading streaming data at the download bitrate.
and downloading, by the client device, streaming data at the download bitrate.
Further, remaining instant claims 3-20 correspond with subject matter disclosed by claims 2-22 of ‘167.
Therefore, if a patent were to be granted, it may result in an improper timewise extension of the “right to exclude” of the subject matter and may lead to possible harassment by multiple assignees.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of US Pat. 12,137,265. Although the conflicting claims are not identical, they are not patentably distinct from each other because they are directed to substantially similar methods, systems, and media; see particularly the rejections above and further the Terminal Disclaimer between US Pat. 12,137,265 and US Pat. 11,800,167 and the accompanying Double Patenting rejections presented in the case history of Pat. 12,137,265.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 6-9, and 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Jana et al (US Pub. No. 2016/0234078; cited on IDS), hereafter, “Jana,” in view of Dai et al (US Pub. No. 2022/0141513; cited on IDS), hereafter, “Dai.”
As to claim 1, Jana discloses a computer-implemented for adaptive bitrate selection (Abstract):
receiving, by at least one computer processor, a data streaming request ([0044], particularly, “In another embodiment, the bandwidth predictor 115 can generate predictions for available bandwidth for an end user device 101 in response to a request to do so, such as from the end user device 101 and/or from a streaming server 150 that streams content, such as video content, to the end user device 101.”);
predicting, by a speed predictive machine learning model and based on one or more streaming parameters, a current sustainable network bandwidth, wherein the speed predictive machine learning model is trained using a training data set comprising a history of the one or more streaming parameters ([0057], particularly, “At 404, the predicted available bandwidth for the end user device 101 can be obtained, such as accessing the predicted available bandwidth via an API of the bandwidth predictor 115. In one embodiment, the information accessible can be limited to the predicted available bandwidth without accessing network performance data from which the predicted available bandwidth was calculated.” And [0063], particularly, “For example, an Auto-Regressive Integrated Moving Average (ARIMA) model can be implemented to predict the next second: the algorithm can fit the best ARIMA with historical data, and it can use a sliding window as the training dataset. As an example, the window size can be set to 15 s.”; “predicted available bandwidth” reading on “a current sustainable network bandwidth” and “network performance data” reading “one or more streaming parameters”; “Auto-Regressive Integrated Moving Average (ARIMA) model” reading on “machine learning model”);
predicting, by a rebuffer predictive model and based on the current sustainable network bandwidth, a buffer level of a data buffer, and N available discrete bitrates, a candidate bitrate at which a likelihood of rebuffering of the data buffer occurs less than a threshold percentage ([0058]-[0059], particularly, “For example, the buffer occupancy can be determined and compared to a buffer threshold to identify whether the buffer occupancy is in a first zone which has a higher likelihood of buffer drain as compared to a second zone which has a lower likelihood of buffer drain. The threshold can be determined based on various factors including the total buffer capacity, the type of media content, and so forth. At 408, a bit rate for a portion of media content to be streamed over a network (e.g., a wireless cellular network) to the end user device 101 can be determined according to the predicted available bandwidth and according to the buffer occupancy.”, [0031] describes “multiple bit rates” i.e. “N available discrete bitrates” as well as [0056], describing, “For instance, seven bits can be utilized for a floating number and one bit for a handover. As an example, seven bits can be divided into integer and fractional parts, three bits for integer and four bits for fractional, providing a precision of 0.063 Mbps and a maximum value of 8.94 Mbps, which is sufficient for video streaming bit rate selection with a range between 0.2 to 4 Mbps”);
selecting, based on the candidate bitrate, a download bitrate to complete the data streaming request ([0059], particularly, “At 408, a bit rate for a portion of media content to be streamed over a network (e.g., a wireless cellular network) to the end user device 101 can be determined according to the predicted available bandwidth and according to the buffer occupancy…The determined bite rate can be applied at 410 during the streaming process.”); and
downloading streaming data at the download bitrate ([0059], particularly, “At 408, a bit rate for a portion of media content to be streamed over a network (e.g., a wireless cellular network) to the end user device 101 can be determined according to the predicted available bandwidth and according to the buffer occupancy…The determined bite rate can be applied at 410 during the streaming process.”).
However, Jana does not explicitly disclose the rebuffer predictive model is a rebuffer predictive machine learning model.
But, Dai discloses predicting, by a rebuffer predictive machine learning model and based on the current sustainable network bandwidth and a buffer level of a data buffer, a candidate bitrate at which a likelihood of rebuffering of the data buffer occurs less than a threshold percentage ([0057]-[0058], particularly, “For example, network metrics prediction 604 includes a network bandwidth 606-1, a rebuffer ratio 606-2, and a failure ratio 606-3...Also, rebuffer ratio 606-2 and failure ratio 606-3 may be similar to rebuffer ratios and failure ratios for features that were similar to current session features 602. Network metrics predictor 302 then outputs network metrics prediction 604 to profile decision generator 304… After training, network conditions prediction model and a profile subset decision prediction model are generated and can be used during real-time execution.”).
Therefore it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the application to combine the teachings of Jana with Dai in order to provide an accurate of forecasting buffering issues.
As to claim 9 and 17, they are rejected by a similar rationale set forth in claim 1’s rejection.
As to claim 6 and 14, the teachings of Jana and Dai as combined for the same reasons set forth in claim 1’s rejection further disclose retraining the speed predictive machine learning model based on the current sustainable network bandwidth (Jana, [0057], particularly, “At 404, the predicted available bandwidth for the end user device 101 can be obtained, such as accessing the predicted available bandwidth via an API of the bandwidth predictor 115. In one embodiment, the information accessible can be limited to the predicted available bandwidth without accessing network performance data from which the predicted available bandwidth was calculated.” And [0063], particularly, “For example, an Auto-Regressive Integrated Moving Average (ARIMA) model can be implemented to predict the next second: the algorithm can fit the best ARIMA with historical data, and it can use a sliding window as the training dataset. As an example, the window size can be set to 15 s.”); and retraining the rebuffer predictive machine learning model based on the candidate bitrate (Jana, [0058]-[0059], particularly, “For example, the buffer occupancy can be determined and compared to a buffer threshold to identify whether the buffer occupancy is in a first zone which has a higher likelihood of buffer drain as compared to a second zone which has a lower likelihood of buffer drain. The threshold can be determined based on various factors including the total buffer capacity, the type of media content, and so forth. At 408, a bit rate for a portion of media content to be streamed over a network (e.g., a wireless cellular network) to the end user device 101 can be determined according to the predicted available bandwidth and according to the buffer occupancy.” And Dai, [0057]-[0058], particularly, “For example, network metrics prediction 604 includes a network bandwidth 606-1, a rebuffer ratio 606-2, and a failure ratio 606-3...Also, rebuffer ratio 606-2 and failure ratio 606-3 may be similar to rebuffer ratios and failure ratios for features that were similar to current session features 602. Network metrics predictor 302 then outputs network metrics prediction 604 to profile decision generator 304… After training, network conditions prediction model and a profile subset decision prediction model are generated and can be used during real-time execution.”).
As to claim 7 and 15, the teachings of Jana and Dai as combined for the same reasons set forth in claim 1’s rejection further disclose in response to the likelihood of rebuffering of the data buffer being over a threshold percentage, selecting a next lower available bitrate until the likelihood of rebuffering of the data buffer is under the threshold percentage (Jana, [0058]-[0059], particularly, “For example, the buffer occupancy can be determined and compared to a buffer threshold to identify whether the buffer occupancy is in a first zone which has a higher likelihood of buffer drain as compared to a second zone which has a lower likelihood of buffer drain. The threshold can be determined based on various factors including the total buffer capacity, the type of media content, and so forth. At 408, a bit rate for a portion of media content to be streamed over a network (e.g., a wireless cellular network) to the end user device 101 can be determined according to the predicted available bandwidth and according to the buffer occupancy).
As to claim 8 and 16, the teachings of Jana and Dai as combined for the same reasons set forth in claim 1’s rejection further disclose the one or more streaming parameters further comprise any of: N previous bitrates, where N represents a number of most recent bitrates in a history of previous download bitrates (Jana, [0063], particularly, “For example, an Auto-Regressive Integrated Moving Average (ARIMA) model can be implemented to predict the next second: the algorithm can fit the best ARIMA with historical data, and it can use a sliding window as the training dataset. As an example, the window size can be set to 15 s.”); an average speed of the N previous bitrates; a standard deviation of the N previous bitrates; or a chunk duration.
Allowable Subject Matter
Claims 2-5, 10-13, and 18-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims while alleviating the Nonstatutory Double Patenting rejections.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS J DAILEY whose telephone number is (571)270-1246. The examiner can normally be reached 9:30am-6:00pm.
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/THOMAS J DAILEY/ Primary Examiner, Art Unit 2458