DETAILED ACTION
This Office Action is in response to the amendment to Application Ser. No. 18/644,566 filed on August 4, 2026. Claims 9, 16 and 20 are cancelled. Claims 1, 11 and 17 are currently amended. Claims 1-8, 10-15 and 17-19 are pending and are examined.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 August 4, 2026, has been entered.
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 arguments with respect to the rejection of Claims 1-8, 10-15 and 17-19 under 35 U.S.C. 103 have been fully considered by the Examiner.
On pages 8-9 of the response filed August 4, 2026, Applicant argues, “Amended claim 1 recites, in relevant part, applying a machine learning (ML) model to determine a bitrate for 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.’ The Specification as filed details that training data is gathered specifically for the individual client device to optimize model decisions for that specific device. Specifically, ‘AI/ML models can be trained, validated using historical network performance data and operating status data specific to the client device.’ Specification, par. [0018]. Furthermore, ‘The process 600 begins with providing training data, such as historical streaming environment data, historical network performance data, and historical device operating status data, etc. at 610, whether labeled or unlabeled. The historical data pertains to the specific client device.’ Id., par. [0084] (emphasis added).
The Office Action asserts that Huang discloses using a generative adversarial network (GAN) to train ML models/agents to perform ABR streaming. However, Huang describes that its training data consists of general, public network trace datasets. Section 4.1 (“Experimental Setup”) of Huang states: ‘We collect about 2,300 network traces from different public datasets for training and evaluating Tiyuntsong. The details of our network traces are composed of Norway [15], Synthetic Network Traces [6], Belgium [16], FCC [6], and Oboe [7].’ Huang, Sec. 4.1, emphasis added. Thus, the training data is not specific to the client device, as recited in amended claim 1, but rather some publicly available data set. None of Moustafa, MacGinnis, Paliwal, nor Knowler remedy this deficiency.”
The Examiner respectfully disagrees. Paragraph 58 of Paliwal states:
“In some embodiments, the technology described herein may implement machine learning model training based on a specific streaming platform or streaming device. For example, different products may have different hardware capabilities such as Wi/Fi, chips, drives, CPU performance, etc. All of these factors may influence speed selections. Therefore, the platform and/or streaming device may train and deploy the machine learning models per platform or per device. The ML system may collect the specifics for each platform or device, or may cluster based on hardware capabilities, and then apply the machine learning models (emphasis added).”
Contrary to Applicant’s assertion, Paliwal suggests training the machine learning model for a specific streaming device using historical data of that device.
Nevertheless, new grounds of rejection under 35 U.S.C. 103, necessitated by the amendment, are set forth in this Office Action.
Claim Interpretation
“The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met.” See MPEP 2111.04 II.
Regarding method Claim 17, the following limitations recite steps that are performed only upon certain conditions being met:
“estimate an effective bandwidth available for streaming based on the available network bandwidth and the consumption playback rate, wherein when the available network bandwidth exceeds the consumption playback rate, the bitrate controller allocates a portion of the available network bandwidth for streaming the media stream at the determined bitrate and reserves a remaining portion of the available network bandwidth for non-streaming network activities”.
Given its broadest reasonable interpretation, Claim 17 does not require the action “the bitrate controller allocates a portion of the available network bandwidth for streaming the media stream at the determined bitrate and reserves a remaining portion of the available network bandwidth for non-streaming network activities” to be performed as it is subject to a condition, i.e., the available network bandwidth exceeding the consumption playback rate, that is not required by the claim to occur.
8. While the broadest reasonable interpretation of Claim 17 does not require the performance of steps which are contingent upon conditions that are not required to be met, for the purposes of compact prosecution, insofar as the recited limitations are definite, prior art has been applied to each limitation in this Office Action as if each step is required to be performed.
Claim Objections
The claims are objected to because of the following informalities:
regarding Claim 17, the term “estimate” recited in line 16 should be “estimating”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
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 1-8, 10-15 and 17-19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation “estimate an effective bandwidth available for streaming based on the available network bandwidth and the consumption playback rate, wherein when the available network bandwidth exceeds the consumption playback rate, the bitrate controller allocates a portion of the available network bandwidth for streaming the media stream at the determined bitrate and reserves a remaining portion of the available network bandwidth for non-streaming network activities” in lines 18-23. There is insufficient antecedent basis for the term “the consumption playback rate” in the claims. Additionally, the relationship between “an effective bandwidth available for streaming” that is estimated and “the determined bitrate [for each time period]” that is determined by applying the machine learning (ML) model is unclear, rendering the claim indefinite.
Dependent Claims 2-8 and 10 are rejected for the reasons presented above with respect to rejected Claim 1 in view of their dependence thereon.
Additionally, Claim 2 recites the limitation “wherein the current status of the network indicates a current network bandwidth available to the client device” in lines 1-2. The relationship between “a current network bandwidth available to the client device” recited in Claim 2 and “an available network bandwidth” recited in Claim 1 is unclear, rendering the claim indefinite. Specifically, it is unclear whether a distinction should be drawn between the terms or whether the terms are being used interchangeably to refer to the same element.
Dependent Claim 3 is rejected for the reasons presented above with respect to rejected Claim 2 in view of its dependence thereon.
Insofar as it recites similar claim elements, Claim 11 is rejected for substantially the same reasons presented above with respect to Claim 1.
Dependent Claims 12-15 are rejected for the reasons presented above with respect to rejected Claim 11 in view of their dependence thereon.
Additionally, insofar as it recites similar claim elements, Claim 12 is rejected for substantially the same reasons presented above with respect to Claim 2.
Insofar as it recites similar claim elements, Claim 17 is rejected for substantially the same reasons presented above with respect to Claim 1.
Dependent Claims 18 and 19 are rejected for the reasons presented above with respect to rejected Claim 17 in view of their dependence thereon.
Additionally, insofar as it recites similar claim elements, Claim 18 is rejected for substantially the same reasons presented above with respect to Claim 2.
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”, and in further view of Rodbro et al., Pub. No. US 2011/0312283 A1, hereby “Rodbro”.
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, wherein the current status of the network indicates an available network bandwidth (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, wherein the conditions include the bandwidth of the connection);
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, wherein the ML model is trained... using historical network performance data and operating status data specific to the client device as training data (Paliwal figs. 3, 4 and 9 and paragraphs 48, 55, 58, 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);
estimate an effective bandwidth available for streaming based on the available network bandwidth and the consumption playback rate... (Paliwal paragraphs 18, 48, 55, 62-63, 66, 99-103 and 107-108: a sustainable bitrate for streaming is predicted based in part on network bandwidth 316 and buffer level 318, which is an indirect measure of the client playback rate)”.
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 (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).
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, to train 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").
However, while Paliwal discloses predicting a suitable bitrate for streaming based in part on network bandwidth and client buffer level (Paliwal paragraphs 18, 48, 55, 62-63, 66, 99-103 and 107-108), the combination of Moustafa, Paliwal and Huang does not explicitly disclose “estimate an effective bandwidth available for streaming based on the available network bandwidth and the consumption playback rate, wherein when the available network bandwidth exceeds the consumption playback rate, the bitrate controller allocates a portion of the available network bandwidth for streaming the media stream at the determined bitrate and reserves a remaining portion of the available network bandwidth for non-streaming network activities (emphasis added)”.
In the same field of endeavor, Rodbro discloses “wherein when the available network bandwidth exceeds the consumption playback rate, the bitrate controller allocates a portion of the available network bandwidth for streaming the media stream at the determined bitrate and reserves a remaining portion of the available network bandwidth for non-streaming network activities (Rodbro paragraphs 19, 29, 37-38 and Claims 1, 10 and 11: “This means that the bandwidth requirement (BW_RQ(1)) for the real-time application 1061 is reserved and then the remaining ‘slack’ bandwidth is divided over the other applications.”).”
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 reserve excess bandwidth for other applications when the bandwidth of the connection exceeds the bandwidth required for streaming at the determined bitrate as taught by Rodbro. One of ordinary skill in the art would have been motivated to combine reserving excess bandwidth for other applications when the bandwidth of the connection exceeds the bandwidth required for streaming at the determined bitrate to allow non real-time applications access to the network using as much bandwidth as is spare once the streaming application has the bandwidth needed for streaming at the determined bitrate (Rodbro paragraph 38).
Regarding Claim 2, the combination of Moustafa, Paliwal, Huang and Rodbro 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, Huang and Rodbro 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, Huang and Rodbro 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, Huang and Rodbro 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, Huang and Rodbro in view of MacInnis, Pub. No. US 2016/0134673 A1.
Regarding Claim 3, the combination of Moustafa, Paliwal, Huang and Rodbro 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, Huang and Rodbro 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, Huang and Rodbro, 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, Huang and Rodbro 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, Huang and Rodbro 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, Huang and Rodbro, 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, Huang and Rodbro 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, Huang and Rodbro 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, Huang and Rodbro, 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, Huang and Rodbro 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, Huang and Rodbro 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, Huang and Rodbro, 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, Huang and Rodbro in view of Knowler et al., Pub. No. US 2021/0044641 A1, hereby “Knowler”.
Regarding Claim 5, the combination of Moustafa, Paliwal, Huang and Rodbro 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, Huang and Rodbro 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, Huang and Rodbro, 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:
Chen et al., Pub. No. US 2011/0268428 A1, discloses a technique for dynamically adjusting streaming media bitrates wherein the content player estimates a bitrate that can be supported based on available network bandwidth and the playback rate;
Hodroj et al., Pub. No. US 2012/0040682 A1, discloses systems and methods for assigning communication bandwidth wherein voice data is allocated a percentage of bandwidth and remaining bandwidth is allocated to other non-voice data;
Lieber, Pub. No. US 2015/0032851 A1, discloses a method for serving a media stream wherein the bitrate for the next segment is determined based on the player state, buffer state, and network bandwidth available to the server; and
Phillips et al., Pub. No. US 2016/0366202 A1, discloses a scheme for managing delivery of segmented media content in an ABR network wherein bitrate decisions are made at an edge network node using a model of the client’s video buffer and based on real time network conditions.
A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this action. An extension of time may be obtained under 37 CFR 1.136(a). However, in no event, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this 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.
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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
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