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 .
Response to Amendment
Claims 1-3, 5-9, 12-17, & 19-24 were previously pending in this application. The amendment filed 07 May 2026 has been entered and the following has occurred: Claims 1-3, 5-7, 9-12, 14, 16-18, 21, 22, & 24-25 have been amended. Claims 25-34 have been added. Claims 4, 6, 10-16, 18, 19, & 21-23 have been cancelled.
Claims 1-3, 5, 7-9, 17, 20, & 24-34 remain pending in the application.
Claim Rejections – 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Regarding Claim 9, the claim does not fall within at least one of the four categories of patent eligible subject matter because the claim recites “a computer-readable storage medium storing computer-readable instructions which, when executed by a processor, cause the processor to perform acts”. Under broadest reasonable interpretation, “a computer-readable storage medium” includes transitory forms of signal transmission, such as a propagating electrical or electromagnetic signal or carrier wave. Therefore, according to MPEP 2106.03(I), the claims are directed towards non-statutory subject matter. Examiner recommends adding language “non-transitory” before “computer-readable storage medium” to ensure that the BRI does not include said transitory forms of signal transmission.
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.
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 1-3, 5, 7-9, 17, 20, 24-26, 29-30, & 32-34 are rejected under 35 U.S.C. 103 as being unpatentable over Quy et al. (U.S. Patent Publication No. 2021/0118323), hereinafter “Quy”, in view of Amble et al. (U.S. Patent Publication No. 2020/0221951), hereinafter “Amble”.
Claim 1 –
Regarding Claim 1, Quy discloses a method for providing a user characteristic to a service provider for a virtual conference with a user, the method comprising:
collecting, by a user computing device (See Quy Par [0017] which discloses an emotion monitoring device (EMD) being implemented on a number of devices, such as, a user’s tablet computer, smart display, netbook computer, laptop, personal computer), raw video or audio data associated with a user during a virtual conference between the user and the service provider (See Quy Par [0015] which discloses a biosensor that records physiological signals which are integrated into an augmented or virtual reality device; See Quy Par [0013]-[0014] which discloses emotion monitors being connected to a network to share emotion data such as during video calls for social and business interactions and/or teletherapy, e.g. virtual group therapy; See Quy Par [0040] which discloses the emotion data displayed for review by a therapist such that the virtual conference would be between the user and the service provider, i.e. therapist; See Quy Par [0021] which discloses a voice, i.e. audio, or video call such that the algorithm extracts voice features from the audio signal associated with the video call, and further discloses various body language features can be identified and extracted for analysis from the video signal, i.e. video data, such as posture, head movements, or fidgeting),
extracting, by the user computing device during the virtual conference (See Quy Par [0015] which discloses a biosensor that records physiological signals which are integrated into an augmented or virtual reality device; See Quy Par [0013]-[0014] which discloses emotion monitors being connected to a network to share emotion data such as during video calls for social and business interactions and/or teletherapy, e.g. virtual group therapy; See Quy Par [0040] which discloses the emotion data displayed for review by a therapist such that the virtual conference would be between the user and the service provider, i.e. therapist), intermediate user data from the raw video or audio data by performing a first processing of the raw video or audio data (See Quy Par [0017] which discloses the physiological signals being transmitted to an emotion monitoring device (EMD) and further discloses the EMD processing the physiological signals to derive and display emotion data, such as arousal and valence components, thereby constituting processed raw media data, i.e. intermedia user data; See Quy Par [0020] which discloses the use of one or more of a camera image, webcam, etc. for capturing raw media data and categorizing behavioral data from said captured raw media data; See Quy Par [0021] which discloses a voice, i.e. audio, or video call such that the algorithm extracts voice features from the audio signal associated with the video call, and further discloses various body language features can be identified and extracted for analysis from the video signal, i.e. video data, such as posture, head movements, or fidgeting), wherein the intermediate user data comprises one or more of a physiological waveform extracted (See Quy Par [0017] which discloses the physiological signals, i.e. physiological waveform, being transmitted to an emotion monitoring device (EMD) and further discloses the EMD processing the physiological signals to derive and display emotion data, such as arousal and valence components, thereby constituting processed raw media data, i.e. intermedia user data; See Quy Par [0020] which discloses the use of one or more of a camera image, webcam, etc. for capturing raw media data and categorizing behavioral data from said captured raw media data; See Quy Par [0021] which discloses voice features extracted from a video call and various body language features, i.e. behavioral signals, and further known techniques classify these features to obtain emotion data, i.e. second processing, being identified and extracted for analysis from the video signal, such as posture, head movements, or fidgeting);
encoding, by the user computing device, the raw video or audio data into encoded media data (See Quy Par [0017] which discloses an emotion monitoring device (EMD) being implemented on a number of devices, such as, a user’s tablet computer, smart display, netbook computer, laptop, personal computer, and the EMD processing/outputting results related to a user’s emotion data such as by displaying said results, constituting “transformed” media data under broadest reasonable interpretation (BRI); See Quy Par [0016] which discloses received signals possibly being processed to enhance signal detection and remove artifacts based on blind signal separation methods or machine learning techniques, also constituting “encoded” media data under BRI; See Quy Par [0020] which discloses the use of one or more of a camera image, webcam, etc. for capturing raw media data and categorizing behavioral data from said captured raw media data; See Quy Par [0021] which discloses a voice, i.e. audio, or video call such that the algorithm extracts voice features from the audio signal associated with the video call, and further discloses various body language features can be identified and extracted for analysis from the video signal, i.e. video data, such as posture, head movements, or fidgeting; See Quy Par [0046] discloses “The biosensor sends the signal to a signal processing unit (SPU) which amplifies the signal and reduces artifact and noise in the signal, but for some types of biosensors, e.g. camera or microphone, this step may be omitted and the signals are processed in a later step to remove artifact and noise, e.g., by discarding signal epochs with poor image or audio quality, and data outliers”, i.e. effectively describing that in some instances a separate step of transforming the “raw media data” into “transformed raw media data” occurs, and that separate step is outside of the process of receiving raw media data, processing said raw media data into intermediate user data, and extracting physiological characteristics and/or behavioral characteristics of the user from said intermediate user data, albeit not recited explicitly for “encoding” said media data per se, which is instead met by Amble as reasoned further below in the rejection),
transmitting, by the user computing device to a remote device (See Quy Par [0017] which discloses an emotion monitoring device (EMD) being implemented on a number of devices, such as, a user’s tablet computer, smart display, netbook computer, laptop, personal computer, and the EMD processing/outputting results related to a user’s emotion data; See Quy Par [0025] which discloses the biometric and emotion data being transmitted to an internet server, or a cloud infrastructure, via a wired or wireless telecommunication network, i.e. remotely or to a remote device), the encoded media data with the intermediate user data to a remote device for second processing of the intermediate user data having the physiological waveform extracted from the raw video or audio data (See Quy Par [0017] which discloses the physiological signals being transmitted to an emotion monitoring device (EMD) and further discloses the EMD processing the physiological signals to derive and display emotion data, such as arousal and valence components, thereby constituting processed raw media data, i.e. intermedia user data and specifically in Par [0017] Quy states that analysis of EMG signals, body heat signatures, voice features, body language, or encoding of facial micro-expressions, (e.g., as monitored by cameras); See Quy Par [0037] which discloses biosensors for collecting one or more physiological signals, including a camera to monitor facial expressions, eye movements, or heart rate (from subtle changes in facial heat signatures, color (which would include blushing), or movement, and further, each of these data streams would naturally include waveforms, because electrical signals are represented by waveforms; See Quy Par [0025] which discloses the biometric and emotion data being transmitted to an internet server, or a cloud infrastructure, via a wired or wireless telecommunication network, albeit not recited for “encoded” said media data per se, which is instead met by Amble as reasoned further below in the rejection), wherein the remote device performs second processing of the intermediate user data that includes extracting one or more of a physiological characteristic and a behavioral characteristic based at least in part upon the physiological waveform (See Quy Par [0016] which discloses received signals possibly being processed to enhance signal detection and remove artifacts based on blind signal separation methods or machine learning techniques, also constituting “transformed” media data under BRI; See Quy Par [0046] which discloses “The biosensor sends the signal to a signal processing unit (SPU) which amplifies the signal and reduces artifact and noise in the signal, but for some types of biosensors, e.g. camera or microphone, this step may be omitted and the signals are processed in a later step to remove artifact and noise, e.g., by discarding signal epochs with poor image or audio quality, and data outliers”, i.e. effectively describing that in some instances a separate step of transforming the “raw media data” into “transformed raw media data” occurs, and that separate step is outside of the process of receiving raw media data, processing said raw media data into intermediate user data, and extracting physiological characteristics and/or behavioral characteristics of the user from said intermediate user data; See Quy Par [0017] which discloses the physiological signals being transmitted to an emotion monitoring device (EMD) and further discloses the EMD processing the physiological signals to derive and display emotion data, such as arousal and valence components, thereby constituting processed raw media data, i.e. intermedia user data; See Quy Par [0025] which discloses the biometric and emotion data being transmitted to an internet server, or a cloud infrastructure, via a wired or wireless telecommunication network; See Quy Par [0038] which discloses the already-processed emotion data being processed, analyzed, and outputted including sharing emotion data among the network of users, constituting a second processing of the intermediate user data; See Quy Par [0039] which discloses the server deriving emotion data from the signals, such as by the extraction techniques described in Quy Par [0017]-[0019] which disclose extracting a physiological characteristic from the intermediate user data, e.g. heart rate activity, EEG, etc., and Quy Par [0019]-[0021] which disclose extracting a behavioral characteristic from the intermediate user data, such as facial expressions, head movements, posture, or fidgeting, i.e. in Quy Par [0021], audio and/or video signal received, algorithm extracts voice features from the audio signal, and second processing, i.e. classifying technique, classifies the voice features to obtain emotion data).
While Quy generally discloses “transforming” video data into “transformed” media data, it is understood by Examiner that “encoding” has a more specific meaning than simply “transforming” data, such as a specific action performed . As such, Quy does not seem to anticipate the following claim limitations that recite “encoding” raw media data and/or “encoded” media data:
encoding, by the computing device, the video data into encoded media data,
the encoded media data being smaller data packets of the video data;
transmitting, by the user computing device to a remote device, the encoded media data with the intermediate user data having the physiological waveform extracted from the raw video or audio data, wherein the remote device performs second processing of the intermediate user data;
However, Amble specifically discloses “encoding” said video data as shown below:
encoding, by the user computing device, the raw video or audio data into encoded media data (It is further understood by Examiner that “compression” comprises encoding data into fewer bits/data packets than the original data (While not relied upon, see Wikipedia “Data Compression” NPL for further evidence of compression comprising “encoding” efforts i.e. encoding information using fewer bits than the original data file/stream), therefore see Amble Par [0026]-[0027] which specifically discloses a system module performing the optimization, compression, and/or transmission of various tele-health data, including audio-video conference streams; See Amble Par [0035] which specifically mentions bandwidth management through application-specific compression algorithms, and further specifically discloses at Amble Par [0069] that the communication between a client and server can occur in the form of data packet);
transmitting, by the user computing device, the encoded media data with the intermediate user data to a remote device for second processing of the intermediate user data (see Amble Par [0026]-[0027] which specifically discloses a system module performing the optimization, compression, and/or transmission of various tele-health data, including audio-video conference streams, and specifically mentions a device integration layer, such that the integration of any third-party medical (digital) device regardless of the output format to integrate with DIL and communicate with remote entities, i.e. devices, in a tele-health session, such as by transmitting data in the correct method/frequency/security to the cloud servers for distribution to remote entities; See Amble Par [0086] which discloses sending digital information to a system portal during an n-way telemedicine session, such that that processing or further analysis of the data can be performed as described in Amble Par [0087]-[0093]).
The disclosure of Amble is directly applicable to the disclosure of Quy because both disclosures share limitations and capabilities such as being directed towards providing and monitoring tele-health sessions over a tele-health platform.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Quy, which already generally discloses “transforming” video data into transformed media data, for the “transforming” to specifically comprise “encoding” and/or compressing of said data, as disclosed by Amble, because this allows for bandwidth management through application-specific compression algorithms, such that real-time auto-configuring for data transmission, i.e., communications based on bandwidth requirements for various telemetry communications, can be performed and further optimized in the system (See Amble Par [0035]).
Claim 2 –
Regarding Claim 2, Quy and Amble disclose the method of claim 1 in its entirety. Quy and Amble further disclose a method, wherein:
the one or more of a physiological signal and a behavioral signal is a waveform (See Quy Par [0015] which discloses the use of biosensors recording physiological signals such that electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG), photoplethysmography (PPG) can be employed, which are all understood by one of ordinary skill in the art to constitute waveform signals, and further discloses PPG including recording heart pulse rate and blood volume pulse such that heart rate variability (HRV) is determined) corresponding to one or more of a frequency (See Quy Par [0018] which discloses valence was being associated with HRV, in particular the ratio of low frequency to high frequency (LF/HF) heart rate activity) associated with the user characteristic (See Quy Par [0018] which discloses if LF/HF is low (calibrated for that user) and/or the heart rate range is low (calibrated for that user) this indicates a negative emotional state, i.e. associated with a user characteristic),
the raw video or audio data comprises uncompressed video or audio data, the encoded media data comprises compressed video or audio data (See Quy Par [0037] which discloses biosensors for collecting one or more physiological signals, including a camera to monitor facial expressions, eye movements, or heart rate (from subtle changes in facial heat signatures, color (which would include blushing), or movement, and further, each of these data streams would naturally include waveforms, because electrical signals are represented by waveforms; See Quy Par [0025] which discloses the biometric and emotion data being transmitted to an internet server, or a cloud infrastructure, via a wired or wireless telecommunication network; See Amble Par [0026] which discloses the audio/video channel can be optimized, compressed, and transported, such that if the audio/video channel can be compressed, then it first exists in an uncompressed state; See Amble Par [0035] which discloses application-specific compression algorithms, such as for bandwidth management purposes), and
the physiological waveform is extracted by the user computing device from the uncompressed video or audio data prior to sending the physiological waveform to the remote device together with the compressed video or audio data (See Quy Par [0017] which discloses the physiological signals being transmitted to an emotion monitoring device (EMD) and further discloses the EMD processing the physiological signals to derive and display emotion data, such as arousal and valence components, thereby constituting processed raw media data, i.e. intermedia user data and specifically in Par [0017] Quy states that analysis of EMG signals, body heat signatures, voice features, body language, or encoding of facial micro-expressions, (e.g., as monitored by cameras); See Quy Par [0037] which discloses biosensors for collecting one or more physiological signals, including a camera to monitor facial expressions, eye movements, or heart rate (from subtle changes in facial heat signatures, color (which would include blushing), or movement, and further, each of these data streams would naturally include waveforms, because electrical signals are represented by waveforms; See Amble Par [0026] which discloses the audio/video channel can be optimized, compressed, and transported, such that if the audio/video channel can be compressed, then it first exists in an uncompressed state).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Quy, which already generally discloses “transforming” video data into transformed media data, for the “transforming” to specifically comprise “encoding” and/or compressing of said data, as disclosed by Amble, because this allows for bandwidth management through application-specific compression algorithms, such that real-time auto-configuring for data transmission, i.e., communications based on bandwidth requirements for various telemetry communications, can be performed and further optimized in the system (See Amble Par [0035]).
Claim 3 –
Regarding Claim 3, Quy and Amble disclose the method of claim 1 in its entirety. Quy further discloses a method, wherein:
the raw video or audio data is collected using a sensor communicatively coupled to the user computing device (“a sensor” under BRI, could include any camera, or other tool for collecting patient/user data, therefore see Quy Par [0037] which discloses physiological signals being transmitted to an emotion monitoring device, such as a mobile device, e.g. smart phone, etc., coupled via wired or short-range wireless connection; See Quy Par [0037] which discloses biosensors for collecting one or more physiological signals, including a camera to monitor facial expressions, eye movements, or heart rate (from subtle changes in facial heat signatures, color (which would include blushing), or movement, and further, each of these data streams would naturally include waveforms, because electrical signals are represented by waveforms).
Claim 5 –
Regarding Claim 5, Quy and Amble disclose the method of claim 1 in its entirety. Quy and Amble further disclose a method, further comprising:
assigning different authorization levels to different physiological and behavioral characteristics of the user (See Amble Par [0037] which defines a role-based access schema for tele-health delivery that factors in pre-defined sets of views for any given role and Amble Par [0076] which discloses the module being used for identification of user roles (such as Physician, Clinician, patient/resident, HIPAA-authorized member, non-authorized member, system admin, etc.); and
selectively restricting access by the service provider to individual physiological and behavioral characteristics based at least on the different authorization levels (See Amble Par [0076] which discloses a security module that can handle authentication and authorization, such that the security module can be used for identification of user roles (i.e. HIPAA-authorized member, non-authorized member, system admin, Physician, Clinician, etc., which are different authorization levels; See Amble Par [0037] & [0084] which discloses role-based access for distribution of data, such as pre-defined sets of views for any given role/authorization level, i.e. restricting which data is available/presented to the user based on their role/access).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combined disclosure of Quy and Amble, which already discloses a service provider having access to the user characteristics, to assign an authorization level to the service provider indicating which of the plurality of user characteristics are accessible by the service provider, as taught by Amble, to allow for securing data by only allowing viewing of certain data based on the role and access levels of the viewer, thereby ensuring HIPAA compliance (See Amble Par [0037] & [0076]).
Claim 7 –
Regarding Claim 7, Quy and Amble disclose the method of claim 1 in its entirety. Quy further discloses a method, wherein:
the user computing device is a game controller (See Quy Par [0015] which discloses sensors being be integrated into the casing of a mobile phone, game controller), that collects the raw video or audio data and extracts the physiological waveform during a game played by the user via a game server (See Quy Par [0015] which discloses sensors being be integrated into the casing of a mobile phone, game controller; See Quy Par [0024] which discloses the emotion data can be monitored or implemented via various means such as group activities, multiplayer games, etc., to enhance telepresence in video conferencing between remote participants by monitoring and sharing their emotional responses, i.e. participants of a multiplayer game; See Quy Par [0025] which discloses a server application program allowing the interaction with the users, sharing emotion data among multiple users in real time or later as required, such that the interaction could be group activities, multiplayer games as in Quy Par [0024]).
Claim 8 –
Regarding Claim 8, Quy and Amble disclose the method of claim 1 in its entirety. Quy further discloses a method, wherein:
the physiological waveform is at least one of a blushing waveform or a blinking waveform (See Quy Par [0037] which discloses biosensors for collecting one or more physiological signals, including a camera to monitor facial expressions, eye movements (which would include blinking), or heart rate (from subtle changes in facial heat signatures, color (which would include blushing), or movement)).
Claim 9 –
Regarding Claim 9, Quy discloses a non-transitory, computer-readable storage medium storing computer-readable instructions which, when executed by a processor, cause the processor to perform acts comprising:
collecting, raw video or audio data associated with a user during a virtual teleconference (See Quy Par [0017] which discloses an emotion monitoring device (EMD) being implemented on a number of devices, such as, a user’s tablet computer, smart display, netbook computer, laptop, personal computer; See Quy Par [0015] which discloses a biosensor that records physiological signals which are integrated into an augmented or virtual reality device; See Quy Par [0013]-[0014] which discloses emotion monitors being connected to a network to share emotion data such as during video calls for social and business interactions and/or teletherapy, e.g. virtual group therapy; See Quy Par [0021] which discloses a voice, i.e. audio, or video call such that the algorithm extracts voice features from the audio signal associated with the video call, and further discloses various body language features can be identified and extracted for analysis from the video signal, i.e. video data, such as posture, head movements, or fidgeting; See Quy Par [0040] which discloses the emotion data displayed for review by a therapist such that the virtual conference would be between the user and the service provider, i.e. therapist);
extracting intermediate user data from the raw video or audio data (See Quy Par [0015] which discloses a biosensor that records physiological signals which are integrated into an augmented or virtual reality device; See Quy Par [0013]-[0014] which discloses emotion monitors being connected to a network to share emotion data such as during video calls for social and business interactions and/or teletherapy, e.g. virtual group therapy; See Quy Par [0040] which discloses the emotion data displayed for review by a therapist such that the virtual conference would be between the user and the service provider, i.e. therapist; See Quy Par [0017] which discloses the physiological signals being transmitted to an emotion monitoring device (EMD) and further discloses the EMD processing the physiological signals to derive and display emotion data, such as arousal and valence components, thereby constituting processed raw media data, i.e. intermedia user data; See Quy Par [0020] which discloses the use of one or more of a camera image, webcam, etc. for capturing raw media data and categorizing behavioral data from said captured raw media data; See Quy Par [0021] which discloses a voice, i.e. audio, or video call such that the algorithm extracts voice features from the audio signal associated with the video call, and further discloses various body language features can be identified and extracted for analysis from the video signal, i.e. video data, such as posture, head movements, or fidgeting), wherein the intermediate user data comprises a physiological waveform extracted from the raw video or audio data (See Quy Par [0017] which discloses the physiological signals being transmitted to an emotion monitoring device (EMD) and further discloses the EMD processing the physiological signals to derive and display emotion data, such as arousal and valence components, thereby constituting processed raw media data, i.e. intermedia user data; See Quy Par [0020] which discloses the use of one or more of a camera image, webcam, etc. for capturing raw media data and categorizing behavioral data from said captured raw media data; See Quy Par [0021] which discloses voice features extracted from a video call and various body language features, i.e. behavioral signals, and further known techniques classify these features to obtain emotion data, i.e. second processing, being identified and extracted for analysis from the video signal, such as posture, head movements, or fidgeting);
encoding, the raw video or audio data into compressed video or audio data (See Quy Par [0017] which discloses an emotion monitoring device (EMD) being implemented on a number of devices, such as, a user’s tablet computer, smart display, netbook computer, laptop, personal computer, and the EMD processing/outputting results discloses received signals possibly being processed to enhance signal detection and remove artifacts based on blind signal separation methods or machine learning techniques, also constituting “encoded” media data under BRI; See Quy Par [0020] which discloses the use of one or more of a camera image, webcam, etc. for capturing raw media data and categorizing behavioral data from said captured raw media data; See Quy Par [0021] which discloses a voice, i.e. audio, or video call such that the algorithm extracts voice features from the audio signal associated with the video call, and further discloses various body language features can be identified and extracted for analysis from the video signal, i.e. video data, such as posture, head movements, or fidgeting; See Quy Par [0046] discloses “The biosensor sends the signal to a signal processing unit (SPU) which amplifies the signal and reduces artifact and noise in the signal, but for some types of biosensors, e.g. camera or microphone, this step may be omitted and the signals are processed in a later step to remove artifact and noise, e.g., by discarding signal epochs with poor image or audio quality, and data outliers”, i.e. effectively describing that in some instances a separate step of transforming the “raw media data” into “transformed raw media data” occurs, and that separate step is outside of the process of receiving raw media data, processing said raw media data into intermediate user data, and extracting physiological characteristics and/or behavioral characteristics of the user from said intermediate user data, albeit not recited explicitly for “encoding” said media data per se, which is instead met by Amble as reasoned further below in the rejection);
transmitting, the encoded media data with the intermediate user data to a server (See Quy Par [0017] which discloses the physiological signals being transmitted to an emotion monitoring device (EMD) and further discloses the EMD processing the physiological signals to derive and display emotion data, such as arousal and valence components, thereby constituting processed raw media data, i.e. intermedia user data and specifically in Par [0017] Quy states that analysis of EMG signals, body heat signatures, voice features, body language, or encoding of facial micro-expressions, (e.g., as monitored by cameras); See Quy Par [0037] which discloses biosensors for collecting one or more physiological signals, including a camera to monitor facial expressions, eye movements, or heart rate (from subtle changes in facial heat signatures, color (which would include blushing), or movement, and further, each of these data streams would naturally include waveforms, because electrical signals are represented by waveforms; See Quy Par [0025] which discloses the biometric and emotion data being transmitted to an internet server, or a cloud infrastructure, via a wired or wireless telecommunication network, albeit not recited for “encoded” said media data per se, which is instead met by Amble as reasoned further below in the rejection).
While Quy generally discloses “transforming” video data into “transformed” media data, it is understood by Examiner that “encoding” has a more specific meaning than simply “transforming” data, such as a specific action performed . As such, Quy does not seem to anticipate the following claim limitations that recite “encoding” raw media data and/or “encoded” media data:
encoding, by the computing device, the raw video or audio data into compressed video or audio data,
transmitting, by the user computing device, the encoded media data with the intermediate user data to server.
However, Amble specifically discloses “encoding” said video data as shown below:
encoding, by the user computing device, the video data into encoded media data (It is further understood by Examiner that “compression” comprises encoding data into fewer bits/data packets than the original data (While not relied upon, see Wikipedia “Data Compression” NPL for further evidence of compression comprising “encoding” efforts i.e. encoding information using fewer bits than the original data file/stream), therefore see Amble Par [0026]-[0027] which specifically discloses a system module performing the optimization, compression, and/or transmission of various tele-health data, including audio-video conference streams; See Amble Par [0035] which specifically mentions bandwidth management through application-specific compression algorithms, and further specifically discloses at Amble Par [0069] that the communication between a client and server can occur in the form of data packet), the encoded media data being smaller data packets of the video data (See Amble Par [0026]-[0027] which specifically discloses a system module performing the optimization, compression, and/or transmission of various tele-health data, including audio-video conference streams; See Amble Par [0035] which specifically mentions bandwidth management through application-specific compression algorithms, such that real-time auto-configuring for data transmission, such as based on bandwidth requirements for various telemetry communications can be performed, and further specifically discloses at Amble Par [0069] that the communication between a client and server can occur in the form of data packet, such that if said audio-video conference data streams are compressed, they would thereby encode data packets using fewer bits than the original audio-video data stream);
transmitting, by the user computing device, the encoded media data with the intermediate user data to a server (see Amble Par [0026]-[0027] which specifically discloses a system module performing the optimization, compression, and/or transmission of various tele-health data, including audio-video conference streams, and specifically mentions a device integration layer, such that the integration of any third-party medical (digital) device regardless of the output format to integrate with DIL and communicate with remote entities, i.e. devices, in a tele-health session, such as by transmitting data in the correct method/frequency/security to the cloud servers for distribution to remote entities; See Amble Par [0086] which discloses sending digital information to a system portal during an n-way telemedicine session, such that that processing or further analysis of the data can be performed as described in Amble Par [0087]-[0093]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Quy, which already generally discloses “transforming” video data into transformed media data, for the “transforming” to specifically comprise “encoding” and/or compressing of said data, as disclosed by Amble, because this allows for bandwidth management through application-specific compression algorithms, such that real-time auto-configuring for data transmission, i.e., communications based on bandwidth requirements for various telemetry communications, can be performed and further optimized in the system (See Amble Par [0035]).
Claim 17 –
Regarding Claim 17, Quy discloses a computing device for providing a user characteristic to a service provider for a virtual conference with a user, the system comprising:
a processor (See Quy Par [0017] which discloses the use of one or more computers which would include a processor under BRI); and
a computer-readable storage medium storing computer-readable instructions which, when executed by the processor, cause the computing device to (See Quy Par [0017] which discloses an emotion monitoring device (EMD) being implemented on a number of devices, such as, a user’s tablet computer, smart display, netbook computer, laptop, personal computer; See Quy Par [0042] which discloses the use of one or more storage servers, data sources, etc., and further discloses storage of a software application for purposes of performing the steps recited throughout):
collect raw video or audio data associated with a user that participates in a virtual teleconference (See Quy Par [0015] which discloses a biosensor that records physiological signals which are integrated into an augmented or virtual reality device; See Quy Par [0013]-[0014] which discloses emotion monitors being connected to a network to share emotion data such as during video calls for social and business interactions and/or teletherapy, e.g. virtual group therapy; See Quy Par [0040] which discloses the emotion data displayed for review by a therapist such that the virtual conference would be between the user and the service provider, i.e. therapist), wherein
extract intermediate user data from the raw video or audio data by performing a first processing of the video data (See Quy Par [0015] which discloses a biosensor that records physiological signals which are integrated into an augmented or virtual reality device; See Quy Par [0013]-[0014] which discloses emotion monitors being connected to a network to share emotion data such as during video calls for social and business interactions and/or teletherapy, e.g. virtual group therapy; See Quy Par [0040] which discloses the emotion data displayed for review by a therapist such that the virtual conference would be between the user and the service provider, i.e. therapist; See Quy Par [0017] which discloses the physiological signals being transmitted to an emotion monitoring device (EMD) and further discloses the EMD processing the physiological signals to derive and display emotion data, such as arousal and valence components, thereby constituting processed raw media data, i.e. intermedia user data; See Quy Par [0020] which discloses the use of one or more of a camera image, webcam, etc. for capturing raw media data and categorizing behavioral data from said captured raw media data; See Quy Par [0021] which discloses a voice, i.e. audio, or video call such that the algorithm extracts voice features from the audio signal associated with the video call, and further discloses various body language features can be identified and extracted for analysis from the video signal, i.e. video data, such as posture, head movements, or fidgeting), the intermediate user data comprising a physiological waveform (See Quy Par [0015] which discloses a biosensor that records physiological signals which are integrated into an augmented or virtual reality device; See Quy Par [0013]-[0014] which discloses emotion monitors being connected to a network to share emotion data such as during video calls for social and business interactions and/or teletherapy, e.g. virtual group therapy; See Quy Par [0040] which discloses the emotion data displayed for review by a therapist such that the virtual conference would be between the user and the service provider, i.e. therapist; See Quy Par [0017] which discloses the physiological signals being transmitted to an emotion monitoring device (EMD) and further discloses the EMD processing the physiological signals to derive and display emotion data, such as arousal and valence components, thereby constituting processed raw media data, i.e. intermedia user data; See Quy Par [0021] which discloses voice features extracted from a video call and various body language features, i.e. behavioral signals, and further known techniques classify these features to obtain emotion data, i.e. second processing, being identified and extracted for analysis from the video signal, such as posture, head movements, or fidgeting);
compress the raw video or audio data into compressed video or audio data (See Quy Par [0017] which discloses an emotion monitoring device (EMD) being implemented on a number of devices, such as, a user’s tablet computer, smart display, netbook computer, laptop, personal computer, and the EMD processing/outputting results related to a user’s emotion data such as by displaying said results, constituting “transformed” media data under broadest reasonable interpretation (BRI); See Quy Par [0016] which discloses received signals possibly being processed to enhance signal detection and remove artifacts based on blind signal separation methods or machine learning techniques, also constituting “encoded” media data under BRI; See Quy Par [0020] which discloses the use of one or more of a camera image, webcam, etc. for capturing raw media data and categorizing behavioral data from said captured raw media data; See Quy Par [0021] which discloses a voice, i.e. audio, or video call such that the algorithm extracts voice features from the audio signal associated with the video call, and further discloses various body language features can be identified and extracted for analysis from the video signal, i.e. video data, such as posture, head movements, or fidgeting; See Quy Par [0046] discloses “The biosensor sends the signal to a signal processing unit (SPU) which amplifies the signal and reduces artifact and noise in the signal, but for some types of biosensors, e.g. camera or microphone, this step may be omitted and the signals are processed in a later step to remove artifact and noise, e.g., by discarding signal epochs with poor image or audio quality, and data outliers”, i.e. effectively describing that in some instances a separate step of transforming the “raw media data” into “transformed raw media data” occurs, and that separate step is outside of the process of receiving raw media data, processing said raw media data into intermediate user data, and extracting physiological characteristics and/or behavioral characteristics of the user from said intermediate user data, albeit not recited explicitly for “encoding” said media data per se, which is instead met by Amble as reasoned further below in the rejection);
transmit the compressed video or audio data with the intermediate user data having the physiological waveform to a server (See Quy Par [0017] which discloses the physiological signals being transmitted to an emotion monitoring device (EMD) and further discloses the EMD processing the physiological signals to derive and display emotion data, such as arousal and valence components, thereby constituting processed raw media data, i.e. intermedia user data; See Quy Par [0025] which discloses the biometric and emotion data being transmitted to an internet server, or a cloud infrastructure, via a wired or wireless telecommunication network, albeit not recited for “encoded” said media data per se, which is instead met by Amble as reasoned further below in the rejection);.
While Quy generally discloses “transforming” video data into “transformed” media data, it is understood by Examiner that “encoding” has a more specific meaning than simply “transforming” data, such as a specific action performed . As such, Quy does not seem to anticipate the following claim limitations that recite “encoding” raw media data and/or “encoded” media data:
encode the video data into encoded media data,
the encoded media data being smaller data packets of the video data;
transmit the encoded media data with the intermediate user data to a server.
However, Amble specifically discloses “encoding” said video data as shown below:
encode the video data into encoded media data (It is further understood by Examiner that “compression” comprises encoding data into fewer bits/data packets than the original data (While not relied upon, see Wikipedia “Data Compression” NPL for further evidence of compression comprising “encoding” efforts i.e. encoding information using fewer bits than the original data file/stream), therefore see Amble Par [0026]-[0027] which specifically discloses a system module performing the optimization, compression, and/or transmission of various tele-health data, including audio-video conference streams; See Amble Par [0035] which specifically mentions bandwidth management through application-specific compression algorithms, and further specifically discloses at Amble Par [0069] that the communication between a client and server can occur in the form of data packet), the encoded media data being smaller data packets of the video data (See Amble Par [0026]-[0027] which specifically discloses a system module performing the optimization, compression, and/or transmission of various tele-health data, including audio-video conference streams; See Amble Par [0035] which specifically mentions bandwidth management through application-specific compression algorithms, such that real-time auto-configuring for data transmission, such as based on bandwidth requirements for various telemetry communications can be performed, and further specifically discloses at Amble Par [0069] that the communication between a client and server can occur in the form of data packet, such that if said audio-video conference data streams are compressed, they would thereby encode data packets using fewer bits than the original audio-video data stream);
transmit the encoded media data with the intermediate user data to a server (see Amble Par [0026]-[0027] which specifically discloses a system module performing the optimization, compression, and/or transmission of various tele-health data, including audio-video conference streams, and specifically mentions a device integration layer, such that the integration of any third-party medical (digital) device regardless of the output format to integrate with DIL and communicate with remote entities, i.e. devices, in a tele-health session, such as by transmitting data in the correct method/frequency/security to the cloud servers for distribution to remote entities; See Amble Par [0086] which discloses sending digital information to a system portal during an n-way telemedicine session, such that that processing or further analysis of the data can be performed as described in Amble Par [0087]-[0093]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Quy, which already generally discloses “transforming” video data into transformed media data, for the “transforming” to specifically comprise “encoding” and/or compressing of said data, as disclosed by Amble, because this allows for bandwidth management through application-specific compression algorithms, such that real-time auto-configuring for data transmission, i.e., communications based on bandwidth requirements for various telemetry communications, can be performed and further optimized in the system (See Amble Par [0035]).
Claim 20 –
Regarding Claim 20, Quy and Amble disclose the computing device of claim 17 in its entirety. Quy and Amble disclose a method, wherein:
the computer-readable instructions, when executed by the processor cause the computing device to (See Quy Par [0017] which discloses an emotion monitoring device (EMD) being implemented on a number of devices, such as, a user’s tablet computer, smart display, netbook computer, laptop, personal computer; See Quy Par [0042] which discloses the use of one or more storage servers, data sources, etc., and further discloses storage of a software application for purposes of performing the steps recited throughout):
assign different authorization level to different physiological and behavioral characteristics of the user (See Amble Par [0037] which defines a role-based access schema for tele-health delivery that factors in pre-defined sets of views for any given role and Amble Par [0076] which discloses the module being used for identification of user roles (such as Physician, Clinician, patient/resident, HIPAA-authorized member, non-authorized member, system admin, etc.), wherein
a service provider, is restricted to accessing individual physiological and behavioral characteristics based at least on the different authorization levels (See Amble Par [0076] which discloses a security module that can handle authentication and authorization, such that the security module can be used for identification of user roles (i.e. HIPAA-authorized member, non-authorized member, system admin, Physician, Clinician, etc., which are different authorization levels; See Amble Par [0037] & [0084] which discloses role-based access for distribution of data, such as pre-defined sets of views for any given role/authorization level, i.e. restricting which data is available/presented to the user based on their role/access).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combined disclosure of Quy and Amble, which already discloses a service provider having access to the user characteristics, to assign an authorization level to the service provider indicating which of the plurality of user characteristics are accessible by the service provider, as taught by Amble, to allow for securing data by only allowing viewing of certain data based on the role and access levels of the viewer, thereby ensuring HIPAA compliance (See Amble Par [0037] & [0076]).
Claim 24 –
Regarding Claim 24, Quy and Amble disclose the computing device of claim 17 in its entirety. Quy further discloses a computing device, wherein:
the raw video or audio is collected asynchronously with the virtual teleconference (See Quy Par [0040] which discloses the emotion data displayed for asynchronous review by one or more entities, including a group facilitator, counselor, therapist, and/or virtual therapist, such as for purposes described in Quy Par [0013]-[0014] sharing emotion data such as during video calls for social and business interactions and/or teletherapy, e.g. virtual group therapy, which is understood to constitute a virtual conference).
Claim 25 –
Regarding Claim 25, Quy and Amble disclose the method of claim 2 in its entirety. Quy further discloses a method, wherein:
the physiological waveform includes frequency, duration, and amplitude information extracted from the raw video or audio data (See Quy Par [0018] which discloses ratios of low frequency to high frequency (LF/HF); See Quy Par [0016] which discloses level, spikes, and drops, i.e. amplitudes, of SCL analysis).
Claim 26 –
Regarding Claim 26, Quy and Amble disclose the method of claim 25 in its entirety. Quy further discloses a method, wherein:
the frequency, duration, and amplitude information extracted from the raw video or audio data are transmitted to the remote device together with the compressed video or audio data (See Quy Par [0018] which discloses ratios of low frequency to high frequency (LF/HF); See Quy Par [0016] which discloses level, spikes, and drops, i.e. amplitudes, of SCL analysis).
Claim 29 –
Regarding Claim 29, Quy and Amble disclose the method of claim 27 in its entirety. Quy further discloses a method, wherein:
the physiological characteristic comprises a heart rate of the user (See Quy Par [0016] which disclose biosensors for recording physiological signals relating to emotional states including heart rate).
Claim 30 –
Regarding Claim 30, Quy and Amble disclose the method of claim 28 in its entirety. Quy further discloses a method, wherein:
the physiological characteristic comprises a heart rate variability of the user (See Quy Par [0016]-[0018] which discloses processing physiological signals being processed to derive physiological signals such as heart rate variability).
Claim 32 –
Regarding Claim 32, Quy and Amble discloses the method of claim 2 in its entirety. Quy and Amble further disclose a method, wherein:
receiving, at the remote computing device, the compressed video or audio data with the physiological waveform (See Quy Par [0017] which discloses the physiological signals being transmitted to an emotion monitoring device (EMD) and further discloses the EMD processing the physiological signals to derive and display emotion data, such as arousal and valence components, thereby constituting processed raw media data, i.e. intermedia user data and specifically in Par [0017] Quy states that analysis of EMG signals, body heat signatures, voice features, body language, or encoding of facial micro-expressions, (e.g., as monitored by cameras); See Quy Par [0037] which discloses biosensors for collecting one or more physiological signals, including a camera to monitor facial expressions, eye movements, or heart rate (from subtle changes in facial heat signatures, color (which would include blushing), or movement, and further, each of these data streams would naturally include waveforms, because electrical signals are represented by waveforms; See Amble Par [0026] which discloses the audio/video channel can be optimized, compressed, and transported, such that if the audio/video channel can be compressed, then it first exists in an uncompressed state); and
extracting the physiological characteristic from the physiological waveform (See Quy Par [0017] which discloses the physiological signals being transmitted to an emotion monitoring device (EMD) and further discloses the EMD processing the physiological signals to derive and display emotion data, such as arousal and valence components, thereby constituting processed raw media data, i.e. intermedia user data and specifically in Par [0017] Quy states that analysis of EMG signals, body heat signatures, voice features, body language, or encoding of facial micro-expressions, (e.g., as monitored by cameras); See Quy Par [0037] which discloses biosensors for collecting one or more physiological signals, including a camera to monitor facial expressions, eye movements, or heart rate (from subtle changes in facial heat signatures, color (which would include blushing), or movement, and further, each of these data streams would naturally include waveforms, because electrical signals are represented by waveforms;).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Quy, which already generally discloses “transforming” video data into transformed media data, for the “transforming” to specifically comprise “encoding” and/or compressing of said data, as disclosed by Amble, because this allows for bandwidth management through application-specific compression algorithms, such that real-time auto-configuring for data transmission, i.e., communications based on bandwidth requirements for various telemetry communications, can be performed and further optimized in the system (See Amble Par [0035]).
Claim 33 –
Regarding Claim 33, Quy and Amble disclose the method of claim 32 in its entirety. Quy further discloses a method, further comprising:
generating a report that includes the physiological characteristic extracted from the physiological waveform (While not a “report” per se, see Quy Par [0042]-[0043] & [0047] which discloses the internet server transmitting emotion data to one or more remote users with an EMD such that the emotion data are displayed and transmits physiological signals and emotion data calculated from said signals, such that the emotion data may be displayed for review, such as at a user interface, by others for review and trend analysis of results of similar interactions over time, which effectively describes a “report” under BRI).
Claim 34 –
Regarding Claim 34, Quy and Amble disclose the method of claim 33 in its entirety. Quy further discloses a method, further comprising:
transmitting the report to the user computing device and another computing device associated with the service provider (While not a “report” per se, see Quy Par [0042]-[0043] & [0047] which discloses the internet server transmitting emotion data to one or more remote users with an EMD, i.e. service provider and/or user, such that the emotion data are displayed and transmits physiological signals and emotion data calculated from said signals, such that the emotion data may be displayed for review, such as at a user interface, by others for review and trend analysis of results of similar interactions over time, which effectively describes a “report” under BRI).
Claims 27-28 are rejected under 35 U.S.C. 103 as being unpatentable over Quy, in view of Amble, further in view of Landgraf et al. (U.S. Patent Publication No. 2021/0345934), hereinafter “Landgraf”.
Claim 27 –
Regarding Claim 27, Quy and Amble disclose the method of claim 2 in its entirety. Quy and Amble further disclose a method, wherein:
the physiological waveform includes at least systole and diastole portions (See Quy Par [0037] which discloses biosensors for collecting one or more physiological signals, including a camera to monitor facial expressions, eye movements, or heart rate (from subtle changes in facial heat signatures, color, or movement, and further, each of these data streams would naturally include waveforms, because electrical signals are represented by waveforms; See Amble Par [0026] which discloses the audio/video channel can be optimized, compressed, and transported/transmitted, albeit not recited for systole and diastole portions of the heart data)
Therefore, Quy and Amble effectively disclose collection of physiological waveforms, e.g. heart rate, and analyzing said waveforms for determining various aspects of the user, Quy and Amble are generally silent on the physiological, i.e. heart rate, waveform including systole and diastole specifically per se.
However, Landgraf discloses the physiological waveform includes at least systole and diastole portions (See Landgraf Par [0006] & [0012] which discloses that the ECG data and the audio data are transmitted in a common packet; See Landgraf Par [0171] which discloses a state or condition of the heart may be correlated with a magnitude and a duration of audio data within a frequency band, comprising magnitude and duration of audio in a specific frequency range; See Landgraf Par [0172] which discloses various durations of the audio cycle of heart information representing systole and/or diastole phases of said heart cycle). The disclosure of Landgraf is directly applicable to the disclosure of Quy and Amble, because the disclosures share limitations and capabilities, such as being directed towards receiving and analyzing medical or health data for purposes of monitoring one or more users over time.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combined disclosure of Quy and Amble, which already discloses collection of physiological waveforms, e.g. heart rate, and analyzing said waveforms for determining various aspects of the user to further include the physiological waveform including at least systole and diastole portions, as disclosed by Landgraf, because the state or condition of the heart of the subject may be correlated with a certain audio frequency at a certain time during the audio cycle of the heart, such that said cycles are categorized as systolic and diastolic phases of said cycle, and therefore help in determining the state or condition of the heart of the subject (See Landgraf Par [0172]).
Claim 28 –
Regarding Claim 28, Quy, Amble, and Landgraf disclose the method of claim 27 in its entirety. Quy, Amble, and Landgraf further disclose a method, wherein:
the systole and diastole portions extracted from the raw video or audio data are transmitted to the remote device together with the compressed video or audio data (See Quy Par [0037] which discloses biosensors for collecting one or more physiological signals, including a camera to monitor facial expressions, eye movements, or heart rate (from subtle changes in facial heat signatures, color (which would include blushing), or movement, and further, each of these data streams would naturally include waveforms, because electrical signals are represented by waveforms; See Amble Par [0026] which discloses the audio/video channel can be optimized, compressed, and transported/transmitted; See Landgraf Par [0006] & [0012] which discloses that the ECG data and the audio data are transmitted in a common packet; See Landgraf Par [0171] which discloses a state or condition of the heart may be correlated with a magnitude and a duration of audio data within a frequency band, comprising magnitude and duration of audio in a specific frequency range; See Landgraf Par [0172] which discloses various durations of the audio cycle of heart information representing systole and/or diastole phases of said heart cycle).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combined disclosure of Quy and Amble, which already discloses collection of physiological waveforms, e.g. heart rate, and analyzing said waveforms for determining various aspects of the user to further include the physiological waveform including at least systole and diastole portions, as disclosed by Landgraf, because the state or condition of the heart of the subject may be correlated with a certain audio frequency at a certain time during the audio cycle of the heart, such that said cycles are categorized as systolic and diastolic phases of said cycle, and therefore help in determining the state or condition of the heart of the subject (See Landgraf Par [0172]).
Claim 31 is rejected under 35 U.S.C. 103 as being unpatentable over Quy, in view of Amble, further in view of Vorster et al. (U.S. Patent Publication No. 2021/0353227), hereinafter “Vorster”.
Claim 31 –
Regarding Claim 31, Quy and Amble disclose the method of claim 30 in its entirety. Quy and Amble do not further disclose a method, wherein:
the physiological waveform comprises a dicrotic notch portion.
While Quy and Amble generally disclose collecting a physiological waveform from one or more sensors or from an audio or video feed, Quy and Amble are generally silent on said waveform being a dicrotic notch portion.
However, Vorster discloses the physiological waveform comprises a dicrotic notch portion (See Vorster Par [0101] which disclose monitoring blood flow and identifying a dicrotic notch observed on the down stroke of an arterial pressure waveform and represents aortic or pulmonic valve at the onset of ventricular diastole; See Vorster Par [0102] which discloses monitoring telehealth patients and providing telehealth framework for physicians and patients to interact). The disclosure of Vorster is directly applicable to the disclosure of Quy and Amble, because the disclosures share limitations and capabilities, such as being directed towards monitoring one or more users over time for diagnosing purposes.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combined disclosure of Quy and Amble, which already discloses collecting a physiological waveform from one or more sensors or from an audio or video feed, to further include said waveform being a dicrotic notch portion, because this allows for monitoring he down stroke of an arterial pressure waveform and represents aortic or pulmonic valve at the onset of ventricular diastole for determinations of heart conditions (See Vorster Par [0101]-[0102]).
Response to Arguments
Applicant's arguments filed 21 August 2025 have been fully considered but they are not persuasive:
Regarding 35 U.S.C. 103 rejections of Claims 1-3, 5, 7-9, 17, 20, & 24-34, Applicant argues on p. 9-10 of Arguments/Remarks that Quy and Amble do not teach or suggest the intermediate user data comprising a physiological waveform extracted from the raw video or audio data, because “a mere heart rate does not constitute a physiological waveform”. Therefore, Applicant argues that Quy and Amble do not teach or suggest the entirety of the limitation “extracting, by the user computing device during the virtual conference, intermediate user data from the raw video or audio data by performing a first processing of the raw video or audio data, wherein the intermediate user data comprises one or more of a physiological waveform extracted” and as such, the 35 U.S.C. 103 rejections for independent claims 1, 9, & 17 should be withdrawn. Examiner agrees with Applicant’s arguments. More specifically, Examiner agrees that previously cited portions of Quy for the 35 U.S.C. 103 rejections do not necessarily read on the newly amended portions of the limitation regarding the physiological waveform being extracted from raw video or audio data. However a new ground of rejection under 35 U.S.C. 103 over newly portions of Quy, in view of Amble has been made for claims 1, 9, & 17. These newly cited portions include Quy Par [0017] & [0020]-[0021] which specifically disclose the use of physiological signals and extraction of one or more physiological signals from video or audio data streams. For instance, Quy Par [0017] discloses the physiological signals, i.e. physiological waveform, being transmitted to an emotion monitoring device (EMD) and further discloses the EMD processing the physiological signals to derive and display emotion data, such as arousal and valence components, thereby constituting processed raw media data, i.e. intermedia user data; Quy Par [0020] discloses the use of one or more of a camera image, webcam, etc. for capturing raw media data and categorizing behavioral data from said captured raw media data; and Quy Par [0021] which discloses voice features extracted from a video call and various body language features, i.e. behavioral signals, and further known techniques classify these features to obtain emotion data, i.e. second processing, being identified and extracted for analysis from the video signal, such as posture, head movements, or fidgeting. As such, independent claims 1, 9, & 17 and claims dependent therefrom remain rejected under 35 U.S.C. 103.
Regarding 35 U.S.C. 103 rejections of Claims 1-3, 5, 7-9, 17, 20, & 24-34, Applicant argues on p. 11 of Arguments/Remarks that because independent claims 9 & 17 are substantially similar to independent claim 1, independent claims 9 & 17 are also purportedly allowable over the prior art. Examiner respectfully disagrees with Applicant’s arguments. As discussed above, independent claim 1 remains rejected under 35 U.S.C. 103 over a new ground of rejection. Therefore, Applicant’s argument regarding independent claim 1 being purportedly allowable over the prior art are rendered moot, and independent claims 9 & 17 which are substantially similar to independent claim 1 also remain rejected under 35 U.S.C. 103 over Quy, in view of Amble. As such, independent claims 1, 9, & 17 and claims dependent therefrom remain rejected under 35 U.S.C. 103.
Regarding 35 U.S.C. 103 rejections of Claims 1-3, 5, 7-9, 17, 20, & 24-34, Applicant argues on p. 11-12 of Arguments/Remarks that because dependent claims 2-3, 5, 7-8, 20, 24, & 25-34 are dependent from purportedly allowable independent claims 1, 9, & 17, claims 2-3, 5, 7-8, 20, 24, & 25-34 are also allowable by virtue of dependency from independent claims 1, 9, & 17. Examiner respectfully disagrees with Applicant’s arguments. As discussed above, independent claims 1, 9, & 17 remain rejected under 35 U.S.C. 103 and do not represent allowable subject matter. Therefore, Applicant’s arguments regarding independent claims 1, 9, & 17 being purportedly allowable over the prior art are rendered moot, and therefore dependent claims 2-3, 5, 7-8, 20, 24, & 25-34 also remain rejected under 35 U.S.C. 103. As such, independent claims 1, 9, & 17 and claims dependent therefrom remain rejected under 35 U.S.C. 103.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kapoor et al. (U.S. Patent Publication No. 2017/0049339) discloses a system for collecting biosensor data from a user or patient, transferring said data to a mobile device which then transfers the data to health management server for analysis and notifications, including durations of heart cycles and heart rate, including diastolic and systolic portions of said cycle;
Dinesen et al. (U.S. Patent Publication No. 2022/265202) discloses a system for a portable sound sensor, e.g. a microphone or accelerometer, to be positioned on the skin of the abdominal area of the pregnant woman and is functionally connected to a processing unit which executes a processing algorithm on the captured vascular sound and extracts a signal parameter accordingly.
Applicant's amendment necessitated the new ground of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/H.R./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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