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
The amendment filed 02/06/2026 has been entered. Claims 1-5, 7-20 are pending in the application.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 19, and 20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. While paragraph 90 of the instant specification discusses generating “a feature vector that combines movement data, hormone activity, and context information as features in the vector for input into a machine-learned model that is trained on feature vectors of the three corresponding activity data types measured when the user is experiencing a symptom of a physical condition” it does not mention a time-aligned feature vector, unified dataset, or common measurement interval as required by the current independent claims.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 5, 13, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Vaughan (US 2019/0043610) in view of Powers (US 2019/0365286) and further in view of Ghaffari (US 2017/095670).
Regarding claim 1, Vaughan discloses a method comprising: identifying a chemical stimulus administered to a user to treat a physical condition of the user (Para 0065, Para 0192), wherein an upcoming administration of the chemical stimulus is characterized by at least one of a dose and a time to administer the chemical stimulus (Para 0186); monitoring a plurality of movement signals representative of movement of the user (Para 0063, Para 0106; Para 0150); determining, using a machine-learned model, whether to modify the upcoming administration of the chemical stimulus based on a confidence score produced by the machine-learned model (Para 0131; Para 0137; the system relies on a confidence score to determine a diagnosis or not and that diagnosis information is relied upon to determine a therapy); and in response to determining to modify the upcoming administration of the chemical stimulus based on the confidence score exceeding a threshold confidence: determining a modification to the dose or the time associated with the upcoming administration of the chemical stimulus (0171-0173, Para 0186); and applying the chemical stimulus to the user based on the determined modification (Para 0013, 0024).
While Vaughan teaches using wearable digital monitors to collect a level of activity (Para 0063) and passive data can comprise data on the motion or motions of the user (Para 0106), however, is silent regarding directly relying on an on-body sensors of a wearable symptom intervention assembly to capture the plurality of movement signals. Additionally, Vaughan is silent regarding generating a time-aligned feature vector by combining, into a unified dataset, time- aligned measurements from representative of the monitored plurality of movement signals and one or more of a hormone activity of the user, a previous administration of the chemical stimulus, and motor intent data of the user, each of the measurements captured by sensors of the wearable symptom intervention assembly during a common measurement interval; applying a machine-learned model to the feature vector, the machine-learned model configured to identify an onset of a symptom of the physical condition based on the monitored plurality of movement signals.
Powers teaches an analogous method comprising: monitoring a plurality of movement signals representative of movement of the user, the plurality of movement signals captured by on-body sensors of a wearable symptom intervention assembly (102, Fig 1) (Para 0028-0029); determining, using a machine-learned model configured to identify an onset of a symptom of the physical condition based on the monitored plurality of movement signals (Para 0042).
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 method disclosed by Vaughan to rely on on-body sensors of a wearable symptom intervention assembly to collect the plurality of movement signals as taught by Powers in order to be able to passively and continuously be able to track movement all day to allow the user to see how symptoms arise throughout the day (Para 0029).
Powers further teaches generating a time-aligned feature vector representative of the monitored plurality of movement signals during a common measurement interval (Para 0036-0037, Para 0044); applying a machine-learned model to the feature vector, the machine-learned model configured to identify an onset of a symptom of the physical condition based on the monitored plurality of movement signals (Para 0038; Para 0042).
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 method disclosed by Vaughan to include generating a feature vector representative of the monitored plurality of movement signals during a common measurement interval; applying the machine-learned model to the feature vector, wherein machine-learned model identifies the onset of the symptom as taught by Powers in order to more reliably determine symptom onset (Para 0038).
The modified invention of Vaughan and Powers discloses all of the elements of the invention, however, is silent regarding the feature vector is representative of one or more of a hormone activity of the user, a previous administration of the chemical stimulus, and motor intent data of the user.
Ghaffari teaches monitoring a plurality of movement signals (“EMG signals”, Para 0067) and one or more of a hormone activity of the user (Para 0031), a previous administration of the chemical stimulus, and motor intent data of the user (“EEG signals”, (Para 0067-0068) during a common measurement interval (“real-time”, Para 0059), and applying an algorithm or model to the multiple data streams to identify the onset of the symptom of the physical condition (Para 0030-0031, 0059).
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 feature vector to include the plurality of movement signals and motor intent data of the user during a common measurement interval as taught by Ghaffari in order to better predict symptom events based on multiple data streams (Para 0059, 0072).
Regarding claim 5, the modified invention of Vaughan, Powers, and Ghaffari discloses monitoring hormone activity of the user using a plurality of sensors of a wearable symptom intervention assembly (Para 0033 -Ghaffari) configured to measure at least one of a level of a hormone or a level of a biomolecule regulated by the hormone (Para 0031, 0059 -Ghaffari)
Regarding claim 13, the modified invention of Vaughan, Powers, and Ghaffari discloses all of the elements of the invention as discussed above, however, is silent regarding receiving a plurality of images from a camera, wherein the user is depicted in the plurality of images; and determining a change in user posture depicted in the plurality of images, wherein the machine-learned model is configured to identify the onset of the symptom of the physical condition further based on the change in user posture.
Powers further teaches a plurality of images from a camera, wherein the user is depicted in the plurality of images; and determining a change in user posture depicted in the plurality of images, wherein the machine-learned model is configured to identify the onset of the symptom of the physical condition further based on the change in user posture (Para 0032, Para 0042).
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 method disclosed by Vaughan to include a plurality of images from a camera, wherein the user is depicted in the plurality of images; and determining a change in user posture depicted in the plurality of images, wherein the machine-learned model is configured to identify the onset of the symptom of the physical condition further based on the change in user posture as taught by Powers in order to improve detection of symptoms (Para 0032).
Regarding claim 14, the modified invention of Vaughan, Powers, and Ghaffari discloses all of the elements of the invention as discussed above, however, is silent regarding monitoring motor intent data of the user, the motor intent data including electromyography (EMG) signals; and determining a frequency response of the motor intent data, the frequency response indicative of an energy of muscle activity of the user; determining a measure of fatigue based on a comparison of the frequency response and a rested frequency response profile determined using historical EMG signals, wherein the machine-learned model is configured to identify the onset of the symptom of the physical condition further based on the measure of fatigue.
Ghaffari further teaches monitoring motor intent data of the user, the motor intent data including electromyography (EMG) signals (Par 0030); and determining a frequency response of the motor intent data, the frequency response indicative of an energy of muscle activity of the user (“signal features (e.g. dominant frequency)”, Para 0030, 0059); determining a measure of fatigue based on a comparison of the frequency response and a rested frequency response profile determined using historical EMG signals, wherein the machine-learned model is configured to identify the onset of the symptom of the physical condition further based on the measure of fatigue. (Para 0038, 0062).
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 method disclosed by Vaughan to include monitoring motor intent data of the user, the motor intent data including electromyography (EMG) signals; and determining a frequency response of the motor intent data, the frequency response indicative of an energy of muscle activity of the user; determining a measure of fatigue based on a comparison of the frequency response and a rested frequency response profile determined using historical EMG signals as taught by Ghaffari in order to better predict symptom events based on multiple data streams (Para 0059, 0072).
Claims 2, 3 are rejected under 35 U.S.C. 103 as being unpatentable over Vaughan (US 2019/0043610) in view of Powers (US 2019/0365286) and further in view of Ghaffari (US 2017/095670) and further in view of Mei (US 2020/0155078).
Regarding claim 2, the modified invention of Vaughan, Powers, and Ghaffari discloses receiving historical activity data (Para 0084) collected from a plurality of sensors and training a machine-learned model using the first training set (Para 0042), however, is silent regarding the historical activity data including at least one of historical movement signals, hormone activity, a previous administration of the chemical stimulus, a heart rate, or a respiration rate; labeling the historical activity data with a given symptom label representative of a corresponding symptom characterized by the historical activity data; creating a first training set based on the labeled historical activity data
Mei teaches receiving historical activity data collected from a plurality of sensors configured to monitor a given user's activity data (Para 0062), the historical activity data including at least one of historical movement signals (“gyroscopes for monitoring the body movement traces”, Para 0062), hormone activity, a previous administration of the chemical stimulus, a heart rate, or a respiration rate; labeling the historical activity data with a given symptom label representative of a corresponding symptom characterized by the historical activity data (“human labels”; Para 0063); creating a first training set based on the labeled historical activity data; and training the machine-learned model using the first training set (Para 0066).
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 method to include training the machine-learning model using the training sets based on labeled historical activity data as taught by Mei in order to better identify the early onset of a disease based on symptoms which can improve the prognosis of a person with the disease (Para 0002).
Regarding claim 3, the modified invention of Vaughan, Powers, Ghaffari, and Mei discloses the received historical activity data is collected from sensors of a plurality of wearable symptom intervention assemblies (102, Fig 1; Para 0028-0029 -Powers) monitoring a plurality of users having the physical condition, and further comprising: labeling the monitored plurality of movement signals with a symptom label representative of the symptom characterized by the monitored plurality of movement signals (Para 0065 -Mei); creating a second training set using the labeled plurality of movement signals; and retraining the machine-learned model using the second training set such that the machine- learned model is customized to motions of the user (Para 0066 -Mei).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Vaughan (US 2019/0043610) in view of Powers (US 2019/0365286) and further in view of Ghaffari (US 2017/095670) and further in view of Ong (US 2022/0031194).
Regarding claim 4, the modified invention of Vaughan, Powers, and Ghaffari discloses receiving feedback of the determined modification indicating a measure of approval that the user has with the determined modification (Para 0082 -Vaughan), however, is silent regarding modifying an association between the identified onset of the symptom of the physical condition and the monitored plurality of movement signals; and retraining the machine-learned model using the modified association.
Ong teaches modifying an association between the identified onset of the symptom of the physical condition and the monitored plurality of movement signals; and retraining the machine-learned model using the modified association (Para 0168-0169).
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 method of Vaughan to include modifying an association between the identified onset of the symptom of the physical condition and the monitored plurality of movement signals; and retraining the machine-learned model using the modified association as taught by Ong in order to train the model to fit a user’s lifestyle or activity over time (Para 0169).
Claims 7, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Vaughan (US 2019/0043610) in view of Powers (US 2019/0365286) and further in view of Ghaffari (US 2017/095670) and further in view of Saria (US 2018/0206775).
Regarding claim 7, the modified invention of Vaughan, Powers, and Ghaffari discloses all of the elements of the invention as discussed above, however, is silent regarding determining an "on" time duration of a previous administration of the chemical stimulus, the "on" time duration starting at a first time to administer the chemical stimulus and ending at an occurrence of the symptom after the first time to administer the chemical stimulus, the first occurrence of the symptom identified using the machine-learned model; determining an "off' time duration of the previous administration of the chemical stimulus, the "off' time duration starting at the first occurrence of the symptom and ending at a second time to administer the chemical stimulus after the first time; and wherein determining whether to modify the upcoming administration of the chemical stimulus comprises: in response to determining that the "off' time duration is greater than the "on" time duration, determining to modify the upcoming administration.
Saria teaches determining an "on" time duration of a previous administration of the chemical stimulus, the "on" time duration starting at a first time to administer the chemical stimulus and ending at an occurrence of the symptom after the first time to administer the chemical stimulus, the first occurrence of the symptom identified using the machine-learned model (Para 0081, lines 11-14; Para 0102); determining an "off' time duration of the previous administration of the chemical stimulus, the "off' time duration starting at the first occurrence of the symptom and ending at a second time to administer the chemical stimulus after the first time (Para 0081, lines 11-14; Para 0102); and wherein determining whether to modify the upcoming administration of the chemical stimulus comprises: in response to determining that the "off' time duration is greater than the "on" time duration, determining to modify the upcoming administration (Para 0057, Para 0084).
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 method disclosed by Vaughan to include determining an "on" time duration of a previous administration of the chemical stimulus, the "on" time duration starting at a first time to administer the chemical stimulus and ending at an occurrence of the symptom after the first time to administer the chemical stimulus, the first occurrence of the symptom identified using the machine-learned model; determining an "off' time duration of the previous administration of the chemical stimulus, the "off' time duration starting at the first occurrence of the symptom and ending at a second time to administer the chemical stimulus after the first time; and wherein determining whether to modify the upcoming administration of the chemical stimulus comprises: in response to determining that the "off' time duration is greater than the "on" time duration, determining to modify the upcoming administration as taught by Saria.
Regarding claim 17, the modified invention of Vaughan and Powers discloses the method can be used to treat Parkinson’s with a chemical stimulus (Para 0065 -Vaughan), however, is silent regarding the chemical stimulus is one of levodopa, carbidopa, or baclofen.
Saria teaches that levodopa is a common chemical stimulus used to treat Parkinson’s disease (Para 0009).
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 chemical stimuli in the method disclosed by Vaughan to be Levodopa in order to have a method that uses an anti-parkinsonian medicine that cab help control motor symptoms by increasing dopamine in the brain (Para 0009).
Claim 8, 19, 20 is rejected under 35 U.S.C. 103 as being unpatentable over Vaughan (US 2019/0043610) in view of Powers (US 2019/0365286) and further in view of Ghaffari (US 2017/095670) and further in view of Stewart (US 2005/027911).
Regarding claim 8, the modified invention of Vaughan, Powers, and Ghaffari discloses all of the elements of the invention as discussed above, however, is silent regarding causing the client device associated with the user to render a graphical user interface (GUI) comprising user input fields to approve or reject the determined modification; and in response to receiving a user input indicating that the determined modification is approved, modifying the dose or the time associated with the upcoming administration of the chemical stimulus.
Stewart teaches causing the client device associated with the user to render a graphical user interface (GUI) (32, Fig 4; Para 0106) comprising user input fields to approve or reject the determined modification; and in response to receiving a user input indicating that the determined modification is approved, modifying the dose or the time associated with the upcoming administration of the chemical stimulus (Para 0109-0111; “the operator has the ability to accept or reject the recommended medication therapy at step 104” , which ultimately leads to scheduling of therapy at step 86 if recommended therapy is accepted).
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 method disclosed by Vaughan to render a graphical user interface comprising user input fields to approve or reject the determined modification; and in response to receiving a user input indicating that the determined modification is approved, modifying the dose or the time associated with the upcoming administration of the chemical stimulus as taught by Stewart in order to ensure safe dosing for a patient (Para 0008).
Regarding claim 19, Vaughan discloses a system comprising a non-transitory computer-readable storage medium storing instructions for execution and a hardware processor configured to execute the instructions, the instructions, when executed, cause the hardware processor to perform steps (Para 0163) comprising: identifying a chemical stimulus administered to a user to treat a physical condition of a user (Para 0065, Para 0192), wherein an upcoming administration of the chemical stimulus is characterized by at least one of a dose and a time to administer the chemical stimulus (Para 0186); monitoring a plurality of movement signals representative of movement of the user (Para 0063, Para 0106, Para 0150); determining, using a machine-learned model, whether to modify the upcoming administration of the chemical stimulus based on a confidence score produced by the machine-learned model (Para 0131; Para 0137; the system relies on a confidence score to determine a diagnosis or not and that diagnosis information is relied upon to determine a therapy); and in response to determining to modify the upcoming administration of the chemical stimulus based on the confidence score exceeding a threshold confidence: determining a modification to the dose or the time associated with the upcoming administration of the chemical stimulus (0171-0173, Para 0186); and transmitting the determined modification to a client device (“mobile device”) of the user (Para 0024).
While Vaughan teaches using wearable digital monitors to collect a level of activity (Para 0063) and passive data can comprise data on the motion or motions of the user (Para 0106), however, is silent regarding directly relying on an on-body sensors of a wearable symptom intervention assembly to capture the plurality of movement signals. Additionally, Vaughan is silent regarding generating a time-aligned feature vector by combining, into a unified dataset, time- aligned measurements from representative of the monitored plurality of movement signals and one or more of a hormone activity of the user, a previous administration of the chemical stimulus, and motor intent data of the user, each of the measurements captured by sensors of the wearable symptom intervention assembly during a common measurement interval; applying a machine-learned model to the feature vector, the machine-learned model configured to identify an onset of a symptom of the physical condition based on the monitored plurality of movement signals; and responsive to receiving user input indicating approval of the determined modification, applying the chemical stimulus to the user based on the determined modification.
Powers teaches an analogous method comprising: monitoring a plurality of movement signals representative of movement of the user, the plurality of movement signals captured by on-body sensors of a wearable symptom intervention assembly (102, Fig 1) (Para 0028-0029); determining, using a machine-learned model configured to identify an onset of a symptom of the physical condition based on the monitored plurality of movement signals (Para 0042).
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 method disclosed by Vaughan to rely on on-body sensors of a wearable symptom intervention assembly to collect the plurality of movement signals as taught by Powers in order to be able to passively and continuously be able to track movement all day to allow the user to see how symptoms arise throughout the day (Para 0029).
Powers further teaches generating a time-aligned feature vector representative of the monitored plurality of movement signals during a common measurement interval (Para 0036-0037, Para 0044); applying a machine-learned model to the feature vector, the machine-learned model configured to identify an onset of a symptom of the physical condition based on the monitored plurality of movement signals (Para 0038; Para 0042).
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 method disclosed by Vaughan to include generating a feature vector representative of the monitored plurality of movement signals during a common measurement interval; applying the machine-learned model to the feature vector, wherein machine-learned model identifies the onset of the symptom as taught by Powers in order to more reliably determine symptom onset (Para 0038).
The modified invention of Vaughan and Powers discloses all of the elements of the invention, however, is silent regarding the feature vector is representative of one or more of a hormone activity of the user, a previous administration of the chemical stimulus, and motor intent data of the user.
Ghaffari teaches monitoring a plurality of movement signals (“EMG signals”) and one or more of a hormone activity of the user, a previous administration of the chemical stimulus, and motor intent data of the user (“EEG signals”) during a common measurement interval (Para 0067-0068), and applying an algorithm or model to the multiple data streams to identify the onset of the symptom of the physical condition (Para 0030-0031, 0059).
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 feature vector to include the plurality of movement signals and motor intent data of the user during a common measurement interval as taught by Ghaffari in order to better predict symptom events based on multiple data streams (Para 0059, 0072).
The modified invention of Vaughan, Powers, and Ghaffari discloses all of the elements of the invention as discussed above, however, is silent regarding responsive to receiving user input indicating approval of the determined modification, applying the chemical stimulus to the user based on the determined modification.
Stewart teaches causing the client device associated with the user to render a graphical user interface (GUI) (32, Fig 4; Para 0106) comprising user input fields to approve or reject the determined modification; and in response to receiving a user input indicating that the determined modification is approved, modifying the dose or the time associated with the upcoming administration of the chemical stimulus (Para 0109-0111; “the operator has the ability to accept or reject the recommended medication therapy at step 104” , which ultimately leads to scheduling of therapy at step 86 if recommended therapy is accepted).
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 method disclosed by Vaughan to render a graphical user interface comprising user input fields to approve or reject the determined modification; and in response to receiving a user input indicating that the determined modification is approved, modifying the dose or the time associated with the upcoming administration of the chemical stimulus as taught by Stewart in order to ensure safe dosing for a patient (Para 0008).
Regarding claim 20, Vaughan discloses a non-transitory computer readable storage medium storing executable instructions that, when executed by one or more processors, cause the one or more processors to perform steps (Para 0163) comprising: identifying a chemical stimulus administered to a user to treat a physical condition of a user (Para 0065, Para 0192), wherein an upcoming administration of the chemical stimulus is characterized by at least one of a dose and a time to administer the chemical stimulus (Para 0186); monitoring a plurality of movement signals representative of movement of the user (Para 0063, Para 0106, Para 0150); determining, using a machine-learned model configured to identify an onset of a symptom of the physical condition based on the monitored plurality of movement signals, whether to modify the upcoming administration of the chemical stimulus; and in response to determining to modify the upcoming administration of the chemical stimulus: determining a modification to the dose or the time associated with the upcoming administration of the chemical stimulus (0171-0173, Para 0186); and transmitting the determined modification to a client device (“mobile device”) of the user (Para 0024).
While Vaughan teaches using wearable digital monitors to collect a level of activity (Para 0063) and passive data can comprise data on the motion or motions of the user (Para 0106), however, is silent regarding directly relying on an on-body sensors of a wearable symptom intervention assembly to capture the plurality of movement signals. Additionally, Vaughan is silent regarding generating a time-aligned feature vector by combining, into a unified dataset, time- aligned measurements from representative of the monitored plurality of movement signals and one or more of a hormone activity of the user, a previous administration of the chemical stimulus, and motor intent data of the user, each of the measurements captured by sensors of the wearable symptom intervention assembly during a common measurement interval; applying a machine-learned model to the feature vector, the machine-learned model configured to identify an onset of a symptom of the physical condition based on the monitored plurality of movement signals; and responsive to receiving user input indicating approval of the determined modification, applying the chemical stimulus to the user based on the determined modification.
Powers teaches an analogous method comprising: monitoring a plurality of movement signals representative of movement of the user, the plurality of movement signals captured by on-body sensors of a wearable symptom intervention assembly (102, Fig 1) (Para 0028-0029); determining, using a machine-learned model configured to identify an onset of a symptom of the physical condition based on the monitored plurality of movement signals (Para 0042).
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 method disclosed by Vaughan to rely on on-body sensors of a wearable symptom intervention assembly to collect the plurality of movement signals as taught by Powers in order to be able to passively and continuously be able to track movement all day to allow the user to see how symptoms arise throughout the day (Para 0029).
Powers further teaches generating a time-aligned feature vector representative of the monitored plurality of movement signals during a common measurement interval (Para 0036-0037, Para 0044); applying a machine-learned model to the feature vector, the machine-learned model configured to identify an onset of a symptom of the physical condition based on the monitored plurality of movement signals (Para 0038; Para 0042).
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 method disclosed by Vaughan to include generating a feature vector representative of the monitored plurality of movement signals during a common measurement interval; applying the machine-learned model to the feature vector, wherein machine-learned model identifies the onset of the symptom as taught by Powers in order to more reliably determine symptom onset (Para 0038).
The modified invention of Vaughan and Powers discloses all of the elements of the invention, however, is silent regarding the feature vector is representative of one or more of a hormone activity of the user, a previous administration of the chemical stimulus, and motor intent data of the user.
Ghaffari teaches monitoring a plurality of movement signals (“EMG signals”) and one or more of a hormone activity of the user, a previous administration of the chemical stimulus, and motor intent data of the user (“EEG signals”) during a common measurement interval (Para 0067-0068), and applying an algorithm or model to the multiple data streams to identify the onset of the symptom of the physical condition (Para 0030-0031, 0059).
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 feature vector to include the plurality of movement signals and motor intent data of the user during a common measurement interval as taught by Ghaffari in order to better predict symptom events based on multiple data streams (Para 0059, 0072).
The modified invention of Vaughan, Powers, and Ghaffari discloses all of the elements of the invention as discussed above, however, is silent regarding responsive to receiving user input indicating approval of the determined modification, applying the chemical stimulus to the user based on the determined modification.
Stewart teaches causing the client device associated with the user to render a graphical user interface (GUI) (32, Fig 4; Para 0106) comprising user input fields to approve or reject the determined modification; and in response to receiving a user input indicating that the determined modification is approved, modifying the dose or the time associated with the upcoming administration of the chemical stimulus (Para 0109-0111; “the operator has the ability to accept or reject the recommended medication therapy at step 104” , which ultimately leads to scheduling of therapy at step 86 if recommended therapy is accepted).
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 method disclosed by Vaughan to render a graphical user interface comprising user input fields to approve or reject the determined modification; and in response to receiving a user input indicating that the determined modification is approved, modifying the dose or the time associated with the upcoming administration of the chemical stimulus as taught by Stewart in order to ensure safe dosing for a patient (Para 0008).
Claim 10, 15, 16 are rejected under 35 U.S.C. 103 as being unpatentable over Vaughan (US 2019/0043610) in view of Powers (US 2019/0365286) and further in view of Ghaffari (US 2017/095670) and further in view of Ross (US 2021/0402172).
Regarding claim 10, the modified invention of Vaughan, Powers, and Ghaffari discloses all of the elements of the invention as discussed above, however, is silent regarding determining a movement frequency response of the plurality of movement signals, wherein the machine-learned model is configured to identify the onset of the symptom of the physical condition further based on the movement frequency response.
Ross teaches determining a movement frequency response of the plurality of movement signals (Para 0081, 0101), wherein the machine-learned model is configured to identify the onset of the symptom of the physical condition further based on the movement frequency response (Para 0108).
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 method disclosed by Vaughan to include determining a movement frequency response of the plurality of movement signals, wherein the machine-learned model is configured to identify the onset of the symptom of the physical condition further based on the movement frequency response as taught by Ross in order to extract and rely on relevant features for assessing symptom onset and therapy outcomes (Para 0081, 0104).
Regarding claim 15, the modified invention of Vaughan, Powers, and Ghaffari discloses all of the elements of the invention as discussed above, however, is silent regarding providing a biofeedback to the user, the biofeedback including one or more of a sensory cue to promote a neurotypical movement in the user.
Ross teaches in response to determining to modify the upcoming administration of the chemical stimulus, providing a biofeedback to the user, the biofeedback including one or more of a sensory cue to promote a neurotypical movement in the user (Para 0070).
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 method disclosed by Vaughan to include in response to determining to modify the upcoming administration of the chemical stimulus, providing a biofeedback to the user, the biofeedback including one or more of a sensory cue to promote a neurotypical movement in the user as taught by Ross in order to provide a reminder the user, alert the user of a troubleshooting condition, or to perform a tremor inducing activity to measure a tremor motion (Para 0070).
Regarding claim 16, the modified invention of Vaughan, Powers, and Ghaffari discloses the physical condition is Parkinson's disease (Para 0065), however, is silent regarding the symptom is one of a gait freeze or a tremor.
Ross teaches an analogous method wherein the physical condition is Parkinson’s disease and the symptom is one of a gait freeze or a tremor (Para 0084 “resting tremor (associated with Parkinson's Disease)”).
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 method disclosed by Vaughan to have the symptom that is being identified be a tremor as taught by Ross in order to have a method that can identify symptoms indicative of Parkinson’s to better identify and treat users with Parkinson’s disease (Para 0024, Para 0085).
Claims 9, 11, 12 are rejected under 35 U.S.C. 103 as being unpatentable over Vaughan (US 2019/0043610) in view of Powers (US 2019/0365286) and further in view of Ghaffari (US 2017/095670) and further in view of Brokaw (US 9,974,478).
Regarding claim 9, the modified invention of Vaughan, Powers, and Ghaffari discloses the plurality of movement signals is a first plurality of movement signals (Para 0063, Para 0150 -Vaughan), however, is silent regarding further comprising: measuring a second plurality of movement signals at a first joint of the user; measuring a third plurality of movement signals at a second joint of the user, the second joint symmetric about the sagittal plane to the first joint; determining a first kinematic metric score based on a comparison of the second plurality of movement signals to the third plurality of movement signals, the first kinematic metric score indicative of a measure of symmetry of motion about the sagittal plane; generating a baseline movement profile of the first joint using historical movement signals collected at the first joint; and determining a second kinematic metric score based on a comparison of the second plurality of movement signals to the baseline movement profile, the second kinematic metric score indicative of a measure of a variance from an expected movement, wherein the machine-learned model is configured to identify the onset of the symptom of the physical condition further based on at least one of the first kinematic metric score or the second kinematic metric score.
Brokaw teaches measuring a second plurality of movement signals at a first joint of the user; measuring a third plurality of movement signals at a second joint of the user, the second joint symmetric about the sagittal plane to the first joint (Col 21, lines 35-50; Col 34, lines 58-63); determining a first kinematic metric score based on a comparison of the second plurality of movement signals to the third plurality of movement signals, the first kinematic metric score indicative of a measure of symmetry of motion about the sagittal plane; generating a baseline movement profile of the first joint using historical movement signals collected at the first joint; and determining a second kinematic metric score based on a comparison of the second plurality of movement signals to the baseline movement profile, the second kinematic metric score indicative of a measure of a variance from an expected movement, wherein the machine-learned model is configured to identify the onset of the symptom of the physical condition further based on at least one of the first kinematic metric score or the second kinematic metric score (Col 38, line 26- Col 39, line 27).
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 method disclosed by Vaughan to include measuring a second plurality of movement signals at a first joint of the user; measuring a third plurality of movement signals at a second joint of the user, the second joint symmetric about the sagittal plane to the first joint; determining a first kinematic metric score based on a comparison of the second plurality of movement signals to the third plurality of movement signals, the first kinematic metric score indicative of a measure of symmetry of motion about the sagittal plane; generating a baseline movement profile of the first joint using historical movement signals collected at the first joint; and determining a second kinematic metric score based on a comparison of the second plurality of movement signals to the baseline movement profile, the second kinematic metric score indicative of a measure of a variance from an expected movement, wherein the machine-learned model is configured to identify the onset of the symptom of the physical condition further based on at least one of the first kinematic metric score or the second kinematic metric score as taught by Brokaw in order to recognize in that movement data impaired gait, balance, posture and movement patterns and to trigger cues to correct, prevent or otherwise address the behavior based on the recognized impairment (Col 34, lines 58-63).
Regarding claim 11, the modified invention of Vaughan, Powers, and Ghaffari discloses the plurality of movement signals is a first plurality of movement signals (Para 0063, Para 0150 -Vaughan), however, is silent regarding further comprising: measuring a second plurality of movement signals at a muscle group of a foot, a shank, or a thigh of the user, the second plurality of movement signals representative of a phase in a gait cycle; creating a baseline gait profile using historical movement signals measured at the muscle group; and determining a gait report score based on a comparison of the second plurality of movement signals to the baseline gait profile, wherein the machine-learned model is configured to identify the onset of the symptom of the physical condition further based on the gait report score.
Brokaw teaches measuring a second plurality of movement signals at a muscle group of a foot, a shank, or a thigh of the user, the second plurality of movement signals representative of a phase in a gait cycle (Col 49, line 57 – Col 50, line 3); creating a baseline gait profile using historical movement signals measured at the muscle group; and determining a gait report score based on a comparison of the second plurality of movement signals to the baseline gait profile, wherein the machine-learned model is configured to identify the onset of the symptom of the physical condition further based on the gait report score (Col 59, lines 56-67; Col 38, line 26- Col 39, line 27).
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 method disclosed by Vaughan to include measuring a second plurality of movement signals at a muscle group of a foot, a shank, or a thigh of the user, the second plurality of movement signals representative of a phase in a gait cycle; creating a baseline gait profile using historical movement signals measured at the muscle group; and determining a gait report score based on a comparison of the second plurality of movement signals to the baseline gait profile, wherein the machine-learned model is configured to identify the onset of the symptom of the physical condition further based on the gait report score as taught by Brokaw in order to recognize in that movement data impaired gait, balance, posture and movement patterns and to trigger cues to correct, prevent or otherwise address the behavior based on the recognized impairment (Col 34, lines 58-63).
Regarding claim 12, the modified invention of Vaughan, Powers, and Ghaffari discloses the plurality of movement signals is a first plurality of movement signals (Para 0063, Para 0150 -Vaughan), however, is silent regarding further comprising: monitoring a second plurality of movement signals representative of a symptom-affected movement of the user; and comparing the second plurality of movement signals to a symptom profile, wherein the symptom profile is created using historical movement data representative of movement while a given user is experiencing the symptom without assistance from chemical stimulus, wherein determining the modification to the dose or the time associated with the upcoming administration of the chemical stimulus is based on the comparison of the second plurality of movement signals to the symptom profile.
Brokaw teaches monitoring a second plurality of movement signals representative of a symptom-affected movement of the user; and comparing the second plurality of movement signals to a symptom profile, wherein the symptom profile is created using historical movement data representative of movement while a given user is experiencing the symptom without assistance from chemical stimulus, wherein determining the modification to the dose or the time associated with the upcoming administration of the chemical stimulus is based on the comparison of the second plurality of movement signals to the symptom profile (Col 38, line 26- Col 39, line 27).
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 method disclosed by Vaughan to include monitoring a second plurality of movement signals representative of a symptom-affected movement of the user; and comparing the second plurality of movement signals to a symptom profile, wherein the symptom profile is created using historical movement data representative of movement while a given user is experiencing the symptom without assistance from chemical stimulus, wherein determining the modification to the dose or the time associated with the upcoming administration of the chemical stimulus is based on the comparison of the second plurality of movement signals to the symptom profile as taught by Brokaw in order to recognize in that movement data impaired gait, balance, posture and movement patterns and to trigger cues to correct, prevent or otherwise address the behavior based on the recognized impairment (Col 34, lines 58-63).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Vaughan (US 2019/0043610) in view of Powers (US 2019/0365286) and further in view of Ghaffari (US 2017/095670) and further in view of Burnes (US 2019/0069815) .
Regarding claim 18, the modified invention of Vaughan, Powers, and Ghaffari s discloses all of the elements of the invention as discussed above, however, is silent regarding the time to administer the chemical stimulus is an administration time, further comprising: identify a stimulus metabolism period indicating a time period between the intake of the chemical stimulus and a peak efficacy of the chemical stimulus, wherein determining the modification to the administration time comprises: determining a time at which an "off' time duration of the chemical stimulus will begin; and updating the administration time to be earlier, by the stimulus metabolism period, than the time at which an "off' time duration of the chemical stimulus will begin.
Burnes teaches the time to administer the chemical stimulus is an administration time, further comprising: identify a stimulus metabolism period indicating a time period between the intake of the chemical stimulus and a peak efficacy of the chemical stimulus (Para 0061), wherein determining the modification to the administration time comprises: determining a time at which an "off' time duration of the chemical stimulus will begin; and updating the administration time to be earlier, by the stimulus metabolism period, than the time at which an "off' time duration of the chemical stimulus will begin (Para 0045).
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 method disclosed by Vaughan to include identifying a stimulus metabolism period indicating a time period between the intake of the chemical stimulus and a peak efficacy of the chemical stimulus, wherein determining the modification to the administration time comprises: determining a time at which an "off' time duration of the chemical stimulus will begin; and updating the administration time to be earlier, by the stimulus metabolism period, than the time at which an "off' time duration of the chemical stimulus will begin as taught by Burnes in order to help enable adequate dosing of a medication for a particular patient (Para 0034, Para 0045).
Response to Arguments
Applicant’s arguments filed 02/06/2026, on pages 13-14, regarding Vaughn, Powers, and Ghaffari failing to teach measurements captured by sensors…”during a common measurement interval” have ben fully considered but are not persuasive. As detailed in the rejection above, Powers teaches a time-aligned feature vector of the plurality of movement signals (Para 0044) and Ghaffari teaches a time-aligned vector comprising multiple data streams (“Each sensing device 110 can include one or more sensors such as accelerometers, gyroscopes, temperature sensors, galvanic skin responses sensors, chemical sensors, light sensors (e.g., visible or invisible light), sound sensors, bio-potential electrodes (e.g., ExG, such as ECG, EMG, EEG), and other sensors”) that are process in real time, i.e. during a common measurement interval (Para 0030, 0059). Thus, the current prior art teaches the amended limitations.
Conclusion
Applicant's amendment necessitated the new ground(s) 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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/ANTARIUS S DANIEL/Examiner, Art Unit 3783
/KEVIN C SIRMONS/Supervisory Patent Examiner, Art Unit 3783
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