DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 6/29/2026 is(are) in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
The amendment filed July 28, 2026 has been entered. Claims 1-2, 4-9, 11-16, 18-20, 22-23, and 25-28 remain pending in the application. Applicant’s amendments to the Claims have overcome each and every objections previously set forth in the Non-Final Office Action mailed April 28, 2026.
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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2, 4-9, 11-13, 18-20, 22, and 25-28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu (US 20210329306 A1) in view of Kursun (US 20200160184 A1; already of record in IDS).
Regarding to claim 1, Liu teaches A computer-implemented method, in a data source device, comprising (Liu: Abstract):
receiving a first data block of a data stream as an input to a transmitter neural network of the data source device, the transmitter neural network implementing a first neural network architectural configuration (Liu: Paragraph(s) 0058-0059, 0153 teach(es) FIG. 1 is a block diagram illustrating an architecture for video streaming, such as video conferencing, between a sender and a receiver using neural networks; configuration manager may be capable of configuring different layers such as software layer and framework layer including Spark and distributed file system for supporting large-scale data processing);
generating, at the transmitter neural network implementing the first neural network architectural configuration, a first output based on the first data block, the first output representing a data encoded and channel encoded version of the first data block (Liu: Abstract; Paragraph(s) 0090, 0115, 0128-0129, 0095 teach(es) a sender architecture for generation of compressed video data usable for video streaming, such as during video conferencing, using one or more neural networks.. compressed video data is one or more keypoints associated with one or more keyframes or initial frames from one or more video input devices; a training framework implementing or otherwise using a GAN calculates loss values using any other loss function usable to determine loss from one or more sender or receiver neural network types; A sender captures or otherwise generates one or more additional frames at time intervals specified by a video encoding codec or other video streaming technique);
controlling a radio frequency (RF) antenna interface of the data source device based on the first output to transmit a first RF signal representative of the data encoded and channel encoded version of the first data block to a data sink device (Liu: Paragraph(s) 0228, 0587, 0095, 0218, 0469 teach(es) network interface may include a radio frequency front-end for up-conversion from baseband to radio frequency, and down conversion from radio frequency to baseband; A sender captures or otherwise generates one or more additional frames at time intervals specified by a video encoding codec or other video streaming technique).
However, Liu does not explicitly teach modifying the transmitter neural network to implement a second neural network architectural configuration responsive to a change in capabilities of at least one of the data source device or the data sink device.
Kursun from same or similar field of endeavor teaches modifying the transmitter neural network to implement a second neural network architectural configuration responsive to a change in capabilities of at least one of the data source device or the data sink device (Kursun: Abstract; Paragraph(s) 0006, 0069 teach(es) in response to identifying the change in the data pattern and determining the state, reconfigure an architectural configuration of the neural network learning engine by modifying the one or more neural network parameters).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Liu to incorporate the teachings of Kursun for modifying the transmitter neural network to implement a second neural network architectural configuration responsive to a change in capabilities of at least one of the data source device or the data sink device.
There is motivation to combine Kursun into Liu because Kursun’s teachings of reconfiguring an architectural configuration of the neural network learning engine would facilitate state-based real-time adaptations in artificial intelligence systems (Kursun: Abstract).
Regarding to claim 8, Liu teaches A computer-implemented method, in a data sink device, comprising (Liu: Abstract):
receiving, at a radio frequency (RF) antenna interface of the data sink device, a first RF signal from a data source device, the first RF signal representative of a data encoded and channel encoded version of a first data block of a data stream (Liu: Paragraph(s) 0228, 0587, 0095, 0218, 0469 teach(es) network interface may include a radio frequency front-end for up-conversion from baseband to radio frequency, and down conversion from radio frequency to baseband; A sender captures or otherwise generates one or more additional frames at time intervals specified by a video encoding codec or other video streaming technique);
providing a first input representative of the first RF signal as an input to a receiver neural network of the data sink device, the receiver neural network implementing a first neural network architectural configuration (Liu: Paragraph(s) 0070, 0072, 0116 teach(es) a receiver receives video data transmitted over a network and reconstructs, using a receiver neural network, video content for a video output from said video data. For example, a receiver receives video data comprising one or more keypoints to be used, by a receiver neural network in conjunction with a previously received key image, as video data over a network);
generating, at the receiver neural network implementing the first neural network architectural configuration, a first recovered data block representing a recovered channel decoded and data decoded version of the first data block; providing the first recovered data block for processing at one or more software applications of the data sink device (Liu: Paragraph(s) 0070, 0072, 0116, 0302, 0115 teach(es) graphics processing engines alternatively may comprise different types of graphics processing engines within a GPU, such as graphics execution units, media processing engines (e.g., video encoders/decoders), samplers, and blit engines; training one or more neural networks to perform video compression and decompression in order to facilitate video streaming, such as video conferencing).
However, Liu does not explicitly teach modifying the receiver neural network to implement a second neural network architectural configuration responsive to a change in capabilities of at least one of the data sink device or the data source device.
Kursun from same or similar field of endeavor teaches modifying the receiver neural network to implement a second neural network architectural configuration responsive to a change in capabilities of at least one of the data sink device or the data source device (Kursun: Abstract; Paragraph(s) 0006, 0069 teach(es) in response to identifying the change in the data pattern and determining the state, reconfigure an architectural configuration of the neural network learning engine by modifying the one or more neural network parameters).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Liu to incorporate the teachings of Kursun for modifying the receiver neural network to implement a second neural network architectural configuration responsive to a change in capabilities of at least one of the data sink device or the data source device.
There is motivation to combine Kursun into Liu because Kursun’s teachings of reconfiguring an architectural configuration of the neural network learning engine would facilitate state-based real-time adaptations in artificial intelligence systems (Kursun: Abstract).
Regarding to claim 18, Liu teaches A computer-implemented method, in a first infrastructure component of a network infrastructure, comprising (Liu: Abstract):
configuring a data source device to implement a first neural network architectural configuration for a transmitter neural network of the data source device, the transmitter neural network, implementing the first neural network architectural configuration, being configured to generate, for each input data block of a data stream generated at the data source device, a corresponding output for transmission by a radio frequency (RF) antenna interface of the data source device, the corresponding output representing a data encoded and channel encoded version of the input data block (Liu: Abstract; Paragraph(s) 0090, 0115, 0128-0129, 0228, 0587, 0095, 0218, 0469 teach(es) ) a sender architecture for generation of compressed video data usable for video streaming, such as during video conferencing, using one or more neural networks.. compressed video data is one or more keypoints associated with one or more keyframes or initial frames from one or more video input devices; a training framework implementing or otherwise using a GAN calculates loss values using any other loss function usable to determine loss from one or more sender or receiver neural network types; A sender captures or otherwise generates one or more additional frames at time intervals specified by a video encoding codec or other video streaming technique; network interface may include a radio frequency front-end for up-conversion from baseband to radio frequency, and down conversion from radio frequency to baseband; A sender captures or otherwise generates one or more additional frames at time intervals specified by a video encoding codec or other video streaming technique); and
configuring a data sink device to implement a second neural network architectural configuration for a receiver neural network of the data sink device, the receiver neural network, implementing the second neural network architectural configuration, being configured to generate, for each input from an RF antenna interface of the data sink device, a corresponding data block for provision to one or more software applications of the data sink device, the corresponding data block representing a recovered channel decoded and data decoded version of a corresponding data block of the data stream, (Liu: Abstract; Paragraph(s) 0090, 0115, 0128-0129, 0095, 0302, 0115 teach(es) a sender architecture for generation of compressed video data usable for video streaming, such as during video conferencing, using one or more neural networks.. compressed video data is one or more keypoints associated with one or more keyframes or initial frames from one or more video input devices; a training framework implementing or otherwise using a GAN calculates loss values using any other loss function usable to determine loss from one or more sender or receiver neural network types; A sender captures or otherwise generates one or more additional frames at time intervals specified by a video encoding codec or other video streaming technique; graphics processing engines alternatively may comprise different types of graphics processing engines within a GPU, such as graphics execution units, media processing engines (e.g., video encoders/decoders), samplers, and blit engines; training one or more neural networks to perform video compression and decompression in order to facilitate video streaming, such as video conferencing)
the method further comprising at least one of:
configuring the data source device to implement a [modified] neural network architectural configuration for the transmitter neural network responsive to receiving an indicator of a change of capabilities of at least one of the data source device, the data sink device, or an infrastructure component of the network infrastructure (Liu: Paragraph(s) 0058-0059, 0153 teach(es) FIG. 1 is a block diagram illustrating an architecture for video streaming, such as video conferencing, between a sender and a receiver using neural networks; configuration manager may be capable of configuring different layers such as software layer and framework layer including Spark and distributed file system for supporting large-scale data processing); and
configuring the data sink device to implement a [modified] neural network architectural configuration for the receiver neural network responsive to receiving an indicator of a change of capabilities of at least one of the data source device, the data sink device, or an infrastructure component of the network infrastructure (Liu: Abstract; Paragraph(s) 0090, 0115, 0128-0129, 0095 teach(es) a sender architecture for generation of compressed video data usable for video streaming, such as during video conferencing, using one or more neural networks.. compressed video data is one or more keypoints associated with one or more keyframes or initial frames from one or more video input devices; a training framework implementing or otherwise using a GAN calculates loss values using any other loss function usable to determine loss from one or more sender or receiver neural network types; A sender captures or otherwise generates one or more additional frames at time intervals specified by a video encoding codec or other video streaming technique).
However, Liu does not explicitly teach a modified neural network architectural configuration.
Kursun from same or similar field of endeavor teaches a modified neural network architectural configuration (Kursun: Abstract; Paragraph(s) 0006, 0069 teach(es) in response to identifying the change in the data pattern and determining the state, reconfigure an architectural configuration of the neural network learning engine by modifying the one or more neural network parameters).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Liu to incorporate the teachings of Kursun for a modified neural network architectural configuration.
There is motivation to combine Kursun into Liu because Kursun’s teachings of reconfiguring an architectural configuration of the neural network learning engine would facilitate state-based real-time adaptations in artificial intelligence systems (Kursun: Abstract).
Regarding to claim 2, the combination of Liu and Kursun teaches all the limitations of claim 1 above; however the combination does not explicitly teach wherein modifying the transmitter neural network to implement the second neural network architectural configuration is performed responsive to at least one of: a change in capability of the data source device; or a change in capability of the data sink device.
Kursun further teaches wherein modifying the transmitter neural network to implement the second neural network architectural configuration is performed responsive to at least one of: a change in capability of the data source device; or a change in capability of the data sink device (Kursun: Abstract; Paragraph(s) 0006, 0069, 0043, 0045 teach(es) in response to identifying the change in the data pattern and determining the state, reconfigure an architectural configuration of the neural network learning engine by modifying the one or more neural network parameters; The processing device may be configured to use the communication device to communicate with one or more other devices on a network such as, but not limited to the entity system and the state-based learning system. In this regard, the communication device may include an antenna operatively coupled to a transmitter and a receiver (together a “transceiver”), modem. The processing device may be configured to provide signals to and receive signals from the transmitter and receiver).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the combination of Liu and Kursun to incorporate the teachings of Kursun for wherein modifying the transmitter neural network to implement the second neural network architectural configuration is performed responsive to at least one of: a change in capability of the data source device; or a change in capability of the data sink device.
There is motivation to combine Kursun into the combination of Liu and Kursun because Kursun’s teachings of reconfiguring an architectural configuration of the neural network learning engine would facilitate state-based real-time adaptations in artificial intelligence systems (Kursun: Abstract).
Regarding to claims 4 and 11, the combination of Liu and Kursun teaches all the limitations of claims 1 and 8 above; and Liu further teaches further comprising: selecting, at the data source device, the first neural network architectural configuration from a plurality of neural network architectural configurations based on at least one of: one or more capabilities of at least one of the data source device or a data sink device; or a user-indicated preference (Liu: Paragraph(s) 0153 teach(es) configuration manager may be capable of configuring different layers such as software layer and framework layer; resource manager may be capable of managing clustered or grouped computing resources mapped to or allocated for support of distributed file system and job scheduler.. clustered or grouped computing resources may include grouped computing resources at data center infrastructure layer).
Regarding to claim 5, the combination of Liu and Kursun teaches all the limitations of claim 1 above; and Liu further teaches further comprising: implementing the first neural network architectural configuration selected from a plurality of neural network architectural configurations for the transmitter neural network responsive to a command from an infrastructure component of a network infrastructure (Liu: Paragraph(s) 0068, 0082 teach(es) a key image (e.g., a frame) is generated by one or more video input devices as a result of a request to generate a new key image, such as a new key frame, by a receiver.. a key image (e.g., a frame) generated by one or more video input devices as a result of a request to generate a new key image is a second image, or different image from a sequence of images, such as a different frame in a sequence of video frames; a sender selects a key image for transmission to a receiver.. a key image is determined by one or more video inputs and/or one or more sender neural networks; an upstream communication channel is network infrastructure to facilitate transfer of data from a sender to one or more network servers in a computing resource services provider).
Regarding to claim 6, the combination of Liu and Kursun teaches all the limitations of claim 4 above; and Liu further teaches further comprising: receiving a second data block of a data stream as an input to the modified transmitter neural network implementing the second neural network architectural configuration; generating, at said transmitter neural network, a second output based on the second data block and using the second neural network architectural configuration, the second output representing a data encoded and channel encoded version of the second data block; and controlling the RF antenna interface of the data source device based on the second output to transmit a second RF signal representative of the data encoded and channel encoded version of the second data block (Liu: Abstract; Paragraph(s) 0058-0059, 0153, 0090, 0115, 0128-0129, 0095, 0228, 0587, 0095, 0218, 0469, as stated above with respect to claim 1).
Regarding to claim 7, the combination of Liu and Kursun teaches all the limitations of claim 1 above; and Liu further teach wherein generating the first output comprises generating the first output at the transmitter neural network further based on at least one of: sensor data input to the transmitter neural network from one or more sensors of the data source device; a present operational parameter of the RF antenna interface; or capability information representing present capabilities of at least one of the data source device or a data sink device (Liu: Paragraph(s) 0166, 0169, 0226, 0228 teach(es) controller(s) provide signals for controlling one or more components and/or systems of vehicle in response to sensor data received from one or more sensors (e.g., sensor inputs); inference and/or training logic may be used in system FIG. 11A for inferencing or predicting operations based, at least in part, on weight parameters calculated using neural network training operations, neural network functions and/or architectures, or neural network use case; network interface may include a radio frequency front-end for up-conversion from baseband to radio frequency, and down conversion from radio frequency to baseband. In at least one embodiment, frequency conversions may be performed in any technically feasible fashion).
Regarding to claim 9, the combination of Liu and Kursun teaches all the limitations of claim 8 above; however the combination does not explicitly teach modifying the receiver neural network to implement the second neural network architectural configuration is performed responsive to at least one of: a change in capability of the data source device; or a change in capability of the data sink device.
Kursun further teaches modifying the receiver neural network to implement the second neural network architectural configuration is performed responsive to at least one of: a change in capability of the data source device; or a change in capability of the data sink device (Abstract; Paragraph(s) 0006, 0069, 0043, 0045 teach(es) in response to identifying the change in the data pattern and determining the state, reconfigure an architectural configuration of the neural network learning engine by modifying the one or more neural network parameters; The processing device may be configured to use the communication device to communicate with one or more other devices on a network such as, but not limited to the entity system and the state-based learning system. In this regard, the communication device may include an antenna operatively coupled to a transmitter and a receiver (together a “transceiver”), modem. The processing device may be configured to provide signals to and receive signals from the transmitter and receiver).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the combination of Liu and Kursun to incorporate the teachings of Kursun for modifying the receiver neural network to implement the second neural network architectural configuration is performed responsive to at least one of: a change in capability of the data source device; or a change in capability of the data sink device.
There is motivation to combine Kursun into the combination of Liu and Kursun because Kursun’s teachings of reconfiguring an architectural configuration of the neural network learning engine would facilitate state-based real-time adaptations in artificial intelligence systems (Kursun: Abstract).
Regarding to claim 12, the combination of Liu and Kursun teaches all the limitations of claim 11 above; and Liu further teaches further comprising:
receiving, at the RF antenna interface, a second RF signal representative of a data encoded and channel encoded version of a second data block of the data stream; providing a second input representative of the second RF signal as an input to the modified receiver neural network of the data sink device applications (Liu: Paragraph(s) 0228, 0587, 0095, 0218, 0469, 0070, 0072, 0116 teach(es) network interface may include a radio frequency front-end for up-conversion from baseband to radio frequency, and down conversion from radio frequency to baseband; A sender captures or otherwise generates one or more additional frames at time intervals specified by a video encoding codec or other video streaming technique);
generating, at the receiver neural network, a second recovered data block representing a recovered channel decoded and data decoded version of the second data block; and providing the second recovered data block for processing at the one or more software applications (Liu: Paragraph(s) 0070, 0072, 0116, 0302, 0115 teach(es) graphics processing engines alternatively may comprise different types of graphics processing engines within a GPU, such as graphics execution units, media processing engines (e.g., video encoders/decoders), samplers, and blit engines; training one or more neural networks to perform video compression and decompression in order to facilitate video streaming, such as video conferencing).
Regarding to claim 13, the combination of Liu and Kursun teaches all the limitations of claim 8 above; and Liu further teaches wherein generating the first recovered data block comprises generating the first recovered data block at the receiver neural network further based on at least one of: sensor data input to the receiver neural network from one or more sensors of the data sink device; a present operational parameter of the RF antenna interface; or capability information representing present capabilities of at least one of the data sink device or a data source device (Liu: Paragraph(s) 0166, 0169, 0226, 0228 teach(es) controller(s) provide signals for controlling one or more components and/or systems of vehicle in response to sensor data received from one or more sensors (e.g., sensor inputs); inference and/or training logic may be used in system FIG. 11A for inferencing or predicting operations based, at least in part, on weight parameters calculated using neural network training operations, neural network functions and/or architectures, or neural network use case; network interface may include a radio frequency front-end for up-conversion from baseband to radio frequency, and down conversion from radio frequency to baseband. In at least one embodiment, frequency conversions may be performed in any technically feasible fashion).
Regarding to claim 19, the combination of Liu and Kursun teaches all the limitations of claim 18 above; and Liu further teaches further comprising: configuring a second infrastructure component in a transmission path between the data source device and the data sink device to implement a third neural network architectural configuration for a neural network of the second infrastructure component, the second infrastructure component including the first infrastructure component or another infrastructure component (Liu: Paragraph(s) 0363 teach(es) communication paths interconnecting various components in FIG. 20 may be implemented using any suitable protocols, such as PCI (Peripheral Component Interconnect) based protocols (e.g., PCI-Express), or other bus or point-to-point communication interfaces and/or protocol(s), such as NV-Link high-speed interconnect, or interconnect protocols).
Regarding to claim 20, the combination of Liu and Kursun teaches all the limitations of claim 19 above; and Liu further teaches wherein configuring second infrastructure component comprises configuring second infrastructure component to implement the third neural network architectural configuration responsive to receiving capability information from at least one of the data source device, the data sink device, or an infrastructure component of the network infrastructure (Liu: Paragraph(s) 0363, as stated above with respect to claim 19).
Regarding to claim 22, the combination of Liu and Kursun teaches all the limitations of claim 19 above; and Liu further teaches wherein: configuring the data source device to implement the first neural network architectural configuration comprises configuring the data source device to implement the first neural network architectural configuration responsive to receiving capability information representing one or more capabilities from at least one of the data source device, the data sink device, or an infrastructure component of the network infrastructure (Liu: Paragraph(s) 0363, as stated above with respect to claim 20); and configuring the data sink device to implement the second neural network architectural configuration comprises configuring the data sink device to implement the second neural network architectural configuration responsive to receiving capability information representing one or more capabilities from at least one of the data source device, the data sink device, or an infrastructure component of the network infrastructure (Liu: Paragraph(s) 0363, as stated above with respect to claim 20).
Regarding to claim 25, the combination of Liu and Kursun teaches all the limitations of claim 1 above; and Liu further teaches wherein the data stream comprises a real-time data stream (Liu: Abstract; Paragraph(s) 0058), and wherein at least one of: the real-time data stream comprises one of: an audio stream of a voice call or an audio stream or a video stream of a video call; or the data source device comprises a remote video game server (Liu: Abstract; Paragraph(s) 0058, 0081 teach(es) an architecture for video streaming, such as video conferencing, between a sender and a receiver using neural networks), the data sink device comprises a user device, and the real-time data stream comprises a rendered video stream (Liu: Paragraph(s) 0204 teach(es) real-time ray-tracing hardware accelerator may be used to quickly and efficiently determine positions and extents of objects (e.g., within a world model), to generate real-time visualization simulations, for RADAR signal interpretation, for sound propagation synthesis and/or analysis, for simulation of SONAR systems, for general wave propagation simulation, for comparison to LIDAR data for purposes of localization and/or other functions, and/or for other uses).
Regarding to claim 26, the combination of Liu and Kursun teaches all the limitations of claim 22 above; and Liu further teaches wherein the one or more capabilities comprise at least one of: a sensor capability; a processing resource capability; a power capability, an RF antenna interface capability; a data generation capability; a data consumption capability; and a device accessory capability (Liu: Paragraph(s) 0166, 0234-0236).
Regarding to claim 27, the combination of Liu and Kursun teaches all the limitations of claim 18 above; and Liu further teaches A device comprising: a network interface (Liu: Paragraph(s) 0168, 0185, 0228); at least one processor coupled to the network interface (Liu: Paragraph(s) 0135, 0185); and a memory storing executable instructions (Liu: Paragraph(s) 0135), the executable instructions configured to manipulate the at least one processor to perform the method of claim 18 (Liu: Paragraph(s) 0408-0409).
Regarding to claim 28, the combination of Liu and Kursun teaches all the limitations of claim 1 above; and Liu further teaches A device comprising: a radio frequency (RF) antenna interface (Liu: Paragraph(s) 0228); at least one processor coupled to the RF antenna interface (Liu: Paragraph(s) 0135, 0185, 0228, 0587); and a memory storing executable instructions (Liu: Paragraph(s) 0135), the executable instructions configured to manipulate the at least one processor to perform the method of claim 1 (Liu: Paragraph(s) ) 0408-0409).
Claim(s) 14-16 and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu in view of Kursun, as applied claims 8 and 19 above, and in further view of Bao (US 20210185515 A1).
Regarding to claim 14, the combination of Liu and Kursun teaches all the limitations of claim 8 above; however the combination does not explicitly teach further comprising: providing feedback, to a first infrastructure component of a network infrastructure in a transmission path between the data sink device and a data source device, a quality metric for the first recovered data block; and in response to the feedback, receiving, from a second infrastructure component, an updated neural network architectural configuration for implementation at the receiver neural network.
Bao from same or similar field of endeavor teaches further comprising: providing feedback, to a first infrastructure component of a network infrastructure in a transmission path between the data sink device and a data source device, a quality metric for the first recovered data block; and in response to the feedback, receiving, from a second infrastructure component, an updated neural network architectural configuration for implementation at the receiver neural network (Bao: Paragraph(s) 0004, 0038, 0060).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the combination of Liu and Kursun to incorporate the teachings of Bao for further comprising: providing feedback, to a first infrastructure component of a network infrastructure in a transmission path between the data sink device and a data source device, a quality metric for the first recovered data block; and in response to the feedback, receiving, from a second infrastructure component, an updated neural network architectural configuration for implementation at the receiver neural network.
There is motivation to combine Bao into the combination of Liu and Kursun because Bao’s teachings of feedback would facilitate to configure or reconfigure the neural network block for improved performance (Bao: Paragraph(s) 0004).
Regarding to claim 15, the combination of Liu, Kursun, and Bao teaches all the limitations of claim 14 above; however the combination does not explicitly teach wherein the feedback includes one or more of: an objective quality metric generated by the data sink device independent of user input; or a subjective quality metric based on user input from a user of the data sink device.
Bao further teaches wherein the feedback includes one or more of: an objective quality metric generated by the data sink device independent of user input; or a subjective quality metric based on user input from a user of the data sink device (Bao: Paragraph(s) 0143, 0004, 0038, 0060 teach(es) the feedback data may be unprocessed channel quality measurements made by UE. The feedback data may be processed data, and may include channel information gathered by UE (e.g., by receiving one or more reference signals) prior to or after performing a channel estimation or prior to or after frequency-tracking corrections are made).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the combination of Liu, Kursun, and Bao to incorporate the teachings of Bao for wherein the feedback includes one or more of: an objective quality metric generated by the data sink device independent of user input; or a subjective quality metric based on user input from a user of the data sink device.
There is motivation to combine Bao into the combination of Liu, Kursun, and Bao because Bao’s teachings of feedback would facilitate adjust, update, or confirm one or more neural network block parameters based on the feedback information to improve the output values of the neural network blocks, (Bao: Paragraph(s) 0143).
Regarding to claim 16, the combination of Liu and Kursun teaches all the limitations of claim 8 above; however the combination does not explicitly teach wherein the data sink device comprises a device configured to be wirelessly connected to a base station, wireless access point, or other component of an infrastructure network.
Bao from same or similar field of endeavor teaches wherein the data sink device comprises a device configured to be wirelessly connected to a base station, wireless access point, or other component of an infrastructure network (Bao: Abstract; Paragraph(s) 0143 teach(es) communicating capability information, e.g., regarding neural network blocks supported by a user equipment (UE) and a base station.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the combination of Liu and Kursun to incorporate the teachings of Bao for wherein the data sink device comprises a device configured to be wirelessly connected to a base station, wireless access point, or other component of an infrastructure network.
There is motivation to combine Bao into the combination of Liu and Kursun because Bao’s teachings of base station would facilitate to configure or reconfigure a neural network block according to the neural network block parameters (Bao: Abstract).
Regarding to claim 23, the combination of Liu and Kursun teaches all the limitations of claim 19 above; however the combination does not explicitly teach further comprising: receiving feedback from the data sink device responsive to the data sink device generating a recovered data block using the receiver neural network, the feedback representing a quality metric for the recovered data block; determining a modified neural network architectural configuration based on the feedback; and configuring at least one of the data sink device or the data source device to implement the modified neural network architectural configuration.
Bao from same or similar field of endeavor teaches further comprising: receiving feedback from the data sink device responsive to the data sink device generating a recovered data block using the receiver neural network, the feedback representing a quality metric for the recovered data block; determining a modified neural network architectural configuration based on the feedback; and configuring at least one of the data sink device or the data source device to implement the modified neural network architectural configuration (Bao: Paragraph(s) 0004, 0038, 0060, 0143, as stated above with respect to claims 14-15).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the combination of Liu and Kursun to incorporate the teachings of Bao for further comprising: receiving feedback from the data sink device responsive to the data sink device generating a recovered data block using the receiver neural network, the feedback representing a quality metric for the recovered data block; determining a modified neural network architectural configuration based on the feedback; and configuring at least one of the data sink device or the data source device to implement the modified neural network architectural configuration.
There is motivation to combine Bao into the combination of Liu, Kursun, and Bao because Bao’s teachings of feedback would facilitate adjust, update, or confirm one or more neural network block parameters based on the feedback information to improve the output values of the neural network blocks, (Bao: Paragraph(s) 0143).
Response to Arguments
Applicant's arguments filed July 28, 2026 have been fully considered but they are not persuasive.
Regarding applicant’s argument under Claim Rejections - 35 USC § 103 that “The Office has not established that Liu's keypoints represent a channel-encoded version of a data block. A video-compression output, such as keypoints extracted from video frames, is not the same as a channel-encoded output” (Page 11 of the Remarks), examiner respectfully argues that Liu teaches the features of the data encoded and channel encoded version of data block (Liu: Abstract; Paragraph(s) 0090, 0115, 0128-0129, 0095). The clam languages are recited without any technical details to differentiate the claim languages from the combination of the cited references. It is recommended for the applicant to amend further the claims with more technical details and contexts of data source device, data sink device, data stream, architectural configurations, input/output, data-encoded, channel-encoded, etc.
Regarding applicant’s argument that “The Office has not shown that Liu's sender neural network output is used to control an RF antenna interface in this claimed manner” (Page 12 of the Remarks), examiner respectfully argues that Liu teaches the features of network interface including a radio frequency front-end for up-conversion from baseband to radio frequency, and down conversion from radio frequency to baseband. Obviously any kind of network interface is controlled for interfacing of anything (Liu: Paragraph(s) 0228, 0587, 0095, 0218, 0469). It is recommended for the applicant to amend further the claims with more technical details and contexts of controlling the RF antenna interface, etc.
Regarding applicant’s argument that “configuring software or framework layers for large-scale data processing is not the same as a transmitter neural network implementing a neural network architectural configuration” (Page 12 of the Remarks), examiner respectfully argues that, as stated above, the claims do not recite any technical details and context of the transmitter neural network and implementing of a neural network architectural configuration, enough to differentiate the claim language from the cited reference.
Regarding applicant’s argument that “The claim language requires a specific relationship between the modification and the triggering condition: the transmitter neural network is modified to implement the second neural network architectural configuration responsive to a change in capabilities of at least one of the data source device or the data sink device” (Page 12 of the Remarks), examiner respectfully argues that the claims do not recite any technical details and context of the transmitter neural network, modifying of neural network, architectural configuration, implementing of a neural network architectural configuration, change in capabilities of at least one of the data source device or the data sink device, etc. It is recommended for the applicant to amend further the claims with more technical details and contexts of them.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Schnitzler (US 20230376350 A1) teaches System And Method For Adapting To Changing Resource Limitations, including configuration, RF signal, encoding, and neural network.
Grauman (US 20210174817 A1) teaches Systems And Methods For Visually Guided Audio Separation, including a neural network that is configured to train based on one or more sets of video/audio data.
Namgoong (US 20210266036 A1) teaches Machine Learning Based Receiver Performance Improvement Using Peak Reduction Tones, including machine learning trained neural networks configured to map data tones to PRTs for transmission from a specific transmitter and/or for transmission to a specific receiver.
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/CLAY C LEE/ Primary Examiner, Art Unit 3699
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