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 .
Election/Restrictions
Applicant’s election without traverse of Invention I, claims 1-15, in the reply filed on February 04, 2026 is acknowledged.
Response to Amendment
This Action is in response to the Response to the Restriction Requirement dated December 18, 2025, which included amendments to the claims.
Claims 2-9 are amended.
Claims 16-20 are canceled.
Claims 21-25 are added.
Claims 1-15 and 21-25 are pending.
Priority
Claims 1-15 and 21-25 are deemed to have an effective filing date of February 07, 2024.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference character “715” has been used to designate both hardware structure(s) (Figs. 7A-7B) and inference and/or training logic (Figs. 7-10, as described in the originally-filed specification (OFS)). The Examiner notes that “hardware structure(s)” is not mentioned in the originally-filed specification, although “hardware logic devices and circuit” are mentioned (unnumbered) in relation to the data storage, ALU, and activation storage (e.g., paragraphs [0065], [0069] of the OFS) and “hardware logic” is mentioned as being included with the inference and/or training logic 715 (e.g., paragraph [0071] of the OFS).
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 711 (Fig. 11).
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: 1514 (e.g., paragraph [0159]).
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
In paragraph [0158], last line, of the OFS, “deployment pipelines 1510” is recited, but Fig. 15 illustrates a person designated by reference numeral 1510. Paragraph [0164] of the OFS recites “user 1510” which is consistent with the illustration in Fig. 15.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 8-9, 21-22 and 24-25 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Adib et al. (EFD: 10/31/2022 and hereinafter referred to as “Adib”).
Regarding claim 1, Adib discloses a computer-implemented method (e.g., Abstract: method for measuring stress and paragraphs [0020] and [0057]: monitoring device 102 may interface with a host computer 106 to perform some/all of said processing and to output the user’s stress level), comprising: determining, based at least on sensor data captured using one or more sensors of a computing device, physical health data corresponding to a user of the computing device (e.g., paragraphs [0019]: embodiments of the present disclosure can be incorporated into smart devices (e.g., screens, kiosks, TVs, smart home assistants) to respond to user stress levels – implies that the sensors are of a computing device, which the user is using; and [0021]: processing physiological signals captured using a sensor to extract feature data) including at least one of a heart rate or a heart rate variability of the user; (e.g., paragraphs [0023]: featured data representing heartbeats of the subject; and [0068]-[0070]: heart rate variability including heart rate, respiration, and/or motion features are known to infer stress levels of a subject so the machine learning pipeline of Adib extracts feature data for heart-rate variability, respiration, and motion); determining, using a machine learning model, a current level of anxiety of the user based at least on the physical health data (e.g., paragraphs [0017]: systems and techniques disclosed herein deliver the first fully automated, passive system for monitoring stress by extracting HRV using a machine learning (ML) network; [0069]: at the core of the design is a novel machine learning pipeline that can map captured wireless signals to determine stress levels; and [0140]: simulation module 740 includes a trainable ML model); and adjusting, based at least on the current anxiety level of the user exceeding an anxiety threshold, one or more operational aspects of the computing device to attempt to reduce the current level of anxiety of the user (e.g., paragraphs [0019]: method can be used in everyday environments to enable interactive capabilities (inform intervention mechanisms, such as adapt the tone, colors, etc. of a screen based on user stress levels to boost productivity and reduce burnout).
With respect to claim 2, Adib discloses the method of claim 1, further comprising: capturing additional sensor data of the user using one or more additional sensors of the computing device; determining, based at least on the additional sensor data, additional physical health data for the user; and determining, using the machine learning model, the current level of anxiety of the user based at least on the additional physical health data and the physical health data (e.g., paragraphs [0012]: camera-based methods are known that assess stress by sensing visual cues such as head movements, blink rate, and pupil size variation; [0028]: measuring by a sensor to generate the physiological signal corresponding to an electrocardiogram (ECG), a photoplethysmography (PPG) signal and a seismocardiograph signal; [0160]: processing wireless reflections to extract features may include respiratory and body movement monitoring via an accelerometer which is fed directedly into the stress classification network 220).
As to claim 3, Adib discloses the method of claim 2, wherein the additional physical health data includes at least one of current blood pressure, pupil dilation, a change in voice tone or pitch, respiratory rate, a variation in user input, or a galvanic skin response (e.g., paragraphs [0012]: pupil dilation; [0019]: ECG, GSR, PPG; and [0160]: respiratory rate).
With respect to claim 4, Adib discloses the method of claim 1, wherein the one or more operational aspects include at least one of a color scheme used by a display of the computing device, a brightness of the display, a rate or type of notifications or messages indicated by the computing device, a volume or type of sound or music played, a strength or occurrence of haptic feedback, or an indication to perform one or more user-implemented mitigation actions (e.g., paragraph [0019]: adapting the color, tone, etc. of a screen).
As to claim 8, Adib discloses the method of claim 1, further comprising: analyzing the physical health data to infer a current level of at least one of depression, stress, consciousness, or epileptic behavior (e.g., paragraphs [0017], [0057], and [0074]: disclosed embodiments include a passive stress monitoring system that can infer a user’s stress levels form wireless signals).
With respect to claim 9, Adib discloses the method of claim 1, wherein the method is performed using one or more interfaces exposed to one or more applications executing on the computing device and is able to adjust the one or more operational aspects of the computing device (e.g., paragraph [0182]: peripherals 1308 may include network interfaces 1318, input devices 1320, such as a keyboard, mouse, trackball, touch-sensitive pad or display that is able to adjust the one or more operational aspects of the computing device).
Referring to claim 21, Adib discloses the system, comprising: one or more processors (e.g., paragraph [0191]: the processes and logic flows described in this disclosure including the method steps of the subject matter described herein can be performed by one or more programmable processors) configured to: determine, based at least on sensor data captured using one or more sensors of a computing device, physical health data corresponding to a subject associated with the computing device (e.g., paragraphs [0019]: embodiments of the present disclosure can be incorporated into smart devices (e.g., screens, kiosks, TVs, smart home assistants) to respond to user stress levels – implies that the sensors are of a computing device, which the user is associated with; and [0021]: processing physiological signals captured using a sensor to extract feature data/physical health data that is used to measure stress of the subject); determine a current health state of the subject based at least on the physical health data (e.g., paragraphs [0017]: systems and techniques disclosed herein deliver the first fully automated, passive system for monitoring stress by extracting HRV/physical health data using a machine learning (ML) network; and [0069]: at the core of the design is a novel machine learning pipeline that can map captured wireless signals to determine stress levels); and control one or more operational aspects of the computing device by applying one or more mitigation mechanisms based at least on the current health state of the subject satisfying a health-state criterion (e.g., paragraphs [0019]: method can be used in everyday environments to enable interactive capabilities (inform intervention mechanisms, such as adapt/control the tone, colors, etc. of a screen (operational aspects of the computer) based on user stress levels/current health- state of subject to boost productivity and reduce burnout/satisfy a health-state criterion; and [0183]: the system can perform processing to control the operation of a computer).
With respect to claim 22, Abid discloses the system of claim 21, wherein the physical health data includes at least one of heart rate, heart rate variability, pupil dilation, respiratory rate, blood pressure, or galvanic skin response of the subject (e.g., paragraphs [0012]: pupil dilation; [0017]: heart rate variability (HRV); [0019]: ECG, GSR, PPG; and [0160]: respiratory rate).
As to claim 24, Adib discloses the system of claim 21, wherein selection or application of the one or more mitigation mechanisms is further based on mitigation preferences specified by the subject (e.g., paragraph [0073]: Beyond obtaining spot-level stress checks, system and techniques disclosed herein can be used to track changes in a user’s stress level over extended period of time, paving way for future solutions that would allow users to monitor their stress levels and adapt their daily activities).
With respect to claim 25, Adib discloses the system of claim 21, wherein the system comprises at least one of a system for performing simulation operations (e.g., paragraphs [0090] and [0139]-[0140]: the system performs sparsity simulation); a system for performing simulation operations to test or validate autonomous machine applications; a system for performing digital twin operations; a system for performing light transport simulation; a system for rendering graphical output (e.g., paragraph [0066]); a system for performing deep learning operations (e.g., paragraph [0076]); a system for performing generative AI operations using a large language model (LLM), a system implemented using an edge device; a system for generating or presenting virtual reality (VR) content (e.g., paragraph [0183]: program logic may be run/generated on a virtual processor); a system for generating or presenting augmented reality (AR) content; a system for generating or presenting mixed reality (MR) content; a system incorporating one or more Virtual Machines (VMs); a system implemented at least partially in a data center; a system for performing hardware testing using simulation; a system for synthetic data generation; a collaborative content creation platform for 3D assets; or a system implemented at least partially using cloud computing resources (e.g., paragraph [0066]: processing device 124 and output device 126 may communicated over a computer network such as the Internet – implies at least partially using cloud computing resources).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 5-6, 10-15, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Adib in view of US Patent Application Publication No. 2022/0139554 to Pillay et al. (hereinafter referred to as “Pillay”).
With respect to claim 5, Adib discloses the method of claim 1, but does not expressly disclose monitoring a response of the user to adjustments of the one or more operational aspects; and making one or more additional adjustments to the one or more operational aspects based in part on the response. However, Pillay, in a related art: computer-implemented method for treating a medical condition (e.g., abstract), teaches that feedback may be provided by a user in response to passive interventions (e.g., consumable content) (e.g., Pillay paragraphs [0069]-[0071]: a script is generated based on user inputs including HRV where a therapeutic platform is disclosed to provide an adaptive and personalized digital approach to stress reduction therapy; [0088]-[0092], Fig. 4A: the consumable content (passive interventions including adjusting its refresh rate, dimensionality, colors, aspect ratio, size, quality) is based on user inputs where the user my control/adjust/interact with the consumable content; [0112]-[0114]: feedback may be generated based on user’s consumption of the consumable content where the feedback is provided by the user, a third party, or one or more sensors to update the user’s treatment to determine an optimal treatment given an increase in heartbeat). Accordingly, one of ordinary skill in the art would have recognized the benefits of monitoring a response of the user to adjustments of the one or more operational aspects and making one or more additional adjustments to the one or more operational aspects based in part on the response in view of the teachings of Pillay. Consequently, one of ordinary skill in the art would have modified the method of Adib to further include monitoring a response of the user to adjustments made to operational aspects; and making additional adjustments to the one or more operational aspects based in part on the response in view of the teachings of Pillay that such was a known protocol in the computer-implemented medical treatment art, and because the combination would have yielded a predictable result.
As to claim 6, Adib in view of Pillay teaches the method of claim 5, further comprising: providing data, based at least on the one or more of the response or the adjustments, as at least one of: additional training data for the machine learning model; or additional data used to create or update an anxiety profile for the user. However, Pillay teaches, in a related art, that a paradigm may be selected to target anxiety where the script machine learning model at a decision tree is updated based on previous user’s pre-and post- content inputs/feedback and/or a given individual user’s historical usage (e.g., paragraphs [0076], [0113], [0116], and [0119]-[0120] of Pillay: where the machine learning model may be trained using the data flow 430 of Fig. 4E including comparison results that update the training component of the corresponding machine learning model). Accordingly, one of ordinary skill in the art would haver recognized the benefits of providing data, based on the one or more of the response or the adjustments, as additional training data for the machine learning model in view of the further teachings of Pillay. Consequently, one of ordinary skill in the art would have modified the computer-implemented method of Adib in view of Pillay to provide data, based at least on the one or more of the response or adjustments, as additional training data for the machine learning model in view of the teachings of Pillay that such was a known protocol in the computer-implemented method medical treatment art, and because the combination would have yielded a predictable result.
With respect to claim 7, Adib discloses the method of claim 1, but does not expressly disclose allowing the user to activate anxiety monitoring and specify types or extents of adjustments that are able to be made to the one or more operational aspects in response to the current level of anxiety of the user exceeding the anxiety threshold. However, Pillay, in a related art, teaches an artificial intelligences platform (omnichannel therapeutic system 400) may identify one or more platforms to implement for a given patient where a determination may be made to address brain-based anxiety reduction using the therapeutic platform (e.g., omnichannel therapeutic system 400) thereby activating anxiety monitoring (e.g., paragraphs [0147]: paradigms may be brain-based to reflect an understanding of a predominant brain circuit implicated by anxiety or psychedelic relief where the user may benefit from addressing a given paradigm from the plurality of paradigm; and [0181] of Pillay) and that the user can specify the types or extends of adjustments that are able to be made to the operational aspects (e.g., paragraph [0088] of Pillay: consumable content may be determined based on a user preference). Accordingly, one of ordinary skill in the art would have recognized the benefits of allowing the user to activate anxiety monitoring and to specify types of extends of adjustments to the one or more operational aspects in view of the teachings of Pillay. Consequently, one of ordinary skill in the art would have modified the computer-implemented method of Adib to allow the user to activate anxiety monitoring and to specify types or extents of adjustments to the one or more operational aspects in view of the teachings of Pillay that such was a well-known protocol in the computer-implemented medical treatment art for anxiety, and because the combination would have yielded a predictable result.
Referring to claim 10, Adib discloses a processor comprising: one or more circuits to (e.g., paragraphs [0063]: digital circuitry of processing device 124; [0190]-[0192]: the processes and logic flows described in this disclosure including the method steps of the subject matter described herein can be performed by one or more programmable processors … logic circuitry): determine, based in part on sensor data of a user captured using one or more sensors of a computing device, current health data for the user (e.g., paragraphs [0019]: embodiments of the present disclosure can be incorporated into smart devices (e.g., screens, kiosks, TVs, smart home assistants) to respond to user stress levels – implies that the sensors are of a computing device; and [0021]: processing physiological signals captured using a sensor to extract feature data that is analyzed to determine a stress level/ anxiety of the subject – [0004]: chronic stress is known to be correlated with increased risk of depression fatigue, anxiety and insomnia ); analyze, using a machine learning model, the current health data to determine a current level of anxiety of the user (e.g., paragraphs [0017]: systems and techniques disclosed herein deliver the first fully automated, passive system for monitoring stress by extracting HRV using a machine learning (ML) network; and [0069]: at the core of the design is a novel machine learning pipeline that can map captured wireless signals to determine stress levels); but does not expressly teach a processor its stress/anxiety level exceeds a threshold. However, Pillay, in a related art: computer-implemented method for treating a medical condition (e.g., abstract), teaches when a threshold level is exceeded indicating an adjustment to consumable content, adjustments can be made to one or more operational aspects of the computing device (e.g., paragraphs [0114] and [0116] of Pillay: post treatment feedback may be received an da determination may be made that the user would benefit from additional content and/or changes to treatment via omnichannel therapeutic system where when user’s heartbeat has exceeded a threshold – indicating high stress/anxiety, adjustments are made to the content induces calm- that reduces stress as in paragraph [0019] of Adib). Accordingly, one of ordinary skill in the art would have recognized the benefits of the processor using threshold values to determine when to adjust consumable content so that the user is calmed in view of the teachings of Pillay. Consequently, one of ordinary skill in the art would have modified the processor Adib so that when the determined level of stress exceeds a threshold, adjustments are made to at least one operational aspect of the computing device in order to attempt to reduce the current level of stress/anxiety of the user in view of the teachings of Pillay that such was a well-known protocol in the computer-implemented medical treatment arts, and because the combination would have yielded a predictable result.
With respect to claim 11, Adib in view of Pillay teaches the processor of claim 10, wherein the current health data includes at least one of heart rate, heart rate variability, pupil dilation, response time, galvanic response, respiratory rate, voice pitch, pattern of motion, expression, or blood pressure of the user (e.g., Adib paragraphs [0012]: pupil dilation; [0017]: heart rate variability (HRV); [0019]: ECG, GSR, PPG; and [0160]: respiratory rate) .
As to claim 12, Adib in view of Pillay teaches the processor of claim 11, wherein the one or more sensors include at least one of a camera, an infrared imaging sensor, a depth sensor, a motion sensor, a fingerprint scanner, a microphone, a haptic sensor, or a light sensor (e.g., Adib paragraphs [0012]: cameral-based methods are known to assess stress level by sensing visual cues such as pupil dilation and [0017]: motion-based features from a motion sensor).
With respect to claim 13, Abid in view of Pillay teaches the processor of claim 11, wherein the at least one operational aspect includes at least one of a color scheme used by a display of the computing device, a brightness of the display, a rate or type of notifications or messages indicated by the computing device, a volume or type of sound or music played, a strength or occurrence of haptic feedback, or a signal to perform one or more user-indicated mitigation actions (e.g., paragraph [0019] of Adib: adapting the color, tone, etc. of a screen).
As to claim 14, Adib in view of Pillay teaches the processor of claim 10, wherein the one or more circuits are further to: monitor a response of the user to adjustments of the at least one operational aspect; and perform one or more additional adjustments based in part on the response (see rejection of claim 5 above).
With respect to claim 15, Abid in view of Pillay teaches the processor of claim 10, wherein the processor is comprised in at least one of: a system for performing simulation operations; a system for performing simulation operations to test or validate autonomous machine applications; a system for performing digital twin operations; a system for performing light transport simulation; a system for rendering graphical output; a system for performing deep learning operations; a system implemented using an edge device; a system for generating or presenting virtual reality (VR) content; a system for generating or presenting augmented reality (AR) content; a system for generating or presenting mixed reality (MR) content; a system incorporating one or more Virtual Machines (VMs); a system implemented at least partially in a data center; a system for performing hardware testing using simulation; a system for synthetic data generation; a system for performing generative AI operations using a large language model (LLM), a collaborative content creation platform for 3D assets; or a system implemented at least partially using cloud computing resources (see rejection of claim 25 above).
As to claim 23, Adib discloses the system of claim 21, but does not expressly disclose that the health-state criterion comprises a threshold learned for the subject over time based on historical physical health data corresponding to the subject. However, Pillay, in a related art, teaches that user inputs (physical health data) may be provided periodically based on system determined thresholds (e.g., paragraph [0081] of Pillay); that a machine learning model may receive various inputs for a given user of the therapeutic platform and may determine how the inputs relate to clinical symptoms, etc. and determine output recommendations based on the machine learning correlations (e.g., paragraph [0073] of Pillay); and that based on the inputs, a paradigm may be selected to target a specific brain anxiety where the machine learning model is updated based on a given user’s historical usage (e.g., paragraph [0076] of Pillay). Accordingly, one of ordinary skill in the art would have recognized the benefits of a health-state criterion comprising a threshold learned for the subject over time based on historical physical health data corresponding to the subject in view of the teachings of Pillay, and because the combination would have yielded a predictable result.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent Application Publication No. 2022/0176065 to Youngblood et al is directed to a stress reduction system including sensors and machine learning.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CATHERINE M VOORHEES whose telephone number is (571)270-3846. The examiner can normally be reached Monday-Friday 8:30 AM to 4:30 PM.
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/CATHERINE M VOORHEES/Primary Examiner, Art Unit 3792