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 .
Claim Rejections - 35 USC § 101
1. Claim 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims are drawn to "the computer program product comprising “a computer-readable storage medium storing one or more programs for execution by one or more processors of a computing device, the one or more programs comprising instructions to”, but not directly to a statutory storage medium, which is required for the claim to be statutory. The specification fails to address and disclose the computer-readable storage medium being embodied in a non-transitory medium. Accordingly, these recording medium claims are rejected as non-statutory for failing to be drawn directly to any such non-transitory storage medium.
Accordingly, variations of the term "storage" are not necessarily considered to limit a media claim to non-transitory embodiments because many disclosures conflict storage media and signals. For example, US Patent 6,286,104: "the methods described herein may be implemented by a series of computer-executable instructions residing on a storage medium such as a carrier wave". These computer-readable medium claims are rejected as non-statutory for failing to be drawn directly to any such non-transitory storage medium.
Claim Rejections - 35 USC § 103
2. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
3. Claim(s) 1,12, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Shi et al. (US 2014/0282086) in further view of Werner et al. (US 2024/0275838).
Regarding claim 1, Shi teaches a guidance information providing method performed by a computing device, the guidance information providing method comprising: obtaining at least one of network quality data and media quality data related to video communication (see fig. 7, ¶ 0179-0193. A display of the device having a detection unit is able to determine the network conditions of the video communication. The device being able to provide a message stating network state, “poor network state” Upon network being deteriorated there will be video quality deteriorated as well.); determining whether quality of the video communication deteriorates, based on the obtained at least one of the network quality data and the media quality data (see fig. 7, ¶ 0179-0193. The device detecting the current network state will provide a message upon the detected state of the network of the device being poor. Upon network being deteriorated there will be video quality deteriorated as well.); and generating first guidance information for solving a deterioration in the quality of the video communication, based on a first prompt using a predetermined condition (generative AI model) when the quality of the video communication deteriorates (see fig. 7, ¶ 0179-0193. The device displays a prompt of "poor network state" when it is detected that the network state for the electronic device does not satisfy the predetermined condition, the display unit can be controlled to display a prompt box of "the network environment is poor, please turn off the video communication interaction interface for the time being and keep the voice call", the prompt box can be set with an OK button or a Cancel button. When the user clicks the OK button, the second electronic device will turn off the video communication interaction interface and keep the voice call; when the user clicks the Cancel button, the second electronic device will return to the video communication interaction interface.).
Shi provide network condition that will prompt the device to alternate functions dependent on network conditions. However, Shi does not disclose a generative AI model.
Werner discloses a quality monitor module 145 detects a network disruption via real time analysis of audio and/or video data using a machine learning model. The machine learning model may include and/or be associated with one or more of a convolutional neural network model, a random forest model, a clustering model, or a regression model, among other examples. In some implementations, the quality monitor module uses the machine learning model to determine that the network disruption (e.g., a quality of communications does not satisfy a threshold) has occurred by providing candidate bandwidths, data packet loss rates, transmission signal strengths, and/or connection speeds as input to the machine learning model, and using the machine learning model to determine a likelihood, probability, or confidence that a particular outcome (e.g., pixelated video, frozen images, scrambled audio, and/or the like) will be achieved using the candidate parameters. The quality monitor module 145 (or another module internal or external to the user device 115) may train, update, and/or refine the machine learning model to increase the accuracy of the outcomes and/or parameters determined using the machine learning model (see ¶ 0025-0026).
Werner discloses a quality monitor module that includes a machine learning model that detects network conditions. The quality monitor can be internal to the user device. The combination of Werner to Shi will provide the quality monitor of Werner to be implemented in Shi detection unit for network conditions monitoring.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shi to incorporate an AI (machine learning ) model to determine network conditions. The motivation provides the quality monitor (machine learning model) of Werner to be implemented in Shi detection unit for network conditions monitoring.
Regarding claim 12, Shi teaches a guidance information providing server comprising at least one processor configured to execute a plurality of instructions to perform a plurality of operations and at least one memory configured to store the plurality of instructions, wherein the plurality of operations comprises: obtaining at least one of network quality data and media quality data related to video communication (see fig. 7, ¶ 0179-0193. A display of the device having a detection unit is able to determine the network conditions of the video communication. The device being able to provide a message stating network state, “poor network state” Upon network being deteriorated there will be video quality deteriorated as well.); determining whether quality of the video communication deteriorates, based on the obtained at least one of the network quality data and the media quality data (see fig. 7, ¶ 0179-0193. The device detecting the current network state will provide a message upon the detected state of the network of the device being poor. Upon network being deteriorated there will be video quality deteriorated as well.); and generating first guidance information for solving a deterioration in the quality of the video communication, based on a first prompt using a predetermined condition (generative AI model) when the quality of the video communication deteriorates (see fig. 7, ¶ 0179-0193. The device displays a prompt of "poor network state" when it is detected that the network state for the electronic device does not satisfy the predetermined condition, the display unit can be controlled to display a prompt box of "the network environment is poor, please turn off the video communication interaction interface for the time being and keep the voice call", the prompt box can be set with an OK button or a Cancel button. When the user clicks the OK button, the second electronic device will turn off the video communication interaction interface and keep the voice call; when the user clicks the Cancel button, the second electronic device will return to the video communication interaction interface.).
Shi provide network condition that will prompt the device to alternate functions dependent on network conditions. However, Shi does not disclose a generative AI model.
Werner discloses a quality monitor module 145 detects a network disruption via real time analysis of audio and/or video data using a machine learning model. The machine learning model may include and/or be associated with one or more of a convolutional neural network model, a random forest model, a clustering model, or a regression model, among other examples. In some implementations, the quality monitor module uses the machine learning model to determine that the network disruption (e.g., a quality of communications does not satisfy a threshold) has occurred by providing candidate bandwidths, data packet loss rates, transmission signal strengths, and/or connection speeds as input to the machine learning model, and using the machine learning model to determine a likelihood, probability, or confidence that a particular outcome (e.g., pixelated video, frozen images, scrambled audio, and/or the like) will be achieved using the candidate parameters. The quality monitor module 145 (or another module internal or external to the user device 115) may train, update, and/or refine the machine learning model to increase the accuracy of the outcomes and/or parameters determined using the machine learning model (see ¶ 0025-0026).
Werner discloses a quality monitor module that includes a machine learning model that detects network conditions. The quality monitor can be internal to the user device. The combination of Werner to Shi will provide the quality monitor of Werner to be implemented in Shi detection unit for network conditions monitoring.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shi to incorporate an AI (machine learning ) model to determine network conditions. The motivation provides the quality monitor (machine learning model) of Werner to be implemented in Shi detection unit for network conditions monitoring.
Regarding claim 20, Shi teaches a computer-readable storage medium storing one or more programs for execution by one or more processors of a computing device, the one or more programs comprising instructions to: obtain at least one of network quality data and media quality data related to video communication (see fig. 7, ¶ 0179-0193. A display of the device having a detection unit is able to determine the network conditions of the video communication. The device being able to provide a message stating network state, “poor network state” Upon network being deteriorated there will be video quality deteriorated as well.); determine whether quality of the video communication deteriorates, based on the obtained at least one of the network quality data and the media quality data (see fig. 7, ¶ 0179-0193. The device detecting the current network state will provide a message upon the detected state of the network of the device being poor. Upon network being deteriorated there will be video quality deteriorated as well.); and generate first guidance information for solving a deterioration in the quality of the video communication, based on a first prompt using a predetermined condition (generative AI model) when the quality of the video communication deteriorates (see fig. 7, ¶ 0179-0193. The device displays a prompt of "poor network state" when it is detected that the network state for the electronic device does not satisfy the predetermined condition, the display unit can be controlled to display a prompt box of "the network environment is poor, please turn off the video communication interaction interface for the time being and keep the voice call", the prompt box can be set with an OK button or a Cancel button. When the user clicks the OK button, the second electronic device will turn off the video communication interaction interface and keep the voice call; when the user clicks the Cancel button, the second electronic device will return to the video communication interaction interface.).
Shi provide network condition that will prompt the device to alternate functions dependent on network conditions. However, Shi does not disclose a generative AI model.
Werner discloses a quality monitor module 145 detects a network disruption via real time analysis of audio and/or video data using a machine learning model. The machine learning model may include and/or be associated with one or more of a convolutional neural network model, a random forest model, a clustering model, or a regression model, among other examples. In some implementations, the quality monitor module uses the machine learning model to determine that the network disruption (e.g., a quality of communications does not satisfy a threshold) has occurred by providing candidate bandwidths, data packet loss rates, transmission signal strengths, and/or connection speeds as input to the machine learning model, and using the machine learning model to determine a likelihood, probability, or confidence that a particular outcome (e.g., pixelated video, frozen images, scrambled audio, and/or the like) will be achieved using the candidate parameters. The quality monitor module 145 (or another module internal or external to the user device 115) may train, update, and/or refine the machine learning model to increase the accuracy of the outcomes and/or parameters determined using the machine learning model (see ¶ 0025-0026).
Werner discloses a quality monitor module that includes a machine learning model that detects network conditions. The quality monitor can be internal to the user device. The combination of Werner to Shi will provide the quality monitor of Werner to be implemented in Shi detection unit for network conditions monitoring.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shi to incorporate an AI (machine learning ) model to determine network conditions. The motivation provides the quality monitor (machine learning model) of Werner to be implemented in Shi detection unit for network conditions monitoring.
4. Claim(s) 2, 3, 4, 5, 6, 13, 14, 15, 16 are rejected under 35 U.S.C. 103 as being unpatentable over Shi et al. (US 2014/0282086) in further view of Werner et al. (US 2024/0275838).
Regarding claim 2, Shi teaches the guidance information providing method of claim 1, further comprising: providing information about whether the quality of the video communication deteriorates to a transmitting terminal; and obtaining the first prompt from the transmitting terminal (see fig. 7, ¶ 0179-0193. The device displays a prompt of "poor network state" when it is detected that the network state for the electronic device does not satisfy the predetermined condition, the display unit can be controlled to display a prompt box of "the network environment is poor, please turn off the video communication interaction interface for the time being and keep the voice call", the prompt box can be set with an OK button or a Cancel button. When the user clicks the OK button, the second electronic device will turn off the video communication interaction interface and keep the voice call; when the user clicks the Cancel button, the second electronic device will return to the video communication interaction interface.).
Regarding claim 3, Shi teaches the guidance information providing method of claim 2, wherein the first prompt comprises at least one of device information about the transmitting terminal, the network quality data, the information about whether the quality of the video communication deteriorates, and a predefined system prompt (see fig. 7, ¶ 0179-0193. The device displays a prompt of "poor network state" when it is detected that the network state for the electronic device does not satisfy the predetermined condition, the display unit can be controlled to display a prompt box of "the network environment is poor, please turn off the video communication interaction interface for the time being and keep the voice call", the prompt box can be set with an OK button or a Cancel button. When the user clicks the OK button, the second electronic device will turn off the video communication interaction interface and keep the voice call; when the user clicks the Cancel button, the second electronic device will return to the video communication interaction interface. The network condition are determined and this leads to poor video communication thus prompting the user to transfer over to audio upon the network conditions degrading.).
Regarding claim 4, Shi does not teach the guidance information providing method of claim 1, wherein the network quality data comprises at least one of loss data, delay data, and a network error code, and the media quality data comprises video layer information.
Werner teaches wherein the network quality data comprises at least one of loss data, delay data, and a network error code, and the media quality data comprises video layer information (see ¶ 0025-0026. A quality monitor module 145 detects a network disruption via real time analysis of audio and/or video data using a machine learning model. The machine learning model may include and/or be associated with one or more of a convolutional neural network model, a random forest model, a clustering model, or a regression model, among other examples. The quality monitor module uses the machine learning model to determine that the network disruption (e.g., a quality of communications does not satisfy a threshold) has occurred by providing candidate bandwidths, data packet loss rates, transmission signal strengths, and/or connection speeds as input to the machine learning model, and using the machine learning model to determine a likelihood, probability, or confidence that a particular outcome (e.g., pixelated video, frozen images, scrambled audio, and/or the like) will be achieved using the candidate parameters. The quality monitor module 145 (or another module internal or external to the user device 115) may train, update, and/or refine the machine learning model to increase the accuracy of the outcomes and/or parameters determined using the machine learning model.). Werner provides analysis of video data (layer video information) and network data loss.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shi to incorporate an AI (machine learning ) model to determine network conditions. The motivation provides the quality monitor (machine learning model) to providing candidate bandwidths, data packet loss rates, transmission signal strengths, and/or connection speeds as input to the machine learning model, and using the machine learning model to determine a likelihood, probability, or confidence that a particular outcome (e.g., pixelated video, frozen images, scrambled audio, and/or the like).
Regarding claim 5, Shi does not teach the guidance information providing method of claim 1, wherein the determining comprises determining whether the quality of the video communication deteriorates using a pre-trained deep learning model.
Werner teaches wherein the determining comprises determining whether the quality of the video communication deteriorates using a pre-trained deep learning model (see ¶ 0025-0026. A quality monitor module 145 detects a network disruption via real time analysis of audio and/or video data using a machine learning model. The machine learning model may include and/or be associated with one or more of a convolutional neural network model, a random forest model, a clustering model, or a regression model, among other examples. In some implementations, the quality monitor module uses the machine learning model to determine that the network disruption (e.g., a quality of communications does not satisfy a threshold) has occurred by providing candidate bandwidths, data packet loss rates, transmission signal strengths, and/or connection speeds as input to the machine learning model, and using the machine learning model to determine a likelihood, probability, or confidence that a particular outcome (e.g., pixelated video, frozen images, scrambled audio, and/or the like) will be achieved using the candidate parameters. The quality monitor module 145 (or another module internal or external to the user device 115) may train, update, and/or refine the machine learning model to increase the accuracy of the outcomes and/or parameters determined using the machine learning model.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shi to incorporate an AI (machine learning ) model to determine network conditions. The motivation provides the quality monitor (machine learning model) to providing candidate bandwidths, data packet loss rates, transmission signal strengths, and/or connection speeds as input to the machine learning model, and using the machine learning model to determine a likelihood, probability, or confidence that a particular outcome (e.g., pixelated video, frozen images, scrambled audio, and/or the like).
Regarding claim 6, Shi teaches the guidance information providing method of claim 1, further comprising providing the generated first guidance information to a transmitting terminal (see fig. 7, ¶ 0179-0193. The device displays a prompt of "poor network state" when it is detected that the network state for the electronic device does not satisfy the predetermined condition, the display unit can be controlled to display a prompt box of "the network environment is poor, please turn off the video communication interaction interface for the time being and keep the voice call", the prompt box can be set with an OK button or a Cancel button. When the user clicks the OK button, the second electronic device will turn off the video communication interaction interface and keep the voice call; when the user clicks the Cancel button, the second electronic device will return to the video communication interaction interface.).
Regarding claim 13, Shi teaches the guidance information providing server of claim 12, wherein the plurality of operations further comprises: providing information about whether the quality of the video communication deteriorates to a transmitting terminal; and obtaining the first prompt from the transmitting terminal (see fig. 7, ¶ 0179-0193. The device displays a prompt of "poor network state" when it is detected that the network state for the electronic device does not satisfy the predetermined condition, the display unit can be controlled to display a prompt box of "the network environment is poor, please turn off the video communication interaction interface for the time being and keep the voice call", the prompt box can be set with an OK button or a Cancel button. When the user clicks the OK button, the second electronic device will turn off the video communication interaction interface and keep the voice call; when the user clicks the Cancel button, the second electronic device will return to the video communication interaction interface.).
Regarding claim 14, Shi teaches the guidance information providing server of claim 13, wherein the first prompt comprises at least one of device information about the transmitting terminal, the network quality data, the information about whether the quality of the video communication deteriorates, and a predefined system prompt (see fig. 7, ¶ 0179-0193. The device displays a prompt of "poor network state" when it is detected that the network state for the electronic device does not satisfy the predetermined condition, the display unit can be controlled to display a prompt box of "the network environment is poor, please turn off the video communication interaction interface for the time being and keep the voice call", the prompt box can be set with an OK button or a Cancel button. When the user clicks the OK button, the second electronic device will turn off the video communication interaction interface and keep the voice call; when the user clicks the Cancel button, the second electronic device will return to the video communication interaction interface. The network condition are determined and this leads to poor video communication thus prompting the user to transfer over to audio upon the network conditions degrading.).
Regarding claim 15, Shi does not teach the guidance information providing server of claim 12, wherein the network quality data comprises at least one of loss data, delay data, and a network error code, and the media quality data comprises video layer information.
Werner teaches wherein the network quality data comprises at least one of loss data, delay data, and a network error code, and the media quality data comprises video layer information (see ¶ 0025-0026. A quality monitor module 145 detects a network disruption via real time analysis of audio and/or video data using a machine learning model. The machine learning model may include and/or be associated with one or more of a convolutional neural network model, a random forest model, a clustering model, or a regression model, among other examples. The quality monitor module uses the machine learning model to determine that the network disruption (e.g., a quality of communications does not satisfy a threshold) has occurred by providing candidate bandwidths, data packet loss rates, transmission signal strengths, and/or connection speeds as input to the machine learning model, and using the machine learning model to determine a likelihood, probability, or confidence that a particular outcome (e.g., pixelated video, frozen images, scrambled audio, and/or the like) will be achieved using the candidate parameters. The quality monitor module 145 (or another module internal or external to the user device 115) may train, update, and/or refine the machine learning model to increase the accuracy of the outcomes and/or parameters determined using the machine learning model.). Werner provides analysis of video data (layer video information) and network data loss.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shi to incorporate an AI (machine learning ) model to determine network conditions. The motivation provides the quality monitor (machine learning model) to providing candidate bandwidths, data packet loss rates, transmission signal strengths, and/or connection speeds as input to the machine learning model, and using the machine learning model to determine a likelihood, probability, or confidence that a particular outcome (e.g., pixelated video, frozen images, scrambled audio, and/or the like).
Regarding claim 16, Shi teaches the guidance information providing server of claim 12, wherein the plurality of operations further comprises providing the generated first guidance information to a transmitting terminal (see fig. 7, ¶ 0179-0193. The device displays a prompt of "poor network state" when it is detected that the network state for the electronic device does not satisfy the predetermined condition, the display unit can be controlled to display a prompt box of "the network environment is poor, please turn off the video communication interaction interface for the time being and keep the voice call", the prompt box can be set with an OK button or a Cancel button. When the user clicks the OK button, the second electronic device will turn off the video communication interaction interface and keep the voice call; when the user clicks the Cancel button, the second electronic device will return to the video communication interaction interface.).
5. Claim(s) 7, 8, 9, 10, 17, 18, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Shi et al. (US 2014/0282086) in further view of Werner et al. (US 2024/0275838) in further view of Kumar (US 10,264,214).
Regarding claim 7, Shi and Werner do not teach the guidance information providing method of claim 1, further comprising generating second guidance information for sharing the deterioration in the quality of the video communication, based on a second prompt using the generative AI model when the quality of the video communication deteriorates.
Kumar teaches guidance information providing method of claim 1, further comprising generating second guidance information for sharing the deterioration in the quality of the video communication, based on a second prompt using the generative AI model when the quality of the video communication deteriorates (see fig. 4, col. 4, line 49-col. 5, , line 61. col. 6, lines 49-64, col. 9, lines 22-col. 10, lines 21. The system is able to interact with the user via prompts, and acknowledge that the bandwidth (network condition) are poor, which the user can input a response based on the prompt. The prompt can be a second guidance information based on such interactions with the system based on network quality.).
Werner discloses machine learning module for determining network conditions (¶ 0025-0026), thus the combination of Kumar and Werner provide the capabilities of the machine learning to provide and determine network conditions.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shi and Werner to incorporate generating second guidance information for sharing the deterioration in the quality of the video communication. The motivation provides for providing guidance updates to the user for network conditions.
Regarding claim 8, Shi and Werner do not teach the guidance information providing method of claim 7, further comprising: providing the information about whether the quality of the video communication to a transmitting terminal; and obtaining the second prompt from the transmitting terminal.
Kumar teaches the guidance information providing method of claim 7, further comprising: providing the information about whether the quality of the video communication to a transmitting terminal; and obtaining the second prompt from the transmitting terminal (see fig. 4, col. 4, line 49-col. 5, , line 61. col. 6, lines 49-64, col. 9, lines 22-col. 10, lines 21. The system is able to interact with the user via prompts, and acknowledge that the bandwidth (network condition) are poor, which the user can input a response based on the prompt. The prompt can be a second guidance information based on such interactions with the system based on network quality. The agent will handle user intents concerning video quality such as an intent to communicate that video quality is poor (e.g., expressed in user utterances such as “My preview video looks pixelated” or “My video resolution is very low”) by, for example, (1) issuing an agent prompt asking the user if the video should be muted/turned off, (2) automatically changing the network for the video streams to a network having higher bandwidth, if such an alternative network path is available.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shi and Werner to incorporate providing the information about whether the quality of the video communication to a transmitting terminal. The motivation provides for providing guidance updates and based on guidance updates, the user can input a response to respond to the system.
Regarding claim 9, Shi and Werner do not teach the guidance information providing method of claim 7, wherein the second prompt comprises at least one of attendee information, guidance language information, and a predefined system prompt.
Kumar teaches wherein the second prompt comprises at least one of attendee information, guidance language information, and a predefined system prompt (see fig. 4, col. 4, line 49-col. 5, , line 61. col. 6, lines 49-64, col. 9, lines 22-col. 10, lines 21. The system provides guidance language information (providing information related to the issue) and the system prompts are generated based on the detected issues that the system determines. Language based guidance for guiding the user through network issues).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shi and Werner to incorporate language based guidance for guiding the user through network issues. The motivation provides providing for guidance updates and based on guidance updates, the user can input a response to respond to the system.
Regarding claim 10, Shi and Werner do not teach the guidance information providing method of claim 7, further comprising providing the generated second guidance information to one or more receiving terminals.
Kumar teaches guidance information providing method of claim 7, further comprising providing the generated second guidance information to one or more receiving terminals (see fig. 4, col. 4, line 49-col. 5, , line 61. col. 6, lines 49-64, col. 9, lines 22-col. 10, lines 21. The system provides guidance language information (providing information related to the issue) and the system prompts are generated based on the detected issues that the system determines. Language based guidance for guiding the user through network issues. The guidance can be provided to one device or more devices.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shi and Werner to incorporate language based guidance for guiding the user through network issues to one device or other devices. The motivation provides for providing guidance updates and based on guidance updates, the user can input a response to respond to the system.
Regarding claim 17, Shi and Werner do not teach the guidance information providing server of claim 12, wherein the plurality of operations further comprises generating second guidance information for sharing the deterioration in the quality of the video communication, based on a second prompt using the generative AI model when the quality of the video communication deteriorates.
Kumar teaches wherein the plurality of operations further comprises generating second guidance information for sharing the deterioration in the quality of the video communication, based on a second prompt using the generative AI model when the quality of the video communication deteriorates (see fig. 4, col. 4, line 49-col. 5, , line 61. col. 6, lines 49-64, col. 9, lines 22-col. 10, lines 21. The system is able to interact with the user via prompts, and acknowledge that the bandwidth (network condition) are poor, which the user can input a response based on the prompt. The prompt can be a second guidance information based on such interactions with the system based on network quality.).
Werner discloses machine learning module for determining network conditions (¶ 0025-0026), thus the combination of Kumar and Werner provide the capabilities of the machine learning to provide and determine network conditions.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shi and Werner to incorporate generating second guidance information for sharing the deterioration in the quality of the video communication. The motivation provides for providing guidance updates to the user for network conditions.
Regarding claim 18, Shi and Werner do not teach the guidance information providing server of claim 17, wherein the second prompt comprises at least one of attendee information, guidance language information, and a predefined system prompt.
Kumar teaches wherein the second prompt comprises at least one of attendee information, guidance language information, and a predefined system prompt (see fig. 4, col. 4, line 49-col. 5, , line 61. col. 6, lines 49-64, col. 9, lines 22-col. 10, lines 21. The system provides guidance language information (providing information related to the issue) and the system prompts are generated based on the detected issues that the system determines. Language based guidance for guiding the user through network issues).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shi and Werner to incorporate language based guidance for guiding the user through network issues. The motivation provides providing for guidance updates and based on guidance updates, the user can input a response to respond to the system.
Regarding claim 19, Shi and Werner do not teach the guidance information providing server of claim 17, wherein the plurality of operations further comprises providing the generated second guidance information to one or more receiving terminals.
Kumar teaches wherein the plurality of operations further comprises providing the generated second guidance information to one or more receiving terminals (see fig. 4, col. 4, line 49-col. 5, , line 61. col. 6, lines 49-64, col. 9, lines 22-col. 10, lines 21. The system provides guidance language information (providing information related to the issue) and the system prompts are generated based on the detected issues that the system determines. Language based guidance for guiding the user through network issues. The guidance can be provided to one device or more devices.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shi and Werner to incorporate language based guidance for guiding the user through network issues to one device or other devices. The motivation provides for providing guidance updates and based on guidance updates, the user can input a response to respond to the system.
6. Claim(s) 11 is rejected under 35 U.S.C. 103 as being unpatentable over Shi et al. (US 2014/0282086) in further view of Werner et al. (US 2024/0275838) in further view of Birru et al. (US 2024/0095491).
Regarding claim 11, Shi and Werner do not teach the guidance information providing method of claim 1, wherein the generating comprises generating the first guidance information using a large language model (LLM) based on retrieval-augmented generation (RAG).
Birru teaches wherein the generating comprises generating the first guidance information using a large language model (LLM) based on retrieval-augmented generation (RAG) (see ¶ 0111-0118. The AI model continuously learns from historical interactions to upgrade reasoning and response framing, and dynamically adapts user’s accustomed communication style.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shi and Werner to incorporate guidance information using a large language model (LLM) based on retrieval-augmented generation (RAG). The motivation provides AI model continuously learns from historical interactions to upgrade reasoning and response framing, and dynamically adapts user’s accustomed communication style.
Conclusion
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASSAD MOHAMMED whose telephone number is (571)270-7253. The examiner can normally be reached 9:00AM-5:00PM.
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, Duc Nguyen can be reached at 571-272-7503. 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.
/ASSAD MOHAMMED/Examiner, Art Unit 2691
/DUC NGUYEN/Supervisory Patent Examiner, Art Unit 2691
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