DETAILED ACTION
The amendment to Application Ser. No. 18/644,566 filed on March 2, 2026, has been entered. Claims 9, 16 and 20 are cancelled. Claims 1, 4, 6-8, 10-12, 14-15 and 17 are currently amended. Claims 1-8, 10-15 and 17-19 are pending and are examined.
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 .
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.
Response to Arguments
The amendment to Claims 1, 4, 6-7, 11-12, 14-15, and 17, and the cancellation of Claims 9 and 20, respectively, has overcome the objection to the claims for minor informalities set forth in the Non-Final Office Action mailed October 29, 2025. The objection to the claims for minor informalities is hereby withdrawn.
The amendment to Claims 6-8, 10, 14 and 15 has overcome the rejection of Claims 6-8, 10, 14 and 15 under 35 U.S.C. 112(b) as being indefinite set forth in the Non-Final Office Action mailed October 29, 2025. The rejection of Claims 6-8, 10, 14 and 15 under 35 U.S.C. 112(b) is hereby withdrawn.
The arguments with respect to the rejection of Claims 1-20 under 35 U.S.C. 103 have been fully considered by the Examiner but are not persuasive.
Specifically, on page 9 of the response filed March 2, 2026, Applicant argues, “The Office Action alleges that Paliwal discloses the use of predictive models for bitrate selection. However, Paliwal's models are "forest (random forest) decision tree based" regressors or classifiers. Paliwal does not disclose or suggest the specific architecture of a Generative Adversarial Network (GAN). Therefore Paliwal does not disclose at least this element of amended claim 1. None of Moustafa, Knowler, MacInnis, or Huang remedy this deficiency.”
The Examiner respectfully disagrees. Contrary to Applicant’s assertion, Huang discloses using a generative adversarial network (GAN) to train agents, i.e., machine learning models, to perform ABR (Huang Abstract, Fig. 2 and § "1. Introduction" and "3. TIYUNTSONG'S MECHANISM").
New grounds of rejection under 35 U.S.C. 103, necessitated by the amendment, are set forth in this Office Action.
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.
Claims 1, 2, 6, 8, 10, 11, 14 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Moustafa et al., Pub. No. US 2016/0191594 A1, hereby “Moustafa”, in view of Paliwal et al., Pub. No. US 2023/0133880 A1, hereby “Paliwal”, and in further view of Huang et al., the paper titled “TIYUNTSONG: A SELF-PLAY REINFORCEMENT LEARNING APPROACH FOR ABR VIDEO STREAMING”, hereby “Huang”.
Regarding Claim 1, Moustafa discloses “A media streaming system (Moustafa fig. 2 and paragraph 25: adaptive streaming system 200) comprising:
a media server connected to a network (Moustafa fig. 2 and paragraph 25: server 103 connected to network 102); and
a bitrate controller connected to the network (Moustafa fig. 2 and paragraphs 26 and 30-35: media player module (MPM) 206 comprising adaptive logic module 210, wherein the MPM 206 is connected to network 102 via communications interface (COMMS) 260),
wherein the media server is configured to transmit a media stream to a client device connected to the network in a sequence of successive time periods along a chronological timeline (Moustafa fig. 2 and paragraphs 31-33: server 103 transmits content to client 201 as a stream of segments, wherein each segment corresponds to a short interval of play back time),
wherein the bitrate controller is configured to:
continuously monitor the network and obtain real-time network performance data indicating a current status of the network for each time period (Moustafa fig. 2 and paragraph 38: adaptive logic module 210 monitors the conditions of the network connection between client 201 and server 103 by querying network stack (NWS) 220 – i.e., obtains real-time network performance data indicating a current status of the network);
obtain real-time operating status data indicating a current operating status of the client device for each time period (Moustafa fig. 2 and paragraph 38: adaptive logic module 210 monitors the status of buffer 230 of client 201 by querying the buffer 230, i.e., obtains real-time operating status data indicating a current operating status of the client device);
... determine a bitrate for each time period... to optimize the bitrate for each time period, based on the network performance data and the operating status data corresponding to each time period... (Moustafa fig. 2 and paragraphs 39-40: adaptive logic module 210 determines a bit rate for the next segment of content to be streamed to client 201 based on the network conditions reported by NWS 220 and the status of buffer 230, i.e., determines a bit rate for the next short interval of play back time based on real-time network performance data and real-time client operating status data); and
cause the media server to transmit the media stream to the client device at the determined bitrate for each time period (Moustafa fig. 2 and paragraphs 33-36: MPM 206 transmits a content request that includes one or more streaming parameters that cause server 103 to stream content to client 201 at the determined bit rate).”
However, while Moustafa discloses determining the bit rate for the next segment of content to be streamed to the client based on the network conditions and the status of the buffer (Moustafa paragraphs 39-40), Moustafa does not explicitly disclose “apply a machine learning (ML) model to determine a bitrate for each time period, the ML model being configured to optimize the bitrate for each time period based on the network performance data and the operating status data corresponding to each time period, wherein the ML model is trained on a generative adversarial network (GAN) using historical network performance data and operating status data specific to the client device as training data (emphasis added)”.
In the same field of endeavor, Paliwal discloses “apply a machine learning (ML) model to determine a bitrate for each time period, the ML model being configured to optimize the bitrate for each time period, based on the network performance data and the operating status data corresponding to each time period... (Paliwal figs. 3, 4 and 9 and paragraphs 48, 55, 69-71 and 108-110: adaptive bitrate selector 402 uses predictive models 306, i.e., machine learning models trained using training data set 304, to determine a bitrate for the next chunk based on the new data 308 comprising network bandwidth 316 and buffer level 318)”.
It would have been obvious to one of ordinary skill in the art at the time of the effective filing to modify the system of Moustafa to use a predictive models to determine the bit rate for the next segment of content as taught by Paliwal. One of ordinary skill would have been motivated to combine using predictive models to determine the bit rate for the next segment of content to improve bit rate selection (Paliwal paragraph 43).
However, while Paliwal discloses training the inference models using training data including current and past network speed history, i.e., network performance data, and current buffer level, current and past rebuffer history, network connection type, WiFi performance and CPU performance specific to the streaming device, i.e., operating status data (Paliwal paragraphs 48-51 and 58), the combination of Moustafa and Paliwal does not explicitly disclose “apply a machine learning (ML) model to determine a bitrate for each time period, the ML model being configured to optimize the bitrate for each time period based on the network performance data and the operating status data corresponding to each time period, wherein the ML model is trained on a generative adversarial network (GAN) using historical network performance data and operating status data specific to the client device as training data (emphasis added).”
In the same field of endeavor, Huang discloses a self-play reinforcement learning (RL) method that utilizes a generative adversarial network (GAN) to train two agents, i.e., ML models, to perform Adaptive Bitrate (ABR) streaming (Huang Abstract, Fig. 2 and § "1. INTRODUCTION" and "3. TIYUNTSONG'S MECHANISM": agents, i.e., ML models are trained using a generative adversarial network).
One of ordinary skill in the art at the time of the effective filing would have been motivated to combine training the inference models using a generative adversarial network as taught by Huang. One of ordinary skill in the art would have been motivated to combine training the inference models using a generative adversarial network to improve the bitrate selection under different network conditions (Huang Abstract and § "1. INTRODUCTION" and "5. CONCLUSIONS AND FUTURE WORK").
Regarding Claim 2, the combination of Moustafa and Paliwal discloses all of the limitations of Claim 1.
Additionally, Moustafa discloses “wherein the current status of the network indicates a current network bandwidth available to the client device (Moustafa paragraph 38: conditions of the network connection include the bandwidth of the connection).”
Regarding Claim 6, the combination of Moustafa, Paliwal and Huang discloses all of the limitations of Claim 1.
Additionally, Moustafa discloses “wherein the media server is further configured to:
divide the media stream into a sequence of segments corresponding to the sequence of successive time periods (Moustafa paragraph 31: the content is encoded in segments and at a variety of different bit rates that cover relatively short aligned intervals of play back time),
wherein each one of the segments is transmitted to the client device at the determined bitrate for the time period including the segment (Moustafa paragraphs 31-36: server 103 streams each of the segments to client 201 at the bit rate determined by adaptive logic module 210 for the respective segment).”
Regarding Claim 8, the combination of Moustafa, Paliwal and Huang discloses all of the limitations of Claim 6.
Additionally, Paliwal discloses “wherein the media server is further configured to:
encode each one of the segments based on the determined bitrate for the time period including the segment, wherein each encoded segment is transmitted to the client device at the determined bitrate for the time period including the segment (Paliwal paragraphs 41, 44-46 and 69: the next chunk may be encoded on-the-fly and transmitted to media device 106 at the determined bitrate).”
It would have been obvious to one of ordinary skill in the art at the time of the effective filing to modify the system of Moustafa to encode the next segment on the fly at the determined bit rate as taught by Paliwal because doing so constitutes applying a known technique (on-the-fly encoding of content chunks) to known devices and/or methods (a server providing streaming content) ready for improvement to yield predictable and desirable results (encoding of the next segment at the determined bit rate). See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Regarding Claim 10, the combination of Moustafa, Paliwal and Huang discloses all of the limitations of Claim 6.
Additionally, Moustafa discloses “wherein the bitrate controller is further configured to:
generate commands to transmit each one of the segments to the client device at the determined bitrate for the time period including the segment (Moustafa paragraphs 32-35: while not explicitly stated, generation of the content requests by MPM 206 before transmission of the content requests to server 201 is inferred); and
transmit the commands to the media server (Moustafa paragraphs 32-35: MPM 206 causes client 201 to transmit content requests to server 201 that cause server 201 to stream segments of the content to client 201 at the bit rate determined for each segment).”
Insofar as it recites similar claim elements, Claim 11 is rejected for substantially the same reasons presented above with respect to Claim 1.
Additionally, Moustafa discloses “A bitrate controller device connected to a media server configured to transmit a media stream to a client device via a network in a sequence of successive time periods along a chronological timeline (Moustafa fig. 2 and paragraphs 26 and 30-35: client device 201 implementing media player module (MPM) 206 comprising adaptive logic module 210, which is connected by network 102 to server 103 that transmits content to client 201 as a stream of segments), the bitrate controller device comprising:
one or more processors (Moustafa fig. 2 and paragraphs 26-27: processor 203); and
a computer-readable storage media storing computer-executable instructions... (Moustafa fig. 2 and paragraphs 26-28 and 30: memory 204 comprising computer readable instructions which when executed by processor 203 causes the client device 201 to perform operations to implement content streaming operations, either alone or in combination with server 103 – while not explicitly stated, implementation of MPM 206 using computer readable instructions executable by processor 203 is inferred)”.
Insofar as it recites similar claim elements, Claim 14 is rejected for substantially the same reasons presented above with respect to Claim 6.
Insofar as it recites similar claim elements, Claim 17 is rejected for substantially the same reasons presented above with respect to Claim 1.
Additionally, Moustafa discloses “A method for transmitting a media stream from a media server to a client device via a network in a sequence of successive time periods along a chronological timeline... (Moustafa paragraph 1 and 16-17: a method for context aware media streaming)”.
Claims 3, 4, 7, 12, 15 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Moustafa, Paliwal and Huang in view of MacInnis, Pub. No. US 2016/0134673 A1.
Regarding Claim 3, the combination of Moustafa, Paliwal and Huang discloses all of the limitations of Claim 2.
However, while Moustafa discloses that the network conditions monitored by querying the network stack may also include the latency of the connection as well as the number of packets dropped (Moustafa paragraph 38), the combination of Moustafa, Paliwal and Huang does not explicitly disclose “wherein the current status of the network further indicates a current latency, a current round trip time (RTT), and a current packet loss rate pertaining to the network (emphasis added).”
In the same field of endeavor, MacInnis discloses “wherein the current status of the network further indicates a current latency, a current round trip time (RTT), and a current packet loss rate pertaining to the network (MacInnis figs. 2A and 3B and paragraphs 36 and 46: monitored performance characteristics of the network used to determine the bit rate for a next segment of streaming media include latency, round trip time, and packet loss rates).”
It would have been obvious to one of ordinary skill in the art at the time of the effective filing to modify the system of Moustafa, as modified by Paliwal and Huang, to determine the bit rate of the next segment of content to be streamed based in part on the round trip time of the connection between the client and the server as taught by MacInnis because doing so constitutes applying a known technique (selecting a bit rate for a next segment based in part on round trip time) to known devices and/or methods (a server providing streaming content) ready for improvement to yield predictable and desirable results (determining the bit rate of the next segment based in part on the bandwidth, latency, round trip time and packet loss of the connection between the client and the server). See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Regarding Claim 4, the combination of Moustafa, Paliwal and Huang discloses all of the limitations of Claim 1.
However, while Moustafa discloses monitoring the buffer to determine the buffer status/capacity, i.e., an indication of the current playback status of the streaming content (Moustafa paragraph 38), the combination of Moustafa, Paliwal and Huang does not explicitly disclose “wherein the current operating status indicates a current playback status of the media stream, and an available processing capacity and an available memory capacity of the client device (emphasis added).”
In the same field of endeavor, MacInnis discloses “wherein the current operating status indicates a current playback status of the media stream, and an available processing capacity and an available memory capacity of the client device (MacInnis figs. 2A and 3B and paragraphs 36 and 46: performance characteristics of client 300 used to determine the bit rate for a next segment of streaming media include buffer space, i.e., an indication of playback status, as well as processor load and memory usage, i.e., indications of available processing and memory capacity).”
It would have been obvious to one of ordinary skill in the art at the time of the effective filing to modify the system of Moustafa, as modified by Paliwal and Huang, to determine the bit rate of the next segment of content to be streamed based in part on client processor load and memory utilization as taught by MacInnis because doing so constitutes applying a known technique (selecting a bit rate for a next segment based in part on processor load and memory utilization) to known devices and/or methods (a server providing streaming content) ready for improvement to yield predictable and desirable results (determining the bit rate of the next segment based in part on the buffer status, processor load and memory capacity of the client). See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Regarding Claim 7, the combination of Moustafa, Paliwal and Huang discloses all of the limitations of Claim 6.
However, while Moustafa discloses determining the bit rate for the next segment of content to be streamed to the client based on the network conditions and the status of the buffer (Moustafa paragraphs 39-40), the combination of Moustafa, Paliwal and Huang does not explicitly disclose “wherein a bitrate for a selected one of the segments is determined based on the current status of the network and the current operating status of the client device corresponding to a segment preceding the selected one of the segments.”
In the same field of endeavor, MacInnis discloses “wherein a bitrate for a selected one of the segments is determined based on the current status of the network and the current operating status of the client device corresponding to a segment preceding the selected one of the segments (MacInnis fig. 3B and paragraphs 45-47 and 50: client 300 determines the bit rate of a next segment to be requested, i.e., a selected one of the segments, based on monitoring the performance of the network and performance of the client during processing of the current segment, i.e., the segment preceding the selected segment - see feedback path from “receive segment 336” to “network performance 322” shown in figure 3B).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing to modify the system of Moustafa, as modified by Paliwal and Huang, to determine the bit rate of the next segment based on the buffer status and network conditions corresponding to the processing of the current segment as taught by MacInnis because doing so constitutes applying a known technique (selecting a bit rate for a next segment based on network and device performance during processing of a current segment) to known devices and/or methods (a server providing streaming content) ready for improvement to yield predictable and desirable results (determining the bit rate of the next segment based buffer status and network conditions monitored during processing of the current segment). See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Regarding Claim 12, the combination of Moustafa, Paliwal and Huang discloses all of the limitations of Claim 11.
Additionally, Moustafa discloses “wherein the current status of the network indicates a current network bandwidth available to the client device... (Moustafa paragraph 38: conditions of the network connection include the bandwidth of the connection)”.
However, while Moustafa discloses monitoring the buffer to determine the buffer status/capacity, i.e., an indication of the current playback status of the streaming content (Moustafa paragraph 38), the combination of Moustafa, Paliwal and Huang does not explicitly disclose “wherein the current status of the network indicates a current network bandwidth available to the client device, and the current operating status indicates a current playback status of the media stream, and an available processing capacity and an available memory capacity of the client device (emphasis added).”
In the same field of endeavor, MacInnis discloses “wherein... the current operating status indicates a current playback status of the media stream, and an available processing capacity and an available memory capacity of the client device (MacInnis figs. 2A and 3B and paragraphs 36 and 46: performance characteristics of client 300 used to determine the bit rate for a next segment of streaming media include buffer space, i.e., an indication of playback status, as well as processor load and memory usage, i.e., indications of available processing and memory capacity).”
It would have been obvious to one of ordinary skill in the art at the time of the effective filing to modify the device of Moustafa, as modified by Paliwal and Huang, to determine the bit rate of the next segment of content to be streamed based in part on client processor load and memory utilization as taught by MacInnis because doing so constitutes applying a known technique (selecting a bit rate for a next segment based in part on processor load and memory utilization) to known devices and/or methods (a server providing streaming content) ready for improvement to yield predictable and desirable results (determining the bit rate of the next segment based in part on the buffer status, processor load and memory capacity of the client). See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Insofar as it recites similar claim elements, Claim 15 is rejected for substantially the same reasons presented above with respect to Claim 7.
Insofar as it recites similar claim elements, Claim 18 is rejected for substantially the same reasons presented above with respect to Claim 12.
Claims 5, 13 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Moustafa, Paliwal and Huang in view of Knowler et al., Pub. No. US 2021/0044641 A1, hereby “Knowler”.
Regarding Claim 5, the combination of Moustafa, Paliwal and Huang discloses all of the limitations of Claim 1.
However, while Moustafa discloses obtaining, by the adaptive logic module, the current status of the playback buffer of the client, i.e., real-time operating status data indicating a current operating status of the client device (Moustafa paragraph 38), the combination of Moustafa, Paliwal and Huang does not explicitly disclose “wherein the bitrate controller is further configured to: continuously receive a sequence of status messages periodically generated by and sent from the client device, wherein the status messages are timestamped and respectively corresponding to the time periods, each one of the status messages indicates the current operating status of the client device for the corresponding time period.”
In the same field of endeavor, Knowler discloses “continuously receive a sequence of status messages periodically generated by and sent from the client device, wherein the status messages are timestamped and respectively corresponding to the time periods, each one of the status messages indicates the current operating status of the client device for the corresponding time period (Knowler figs. 1, 3 and 4a and paragraphs 30-31, 47, 51 and 56: server 107 periodically receives playback packets generated by reporter module 204 executing on client device 102, the playback packets comprising player status or state information 210 and time information 222 comprising one or more timestamps indicating the time period corresponding to the state information).”
It would have been obvious to one of ordinary skill in the art at the time of the effective filing to modify the system of Moustafa, as modified by Paliwal and Huang, to receive, periodically by the adaptive bitrate logic, messages comprising player state information and timestamps indicating the time period corresponding to the player state information as taught by Knowler because doing so constitutes a simple substitution of one known element (pulling buffer state information by querying) for another (periodically receiving pushed state information) to obtain predictable and desirable results (monitoring the state of the playback buffer). See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Insofar as they recite similar claim elements, Claims 13 and 19 are rejected for substantially the same reasons presented above with respect to Claim 5.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kasal et al., Pub. No. US 2022/0150560 A1, discloses a method to generate personalize data-streaming for a multimedia playback device wherein an ABR model, such as a generative adversarial network, is trained on historical data; and
Yousef et al., Pub. No. US 2022/0337897 A1, discloses a method for playing content streamed in a peer-to-peer network wherein a machine learning model approximating ABR logic is trained using training data collected from the client device.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office Action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM C MCBETH whose telephone number is (571)270-0495. The examiner can normally be reached on Monday - Friday, 8:00AM - 4:30PM ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vivek Srivastava can be reached on 571-272-7304. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/WILLIAM C MCBETH/Examiner, Art Unit 2449
/VIVEK SRIVASTAVA/Supervisory Patent Examiner, Art Unit 2449
Prosecution Insights