DETAILED ACTION
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 Arguments
Applicant’s arguments, see page 8, filed 3/10/2026, with respect to rejections under 112(b) have been fully considered and are persuasive. The 112(b) rejections have been withdrawn.
Applicant's arguments regarding rejections under 35 U.S.C. 103 have been fully considered but they are not persuasive. While Godlasky may not teach the aspects of currently amended claim 1 wherein the physical response includes a gaze or involuntary motion, Lanzkowsky teaches a pain determination method in which pain is diagnosed remotely. Lanzkowsky states that this may be used to remotely diagnose and prescribe treatment, thus furthering the goals of Godlasky’s remote therapy system. Applicant states that Godlasky also fails to teach safety metrics including trust level, comfort level, or safety level, but the proposed combination with Lanzkowsky results in these limitations. Additionally, Applicant contends that Godlasky fails to determine correlation information based on the control of the HMI device and the determined safety metrics, and transmit another set of instructions to control the HMI device for another physical interaction. However, as stated in the previous office action, Godlasky teaches the limitations regarding the correlation in at least [0214]. Further, Godlasky indicates in at least [0123] and [0366] that the system saves all recorded data to inform the next session (the next interaction). The rejections have been augmented to reflect the above points.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-3, 5, 8-10, 12, 14-16, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Godlasky (US 20230414430 A1) in view of Lanzkowsky (US-20190313966-A1).
Claim 1
Godlasky teaches
An electronic device, comprising: circuitry
(Godlasky - [0136] FIG. 5 a process 500 to implement an embodiment of the disclosure. This process 500 may be executed by one or more processors, servers, controllers or another suitable device or computer. )
configured to:
receive touch parameters associated with physical-interactions of a Human-Machine Interaction (HMI) device with a user;
(Godlasky - [0117] A therapist may input data for the patient, such as the patient's current location of pain and a perceived level of pain in each location, patient's current or previous injury, patient's exercise or activity schedule, and a postural analysis or structural analysis of the patient, be noted in conjunction with their self-manually run live session program, to be stored in memory and give appropriate context for AI data analysis and machine learning.
[0168] In an embodiment, during a portion of a massage therapy program, the therapeutic device will perform multiple paths back and forth while in contact with an individual muscle from its approximate proximal attachment location to its approximate distal attachment location on the patient's body, and may include a multitude of different therapeutic techniques, such as oscillations, for example. One feature of the total time of therapy on an individual muscle may include a focus on specific locations along the muscle that commonly correlate to fascial constrictions sometimes referred to as “trigger points” or “muscle knots”. Common locations of “trigger points” have been researched and documented as certain specific locations along a muscle's orientation and these specific locations will also be defined or predefined as a part of the predefined 3D model cloud points and be pre-programmed into massage therapy programs, as the common trigger point locations relate to the cloud point location in Cartesian coordinate space.
[0197] … For example, a patient is receiving trigger point therapy on their hamstring muscle, the system directs the patient to slowly bend and straighten their knee while the device is in contact with the specific trigger point location. The pressure sensor 301 may provide input data feedback in order to maintain a certain amount of pressure while in contact with the patient's trigger point location while the patient is moving the joint associated with the specific trigger point location.)
EXAMINER NOTE: Touch locations, as well as force/pressure of the robot are touch parameters which are considered by the system.
(Godlasky - [0198] In an embodiment, characteristics of a “trigger point” may include a hardness in the muscle. … Specific to the use of a percussion massage gun as the therapeutic device used in contact with a hardness of a trigger point, the percussion massage gun may experience a rebounding or a recoil effect. This may occur when the percussion massage gun comes in contact with a hard surface including a trigger point or bone landmark. … This distinct bouncing or recoil effect will also give a distinct pressure sensor feedback profile, which may be input data from 301, which will result in an adjustment by the system to decrease the pressure of the device in contact with the patient by moving the Z-axis in order to minimize the recoil effect. …)
EXAMINER NOTE: The "softness" of the device is another touch parameter considered by the system.
receive control parameters associated with an operator of the HMI device to control the HMI device
(Godlasky - [0122] The therapist can communicate in real-time with the patient to confirm that the patient data inputs are correct. The therapist can input any necessary changes, including changes to patient input data, as well as adding new pain locations, including new “trigger point” locations in real-time. Trigger point locations can be remembered by the system in terms of their Cartesian coordinate position in space relative to the patient's position during the therapy session. Trigger points and their locations can also be remembered by the system for AI diagnostic therapeutic programming purposes. The therapist can then use their professional judgment to self-manually control or run the ‘live’ therapy session for the user they deem to be most beneficial. The therapist may have designated controls on their therapist device which includes control of the patient's device's X-axis motion, Y-axis motion, Z-axis motion (pressure exerted), through control of actuators 120, 121 and 122 and movement of support members 102c, 102b, and 102d and control of device support 104 including speed of amplitude of the percussion massage device 101 (if percussion massage device is the used therapeutic device).
[0175] In an embodiment, during a portion of a massage therapy program, the therapeutic device will perform multiple paths back and forth while in contact with an individual muscle from its approximate proximal attachment location to its approximate distal attachment location on the patient's body… In this embodiment, the GUI 108 may display a pre-recorded video and audio of a human therapist that is demonstrating the device in contact with the same muscle that the patient is receiving therapy on… The therapist will specifically give the cue for the patient when the device location is on the predefined location of a trigger point for that specific muscle. The patient input of pain with a trigger point will be followed by their grading of the pain on a scale of 1 through 5, for example. The pain associated with the trigger point and the grade, which is input by the patient, is an important parameter which the system will use to contextually map the patient and which the system will use for priority in diagnostic therapeutic programming.)
EXAMINER NOTE: The therapist corresponds to the operator. The therapist controls X, Y, and Z axes (degrees of freedom), as well as communicates with patient to demonstrate contact locations of the robot during operation (transparency).
transmit a set of instructions, based on the received touch parameters and the received control parameters, to the HMI device to control at least one actuator of the HMI device for a physical interaction to the user;
(Godlasky - [0189] In an embodiment, the one or more pressure sensors 301 will also provide feedback loop. This allows running of a therapy program with a predetermined baseline constant pressure of 5 lbs of contractile force, for example. The original predetermined massage program path would be designed for the constant baseline of 5 lbs of contractile force, as an example, to determine the vertical support member 102d Z-axis path in Cartesian coordinate space relative to the patient 113. Then, the real-time feedback loop from the pressure sensors 301 will acquire data and +/− ratio to improve and correct the Z-axis motion path needed to maintain the 5 lbs of tactile force with the patient during the running of the massage program's motions.)
EXAMINER NOTE: The program is adjusted to maintain pressure at contact locations (touch parameters) along the z-axis motion path (control parameter).
(Godlasky - [0004] The graphical user interface further transmits the control signals to the processor to instruct the processor to control the operation of the X-axis actuator, the Y-axis actuator, and the Z-axis actuator.)
EXAMINER NOTE: The interactions are carried out by actuators controlled via control signals (instructions)
determine a physical response of the user, based on the physical interaction of the HMI device to the user, …
determine safety metrics associated with the user, based on the determined physical response of the user and the received touch parameters,
(Godlasky - [0367] In an embodiment, during operation of a massage therapy session a microphone may be used for audio input from the patient to be analyzed by the system. In this embodiment, the system may have certain designated audio recordings. A pre-recorded audio may cue the patient to respond and turn on the microphone to “listen” and interpret a list of audio responses by the patient: such as “yes” or “no”, for example. Each interpreted response would have a pre-programmed pre-recorded audio response from the system, such as: “Can you tell me if this is a tender or painful spot?” The microphone may be active for the following 10 seconds to wait for response of patient being “yes” or “no”. If a patient responds “yes”, the system may respond “How would you grade your level of pain 1 out of 5?”, which would leave the microphone active for the following 10 seconds to analyze the response of a client, “Three”. The system may respond, “OK, I'll remember that this may be an area of pain and possible muscle constriction.”)
EXAMINER NOTE: Per [0037] of applicant's specification, a verbal response of the user (patient) is a type of physical response. A pain level is a level of comfort, and thus a safety metric (applicant's specification at [0058] indicates that comfort level may be a safety metric). Thus, safety metrics (pain level) is determined from a physical response of a user (verbal response of patient).
determine correlation information based on the control of the physical- interaction of the HMI device and the determined safety metrics; and
(Godlasky - [0214] In an embodiment, the strategy of the diagnostic therapeutic program will be evaluated based on improvements of the input to the system including to the analysis of the 3D scan, and the pain location and grade. In this embodiment, one or more of the image sensors, preferably 131 or 132, will provide an updated 3D scan of the patient that will be re-analyzed to show geometric improvements closer to the normal predefined model, which will be based on geometric symmetries. If improvement is not measured during an evaluation it will result in an update of the strategy. In this embodiment, the patient will update input to the GUI 108, or smartphone application, in which they will input the pain location and pain grade. If improvement is not noted on the previous input of the location and grade of pain, it will result in an update to the specific strategy involved, in order to better provide relief of pain for the patient. … In an embodiment, evaluation of a strategy would call for a measured success within a completion of the diagnosed time period or diagnosed number of massage therapy sessions of a therapy program completed by the patient in order to properly evaluate the strategy.)
EXAMINER NOTE: The results of the massage therapy (physical interaction) are evaluated to determine if pain is reduced (safety metric, comfort level). Thus, correlation between the applied strategy and patient pain level is obtained, and the control is modified accordingly.
transmit another set of instructions, based on the received touch parameters, the received control parameters, and the determined correlation information, to the HMI device to control the at least one actuator of the HMI device for another physical interaction to the user.
(Godlasky - [0123] Live therapist session data can then be stored in memory and accessed by the network 190 and used for AI analysis and machine learning, and the data can also store to memory for access by the patient at any future point to repeat the session's paths and locations on their patient device. This means that the AI will learn from the therapist run session, but the user will also have access to repeat the exact therapist run session an infinite number of times as the session data will become a part of their library of therapy programs.
[0366] In an embodiment, machine learning will use all the data acquired on an individual patient for diagnostic therapeutic programming purposes and to predict future areas of concern for therapy based on the patient's history of data.)
EXAMINER NOTE: All data is saved so that the information may be used to inform the interaction during the next session
Godlasky may not explicitly teach the following limitations in combination. However, Lanzkowsky teaches
determine a physical response of the user, based on the physical interaction of the HMI device to the user,
wherein the physical response of the user includes at least one of a gaze of the user or an involuntary motion of the user;
determine safety metrics associated with the user, based on the determined physical response of the user and the received touch parameters,
wherein the safety metrics includes at least one of a trust level of the user with the HMI device, a comfort level of the user with the HMI device, or a safety level of the user with the HMI device;
(Lanzkowsky - [0048] Accelerometer data may be collected and indicate one, two, or three dimensions of motion by sensor 206. The accelerometer data may be recorded. The accelerometer data may be analyzed and may indicate pain level states based on patient voluntary or involuntary movement in response to electromagnetic or other stimuli or without external stimuli.
EXAMINER NOTE: Accelerometer data is used to detect involuntary motion of the user in order to determine the level of pain (comfort level).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize Lanzkowsky’s pain determination method in Godlasky’s remote treatment robot in order to allow for objective analysis of pain so that proper treatment may be applied.
(Lanzkowsky - [0024] Various embodiments disclosed herein are directed toward addressing one or more of the problems discussed above, while prioritizing the patient's health, safety, choice of treatment, reduced adverse effects, and general best interests. An optimal pain treatment plan will have the added benefits of improvements in social and legal issues for the patient. The present disclosure provides a description of various methods and diagnostic systems for analyzing patient pain level state as the patient interacts with the diagnostic system.
[0025] Observing, capturing, and analyzing the affective data gathered can yield significant information about patient pain level states. Analysis of the pain level states may be provided by web services and thus allow treatment to be prescribed. With the disclosed methods and systems, health care professionals may objectively determine the pain levels that patients are experiencing. Affect data can be communicated across a distance and thus pain levels of patients in distant locations may be remotely diagnosed by health care professionals.)
Claim 14
Godlasky teaches
in an electronic device:
(Godlasky - [0136] FIG. 5 a process 500 to implement an embodiment of the disclosure. This process 500 may be executed by one or more processors, servers, controllers or another suitable device or computer. )
receiving touch parameters associated with physical-interactions of a Human-Machine Interaction (HMI) device with a user;
(Godlasky - [0117] A therapist may input data for the patient, such as the patient's current location of pain and a perceived level of pain in each location, patient's current or previous injury, patient's exercise or activity schedule, and a postural analysis or structural analysis of the patient, be noted in conjunction with their self-manually run live session program, to be stored in memory and give appropriate context for AI data analysis and machine learning.
[0168] In an embodiment, during a portion of a massage therapy program, the therapeutic device will perform multiple paths back and forth while in contact with an individual muscle from its approximate proximal attachment location to its approximate distal attachment location on the patient's body, and may include a multitude of different therapeutic techniques, such as oscillations, for example. One feature of the total time of therapy on an individual muscle may include a focus on specific locations along the muscle that commonly correlate to fascial constrictions sometimes referred to as “trigger points” or “muscle knots”. Common locations of “trigger points” have been researched and documented as certain specific locations along a muscle's orientation and these specific locations will also be defined or predefined as a part of the predefined 3D model cloud points and be pre-programmed into massage therapy programs, as the common trigger point locations relate to the cloud point location in Cartesian coordinate space.
[0197] … For example, a patient is receiving trigger point therapy on their hamstring muscle, the system directs the patient to slowly bend and straighten their knee while the device is in contact with the specific trigger point location. The pressure sensor 301 may provide input data feedback in order to maintain a certain amount of pressure while in contact with the patient's trigger point location while the patient is moving the joint associated with the specific trigger point location.)
EXAMINER NOTE: Touch locations, as well as force/pressure of the robot are touch parameters which are considered by the system.
(Godlasky - [0198] In an embodiment, characteristics of a “trigger point” may include a hardness in the muscle. … Specific to the use of a percussion massage gun as the therapeutic device used in contact with a hardness of a trigger point, the percussion massage gun may experience a rebounding or a recoil effect. This may occur when the percussion massage gun comes in contact with a hard surface including a trigger point or bone landmark. … This distinct bouncing or recoil effect will also give a distinct pressure sensor feedback profile, which may be input data from 301, which will result in an adjustment by the system to decrease the pressure of the device in contact with the patient by moving the Z-axis in order to minimize the recoil effect. …)
EXAMINER NOTE: The "softness" of the device is another touch parameter considered by the system.
receiving control parameters associated with an operator of the HMI device to control the HMI device;
(Godlasky - [0122] The therapist can communicate in real-time with the patient to confirm that the patient data inputs are correct. The therapist can input any necessary changes, including changes to patient input data, as well as adding new pain locations, including new “trigger point” locations in real-time. Trigger point locations can be remembered by the system in terms of their Cartesian coordinate position in space relative to the patient's position during the therapy session. Trigger points and their locations can also be remembered by the system for AI diagnostic therapeutic programming purposes. The therapist can then use their professional judgment to self-manually control or run the ‘live’ therapy session for the user they deem to be most beneficial. The therapist may have designated controls on their therapist device which includes control of the patient's device's X-axis motion, Y-axis motion, Z-axis motion (pressure exerted), through control of actuators 120, 121 and 122 and movement of support members 102c, 102b, and 102d and control of device support 104 including speed of amplitude of the percussion massage device 101 (if percussion massage device is the used therapeutic device).
[0175] In an embodiment, during a portion of a massage therapy program, the therapeutic device will perform multiple paths back and forth while in contact with an individual muscle from its approximate proximal attachment location to its approximate distal attachment location on the patient's body… In this embodiment, the GUI 108 may display a pre-recorded video and audio of a human therapist that is demonstrating the device in contact with the same muscle that the patient is receiving therapy on… The therapist will specifically give the cue for the patient when the device location is on the predefined location of a trigger point for that specific muscle. The patient input of pain with a trigger point will be followed by their grading of the pain on a scale of 1 through 5, for example. The pain associated with the trigger point and the grade, which is input by the patient, is an important parameter which the system will use to contextually map the patient and which the system will use for priority in diagnostic therapeutic programming.)
EXAMINER NOTE: The therapist corresponds to the operator. The therapist controls X, Y, and Z axes (degrees of freedom), as well as communicates with patient to demonstrate contact locations of the robot during operation (transparency).
transmitting a set of instructions, based on the received touch parameters and the received control parameters, to the HMI device to control at least one actuator of the HMI device for a physical interaction to the user;
(Godlasky - [0189] In an embodiment, the one or more pressure sensors 301 will also provide feedback loop. This allows running of a therapy program with a predetermined baseline constant pressure of 5 lbs of contractile force, for example. The original predetermined massage program path would be designed for the constant baseline of 5 lbs of contractile force, as an example, to determine the vertical support member 102d Z-axis path in Cartesian coordinate space relative to the patient 113. Then, the real-time feedback loop from the pressure sensors 301 will acquire data and +/− ratio to improve and correct the Z-axis motion path needed to maintain the 5 lbs of tactile force with the patient during the running of the massage program's motions.)
EXAMINER NOTE: The program is adjusted to maintain pressure at contact locations (touch parameters) along the z-axis motion path (control parameter).
(Godlasky - [0004] The graphical user interface further transmits the control signals to the processor to instruct the processor to control the operation of the X-axis actuator, the Y-axis actuator, and the Z-axis actuator.)
EXAMINER NOTE: The interactions are carried out by actuators controlled via control signals (instructions)
determining a physical response of the user, based on the physical interaction of the HMI device to the user,
determining safety metrics associated with the user, based on the determined physical response of the user and the received touch parameters,
(Godlasky - [0367] In an embodiment, during operation of a massage therapy session a microphone may be used for audio input from the patient to be analyzed by the system. In this embodiment, the system may have certain designated audio recordings. A pre-recorded audio may cue the patient to respond and turn on the microphone to “listen” and interpret a list of audio responses by the patient: such as “yes” or “no”, for example. Each interpreted response would have a pre-programmed pre-recorded audio response from the system, such as: “Can you tell me if this is a tender or painful spot?” The microphone may be active for the following 10 seconds to wait for response of patient being “yes” or “no”. If a patient responds “yes”, the system may respond “How would you grade your level of pain 1 out of 5?”, which would leave the microphone active for the following 10 seconds to analyze the response of a client, “Three”. The system may respond, “OK, I'll remember that this may be an area of pain and possible muscle constriction.”)
EXAMINER NOTE: Per [0037] of applicant's specification, a verbal response of the user (patient) is a type of physical response. A pain level is a level of comfort, and thus a safety metric (applicant's specification at [0058] indicates that comfort level may be a safety metric). Thus, safety metrics (pain level) is determined from a physical response of a user (verbal response of patient).
determining correlation information based on the control of the physical- interaction of the HMI device and the determined safety metrics; and
(Godlasky - [0214] In an embodiment, the strategy of the diagnostic therapeutic program will be evaluated based on improvements of the input to the system including to the analysis of the 3D scan, and the pain location and grade. In this embodiment, one or more of the image sensors, preferably 131 or 132, will provide an updated 3D scan of the patient that will be re-analyzed to show geometric improvements closer to the normal predefined model, which will be based on geometric symmetries. If improvement is not measured during an evaluation it will result in an update of the strategy. In this embodiment, the patient will update input to the GUI 108, or smartphone application, in which they will input the pain location and pain grade. If improvement is not noted on the previous input of the location and grade of pain, it will result in an update to the specific strategy involved, in order to better provide relief of pain for the patient. … In an embodiment, evaluation of a strategy would call for a measured success within a completion of the diagnosed time period or diagnosed number of massage therapy sessions of a therapy program completed by the patient in order to properly evaluate the strategy.)
EXAMINER NOTE: The results of the massage therapy (physical interaction) are evaluated to determine if pain is reduced (safety metric, comfort level). Thus, correlation between the applied strategy and patient pain level is obtained, and the control is modified accordingly.
transmitting another set of instructions, based on the received touch parameters, the received control parameters, and the determined correlation information, to the HMI device to control the at least one actuator of the HMI device for another physical interaction to the user.
(Godlasky - [0123] Live therapist session data can then be stored in memory and accessed by the network 190 and used for AI analysis and machine learning, and the data can also store to memory for access by the patient at any future point to repeat the session's paths and locations on their patient device. This means that the AI will learn from the therapist run session, but the user will also have access to repeat the exact therapist run session an infinite number of times as the session data will become a part of their library of therapy programs.
[0366] In an embodiment, machine learning will use all the data acquired on an individual patient for diagnostic therapeutic programming purposes and to predict future areas of concern for therapy based on the patient's history of data.)
EXAMINER NOTE: All data is saved so that the information may be used to inform the interaction during the next session
Godlasky may not explicitly teach the following limitations in combinatoin. However, Lanzkowsky teaches
determining a physical response of the user…
wherein the physical response of the user includes at least one of a gaze of the user or an involuntary motion of the user;
determining safety metrics associated with the user, based on the determined physical response of the user …
wherein the safety metrics includes at least one of a trust level of the user with the HMI device, a comfort level of the user with the HMI device, or a safety level of the user with the HMI device;
(Lanzkowsky - [0048] Accelerometer data may be collected and indicate one, two, or three dimensions of motion by sensor 206. The accelerometer data may be recorded. The accelerometer data may be analyzed and may indicate pain level states based on patient voluntary or involuntary movement in response to electromagnetic or other stimuli or without external stimuli.)
EXAMINER NOTE: Accelerometer data is used to detect involuntary motion of the user in order to determine the level of pain (comfort level).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize Lanzkowsky’s pain determination method in Godlasky’s remote treatment robot in order to allow for objective analysis of pain so that proper treatment may be applied.
(Lanzkowsky - [0024] Various embodiments disclosed herein are directed toward addressing one or more of the problems discussed above, while prioritizing the patient's health, safety, choice of treatment, reduced adverse effects, and general best interests. An optimal pain treatment plan will have the added benefits of improvements in social and legal issues for the patient. The present disclosure provides a description of various methods and diagnostic systems for analyzing patient pain level state as the patient interacts with the diagnostic system.
[0025] Observing, capturing, and analyzing the affective data gathered can yield significant information about patient pain level states. Analysis of the pain level states may be provided by web services and thus allow treatment to be prescribed. With the disclosed methods and systems, health care professionals may objectively determine the pain levels that patients are experiencing. Affect data can be communicated across a distance and thus pain levels of patients in distant locations may be remotely diagnosed by health care professionals.)
Claims 2 and 15
The combination of Godlasky and Lanzkowsky teaches the limitations of claims 1 and 14 as outlined above. Godlasky further teaches
wherein the touch parameters include at least one of:
a contact position of the HMI device on a body portion of the user,
a force of the HMI device;
a softness of the HMI device;
(Godlasky - [0117] A therapist may input data for the patient, such as the patient's current location of pain and a perceived level of pain in each location, patient's current or previous injury, patient's exercise or activity schedule, and a postural analysis or structural analysis of the patient, be noted in conjunction with their self-manually run live session program, to be stored in memory and give appropriate context for AI data analysis and machine learning.
[0168] In an embodiment, during a portion of a massage therapy program, the therapeutic device will perform multiple paths back and forth while in contact with an individual muscle from its approximate proximal attachment location to its approximate distal attachment location on the patient's body, and may include a multitude of different therapeutic techniques, such as oscillations, for example. One feature of the total time of therapy on an individual muscle may include a focus on specific locations along the muscle that commonly correlate to fascial constrictions sometimes referred to as “trigger points” or “muscle knots”. Common locations of “trigger points” have been researched and documented as certain specific locations along a muscle's orientation and these specific locations will also be defined or predefined as a part of the predefined 3D model cloud points and be pre-programmed into massage therapy programs, as the common trigger point locations relate to the cloud point location in Cartesian coordinate space.
[0197] … For example, a patient is receiving trigger point therapy on their hamstring muscle, the system directs the patient to slowly bend and straighten their knee while the device is in contact with the specific trigger point location. The pressure sensor 301 may provide input data feedback in order to maintain a certain amount of pressure while in contact with the patient's trigger point location while the patient is moving the joint associated with the specific trigger point location.)
EXAMINER NOTE: Touch locations, as well as force/pressure of the robot are touch parameters which are considered by the system.
(Godlasky - [0198] In an embodiment, characteristics of a “trigger point” may include a hardness in the muscle. … Specific to the use of a percussion massage gun as the therapeutic device used in contact with a hardness of a trigger point, the percussion massage gun may experience a rebounding or a recoil effect. This may occur when the percussion massage gun comes in contact with a hard surface including a trigger point or bone landmark. … This distinct bouncing or recoil effect will also give a distinct pressure sensor feedback profile, which may be input data from 301, which will result in an adjustment by the system to decrease the pressure of the device in contact with the patient by moving the Z-axis in order to minimize the recoil effect. …)
EXAMINER NOTE: The "softness" of the device is another touch parameter considered by the system.
Claims 3 and 16
Godlasky and Lanzkowsky teaches the limitations of claim 2 as outlined above. Godlasky further teaches
wherein the at least one of the safety metrics or a comfort metrics is negatively correlated with at least one of:
the speed of the HMI device,
the force of the HMI device,
or a level of safety associated with the contact position of the HMI device.
(Godlasky - [0367] … A pre-recorded audio may cue the patient to respond and turn on the microphone to “listen” and interpret a list of audio responses by the patient: such as “yes” or “no”, for example. Each interpreted response would have a pre-programmed pre-recorded audio response from the system, such as: “Can you tell me if this is a tender or painful spot?” The microphone may be active for the following 10 seconds to wait for response of patient being “yes” or “no”. If a patient responds “yes”, the system may respond “How would you grade your level of pain 1 out of 5?”, which would leave the microphone active for the following 10 seconds to analyze the response of a client, “Three”. The system may respond, “OK, I'll remember that this may be an area of pain and possible muscle constriction.”)
EXAMINER NOTE: Because pain is negatively correlated with safety, safety is negatively correlated with pain at a given contact location (less pain means higher comfort/safety). The system remembers pain levels at various locations and adjusts its behavior accordingly. While not cited here, Examiner also points to the example discussed in [0198] of Godlasky wherein a percussive force exerted by the robot is decreased when passing over harder areas (such as bone), as this may be counterproductive to patient therapy (unsafe).
Claims 5 and 18
Godlasky and Lanzkowsky teaches the limitations of claims 1 and 14 as outlined above. Godlasky further teaches
wherein the control parameters include at least one of:
a user-control freedom parameter of the HMI device for the operator,
(Godlasky - [0143] The user controller 600 may contain operational controls for the therapy system 100. For example, it may contain “OK/STOP” button 601 to allow the user to make a selection on the GUI 108, or stop the operation of the therapy system 100 any time. … )
EXAMINER NOTE: The user can terminate the session at any time, which limits all degrees of freedom of the device
Claim 8
Godlasky and Lanzkowsky teaches the limitations of claim 1 as outlined above. Godlasky further teaches
wherein the circuitry is further configured to:
receive background parameters of the user,
wherein the background parameters include at least one of:
a gender of the user, an age of the user, a purpose of touch associated with the HMI device, and a physical state of the user.
(Godlasky - [0256] In an embodiment, previous joint injury locations will be a parameter for the patient to input to GUI 108 for diagnostic therapeutic programming. Previous injuries can often be nagging and a constant source of tension for an individual patient. Identifying the previous injury location gives the AI further context to develop a program specific to the individual. The therapeutic plan will consist of therapy on the muscles directly attached, originated or inserted around the associated joint, and include portions of the muscles' associated fascial lines relating to the portions directly above and below the joint location.)
EXAMINER NOTE: Injury may be a physical state of the user
(Godlasky - [0368] … And related to vitals, these applications can measure sleep quality, stress levels, and exercise recovery. Communication with these applications and data acquired by our system, may be through a network connection or a downloadable application on user smart device.)
EXAMINER NOTE: Stress levels and exercise recovery may be a physical state
Claim 9
Godlasky and Lanzkowsky teaches the limitations of claim 8 as outlined above. Godlasky further teaches
wherein the background parameters are correlated with at least one of the safety metrics or a comfort metrics.
(Godlasky - [0370] In this embodiment, the effectiveness of a therapeutic program may have a positive correlation with increased sleep quality, decreased stress levels, increased exercise recovery, increased exercise performance, and increased activity level.)
EXAMINER NOTE: Stress may also be a measure of comfort (or discomfort), which would indicate a correlation between background information and comfort metrics.
Claim 10
Godlasky and Lanzkowsky teaches the limitations of claim 8 as outlined above. Godlasky further teaches
wherein the circuitry is further configured to
determine a task performance metrics associated with the HMI device,
based on the received touch parameters and the received background parameters.
(Godlasky - [0372] In this embodiment, measurable data may be used as a diagnostic tool for the system. For instance, the measurable data shows what is determined as a high level of activity on that day. The system may, in turn, suggest that a longer therapy session may be necessary that day to more effectively recover from the higher intensity activity day. The data may show that the specific activity type was higher intensity for the lower body, this would yield a suggestion of the specific lower body muscles that were more intensely active on that day. The data may show high stress levels. The system may suggest more time of a session, and certain muscles that are often tightened in relation to stress, like the upper trapezius, for example. Or the measurable data may show decreased exercise recovery, the system may suggest that more time devoted to a therapy session may be of more benefit to the individual than a difficult exercise session, for example.
[0373] In this embodiment, the acquired data may be used as a personal health reminder in these cases. Such as, in this hypothetical, we have the correlative data that suggests use of the system, or use of certain specific programs of the system, improves specific measurements of increased sleep quality, decreased stress levels, increased exercise recovery, increased exercise performance, and increased activity level.
[0374] In this embodiment, the correlations of the system's use and improvements of measurable data should be positive on average. For example, if the measurables show that the data is not improving, then that specific individual may benefit from a different strategy of diagnostic therapeutic programming such as more time during a therapy session, or perhaps more time on certain locations relating to the individual's specific activity or exercise type.)
Claim 12
Godlasky and Lanzkowsky teaches the limitations of claim 10 as outlined above. Godlasky further teaches
wherein the determined task performance metrics is positively correlated with the touch parameters.
(Godlasky - [0374] In this embodiment, the correlations of the system's use and improvements of measurable data should be positive on average. For example, if the measurables show that the data is not improving, then that specific individual may benefit from a different strategy of diagnostic therapeutic programming such as more time during a therapy session, or perhaps more time on certain locations relating to the individual's specific activity or exercise type.)
EXAMINER NOTE: See also rejection of claim 10 above. The background information is used to determine touch parameters such as contact location. Continued use of the generated programs (including touch parameters) results in improvements in measurable data (increased task performance).
Claim(s) 4, 6, 17, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Godlasky and Lanzkowsky as applied above, and further in view of Shin (US-20210085558-A1).
Claims 4 and 17
Godlasky and Lanzkowsky teaches the limitations of claims 2 and 15 as outlined above. Godlasky may not explicitly teach the following limitations in combination. However, Shin teaches
wherein the at least one of the safety metrics or a comfort metrics is positively correlated with at least one of:
the softness of the HMI device,
(Shin - [0227] The object massage operation may mean a massage operation that is determined to be appropriate from the massage operation information of the current artificial intelligence massage apparatus 100 and the facial expression of the user. For example, if the current massage operation is proceeding at the maximum intensity and the user is sick and frowning, the object massage operation may be a massage operation with low massage intensity.)
EXAMINER NOTE: A lower intensity ("softer" intensity) is used to make the user more comfortable when discomfort is detected.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Godlasky’s robotic control with Shin’s suggestion to correlate softness with comfort in order to prevent pain to the user.
(Shin - [0004] Meanwhile, if the strength of the massage is too strong, the massage may cause pain to the user.)
Claims 6 and 19
Godlasky and Lanzkowsky teaches the limitations of claims 1 and 14 as outlined above. Godlasky may not explicitly teach the following limitations in combination. However, Shin teaches
wherein circuitry is further configured to:
determine a physiological stress of the user based on the determined safety metrics,
wherein the determined physiological stress is correlated to at least one of
and the physiological stress includes at least one of:
a gaze of the user,
a facial expression of the user,
(Shin - [0278] Referring to FIG. 18, it is assumed that, after the artificial intelligence massage apparatus 100 starts 1811 a first massage operation having a first massage intensity, the facial expression of the user is changed in the order of a neutral facial expression 1812 and a painful facial expression 1813, and the user changed the first massage operation to the third massage operation 1814 by lowering the first massage intensity to the second massage intensity through speech utterance or input to the user input unit 123. In this case, the massage operation determination model may be learned to change the first massage operation of the first massage intensity to a third massage option of the second massage intensity when the first massage operation is being performed and the facial expression of the user is changed in the order of the neutral facial expression 1812 and the painful facial expression 1813.)
EXAMINER NOTE: Pain is an indication of physiological stress.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Godlasky’s robotic control with Shin’s suggestion to alter programs based on facial expressions indicating stress in order to automatically meet the user's preferences.
(Shin - [0284] In addition, according to various embodiments of the present disclosure, even though there is no user interaction, the massage operation may be controlled to be changed to a massage operation preferred by the user based on the current massage operation and the change of the facial expression of the user.)
Claim(s) 7, 11, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Godlasky and Lanzkowsky as applied above, and further in view of Akalin (Do you feel safe with your robot? Factors influencing perceived safety in human-robot interaction based on subjective and objective measures," ).
Claim 7
Godlasky teaches the limitations of claim 1 as outlined above. Godlasky may not explicitly teach the following limitations in combination. However, Akalin teaches
wherein the safety metrics is negatively correlated with risk metrics and positively correlated with a trust factor.
(Akalin - [p.15, col 1, ln 1] … Consequently and in summary, the main results and guidelines for increased perceived safety in HRI are thus as follows:
We should focus on understanding the conditions that humans feel unsafe rather than they feel safe. The quantifiable measures occur under unsafe conditions
…
The key influencing factors of perceived safety are identified as comfort, experience/familiarity, predictability, sense of control, transparency, and trust.
These factors should be considered in HRI design decisions for safe HRI. The consequences of robot-related factors (refer to (Akalin et al., 2019a) for the factors) should not result in discomfort, lack of control, and distrust of its users. Moreover, the robot behaviors should be familiar, predictable, and transparent)
Akalin's research suggests that safe human-robot interaction is correlated with perceived safety of the user (negatively correlated with risk) and trust (positively correlated with trust). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Godlasky's device by introducing additional safety metrics accounting for risk and trust in order to promote safe human-robot interaction.
Claim 11
Godlasky teaches the limitations of claim 10 as outlined above. Godlasky may not explicitly teach the following limitations in combination. However, Akalin teaches
wherein the determined task performance metrics is positively correlated with a trust factor.
(Akalin - [p.15, col 1, ln 1] … Consequently and in summary, the main results and guidelines for increased perceived safety in HRI are thus as follows:
We should focus on understanding the conditions that humans feel unsafe rather than they feel safe. The quantifiable measures occur under unsafe conditions
…
The key influencing factors of perceived safety are identified as comfort, experience/familiarity, predictability, sense of control, transparency, and trust.
These factors should be considered in HRI design decisions for safe HRI. The consequences of robot-related factors (refer to (Akalin et al., 2019a) for the factors) should not result in discomfort, lack of control, and distrust of its users. Moreover, the robot behaviors should be familiar, predictable, and transparent)
Akalin's research suggests that safe human-robot interaction is correlated with trust of the user. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Godlasky's success evaluation by incorporating trust in order to promote safe human-robot interaction.
Claim 20
Godlasky teaches
A non-transitory computer-readable medium having stored thereon, computer-executable instructions that when executed by an electronic device, causes the electronic device to execute operations,
(Godlasky - [0136] FIG. 5 a process 500 to implement an embodiment of the disclosure. This process 500 may be executed by one or more processors, servers, controllers or another suitable device or computer.
[0079] Processor 150 is operatively coupled to actuators 122, 120, and 121. The controller, or processor 150 includes memory 152 and CPU 151. The memory 152 is any suitable electronic storage medium. This includes any suitable register, non-transitory computer-readable medium and may include a tangible program carrier having program instructions stored thereon.)
the operations comprising:
receiving touch parameters associated with physical-interactions of a robot with a user;
(Godlasky - [0117] A therapist may input data for the patient, such as the patient's current location of pain and a perceived level of pain in each location, patient's current or previous injury, patient's exercise or activity schedule, and a postural analysis or structural analysis of the patient, be noted in conjunction with their self-manually run live session program, to be stored in memory and give appropriate context for AI data analysis and machine learning.
[0168] In an embodiment, during a portion of a massage therapy program, the therapeutic device will perform multiple paths back and forth while in contact with an individual muscle from its approximate proximal attachment location to its approximate distal attachment location on the patient's body, and may include a multitude of different therapeutic techniques, such as oscillations, for example. One feature of the total time of therapy on an individual muscle may include a focus on specific locations along the muscle that commonly correlate to fascial constrictions sometimes referred to as “trigger points” or “muscle knots”. Common locations of “trigger points” have been researched and documented as certain specific locations along a muscle's orientation and these specific locations will also be defined or predefined as a part of the predefined 3D model cloud points and be pre-programmed into massage therapy programs, as the common trigger point locations relate to the cloud point location in Cartesian coordinate space.
[0197] … For example, a patient is receiving trigger point therapy on their hamstring muscle, the system directs the patient to slowly bend and straighten their knee while the device is in contact with the specific trigger point location. The pressure sensor 301 may provide input data feedback in order to maintain a certain amount of pressure while in contact with the patient's trigger point location while the patient is moving the joint associated with the specific trigger point location.)
EXAMINER NOTE: Touch locations, as well as force/pressure of the robot are touch parameters which are considered by the system.
(Godlasky - [0198] In an embodiment, characteristics of a “trigger point” may include a hardness in the muscle. … Specific to the use of a percussion massage gun as the therapeutic device used in contact with a hardness of a trigger point, the percussion massage gun may experience a rebounding or a recoil effect. This may occur when the percussion massage gun comes in contact with a hard surface including a trigger point or bone landmark. … This distinct bouncing or recoil effect will also give a distinct pressure sensor feedback profile, which may be input data from 301, which will result in an adjustment by the system to decrease the pressure of the device in contact with the patient by moving the Z-axis in order to minimize the recoil effect. …)
EXAMINER NOTE: The "softness" of the device is another touch parameter considered by the system.
receiving control parameters associated with an operator of the robot to control the robot;
(Godlasky - [0122] The therapist can communicate in real-time with the patient to confirm that the patient data inputs are correct. The therapist can input any necessary changes, including changes to patient input data, as well as adding new pain locations, including new “trigger point” locations in real-time. Trigger point locations can be remembered by the system in terms of their Cartesian coordinate position in space relative to the patient's position during the therapy session. Trigger points and their locations can also be remembered by the system for AI diagnostic therapeutic programming purposes. The therapist can then use their professional judgment to self-manually control or run the ‘live’ therapy session for the user they deem to be most beneficial. The therapist may have designated controls on their therapist device which includes control of the patient's device's X-axis motion, Y-axis motion, Z-axis motion (pressure exerted), through control of actuators 120, 121 and 122 and movement of support members 102c, 102b, and 102d and control of device support 104 including speed of amplitude of the percussion massage device 101 (if percussion massage device is the used therapeutic device).
[0175] In an embodiment, during a portion of a massage therapy program, the therapeutic device will perform multiple paths back and forth while in contact with an individual muscle from its approximate proximal attachment location to its approximate distal attachment location on the patient's body… In this embodiment, the GUI 108 may display a pre-recorded video and audio of a human therapist that is demonstrating the device in contact with the same muscle that the patient is receiving therapy on… The therapist will specifically give the cue for the patient when the device location is on the predefined location of a trigger point for that specific muscle. The patient input of pain with a trigger point will be followed by their grading of the pain on a scale of 1 through 5, for example. The pain associated with the trigger point and the grade, which is input by the patient, is an important parameter which the system will use to contextually map the patient and which the system will use for priority in diagnostic therapeutic programming.)
EXAMINER NOTE: The therapist corresponds to the operator. The therapist controls X, Y, and Z axes (degrees of freedom), as well as communicates with patient to demonstrate contact locations of the robot during operation (transparency).
transmitting a set of instructions, based on the received touch parameters and the received control parameters, to the robot to control at least one actuator of the robot for a physical interaction to the user;
(Godlasky - [0189] In an embodiment, the one or more pressure sensors 301 will also provide feedback loop. This allows running of a therapy program with a predetermined baseline constant pressure of 5 lbs of contractile force, for example. The original predetermined massage program path would be designed for the constant baseline of 5 lbs of contractile force, as an example, to determine the vertical support member 102d Z-axis path in Cartesian coordinate space relative to the patient 113. Then, the real-time feedback loop from the pressure sensors 301 will acquire data and +/− ratio to improve and correct the Z-axis motion path needed to maintain the 5 lbs of tactile force with the patient during the running of the massage program's motions.)
EXAMINER NOTE: The program is adjusted to maintain pressure at contact locations (touch parameters) along the z-axis motion path (control parameter).
(Godlasky - [0004] The graphical user interface further transmits the control signals to the processor to instruct the processor to control the operation of the X-axis actuator, the Y-axis actuator, and the Z-axis actuator.)
EXAMINER NOTE: The interactions are carried out by actuators controlled via control signals (instructions)
determining a physical response of the user, based on the physical interaction of the robot to the user;
determining safety metrics associated with the user, based on the determined physical response of the user and the received touch parameters,
(Godlasky - [0367] In an embodiment, during operation of a massage therapy session a microphone may be used for audio input from the patient to be analyzed by the system. In this embodiment, the system may have certain designated audio recordings. A pre-recorded audio may cue the patient to respond and turn on the microphone to “listen” and interpret a list of audio responses by the patient: such as “yes” or “no”, for example. Each interpreted response would have a pre-programmed pre-recorded audio response from the system, such as: “Can you tell me if this is a tender or painful spot?” The microphone may be active for the following 10 seconds to wait for response of patient being “yes” or “no”. If a patient responds “yes”, the system may respond “How would you grade your level of pain 1 out of 5?”, which would leave the microphone active for the following 10 seconds to analyze the response of a client, “Three”. The system may respond, “OK, I'll remember that this may be an area of pain and possible muscle constriction.”)
EXAMINER NOTE: Per [0037] of applicant's specification, a verbal response of the user (patient) is a type of physical response. A pain level is a level of comfort, and thus a safety metric (applicant's specification at [0058] indicates that comfort level may be a safety metric). Thus, safety metrics (pain level) is determined from a physical response of a user (verbal response of patient).
determining correlation information based on the control of the physical- interaction of the robot and the determined safety metrics; and
(Godlasky - [0214] In an embodiment, the strategy of the diagnostic therapeutic program will be evaluated based on improvements of the input to the system including to the analysis of the 3D scan, and the pain location and grade. In this embodiment, one or more of the image sensors, preferably 131 or 132, will provide an updated 3D scan of the patient that will be re-analyzed to show geometric improvements closer to the normal predefined model, which will be based on geometric symmetries. If improvement is not measured during an evaluation it will result in an update of the strategy. In this embodiment, the patient will update input to the GUI 108, or smartphone application, in which they will input the pain location and pain grade. If improvement is not noted on the previous input of the location and grade of pain, it will result in an update to the specific strategy involved, in order to better provide relief of pain for the patient. … In an embodiment, evaluation of a strategy would call for a measured success within a completion of the diagnosed time period or diagnosed number of massage therapy sessions of a therapy program completed by the patient in order to properly evaluate the strategy.)
EXAMINER NOTE: The results of the massage therapy (physical interaction) are evaluated to determine if pain is reduced (safety metric, comfort level). Thus, correlation between the applied strategy and patient pain level is obtained, and the control is modified accordingly.
transmitting another set of instructions, based on the received touch parameters, the received control parameters, and the determined correlation information, to the robot to control the at least one actuator of the robot for another physical interaction to the user.
(Godlasky - [0123] Live therapist session data can then be stored in memory and accessed by the network 190 and used for AI analysis and machine learning, and the data can also store to memory for access by the patient at any future point to repeat the session's paths and locations on their patient device. This means that the AI will learn from the therapist run session, but the user will also have access to repeat the exact therapist run session an infinite number of times as the session data will become a part of their library of therapy programs.
[0366] In an embodiment, machine learning will use all the data acquired on an individual patient for diagnostic therapeutic programming purposes and to predict future areas of concern for therapy based on the patient's history of data.)
EXAMINER NOTE: All data is saved so that the information may be used to inform the interaction during the next session
Godlasky may not explicitly teach the following limitations in combinatoin. However, Akalin teaches
wherein the safety metrics includes at least one of a trust level of the user with the robot, a comfort level of the user with the robot, or a safety level of the user with the robot;
(Akalin - [p.15, col 1, ln 1] … Consequently and in summary, the main results and guidelines for increased perceived safety in HRI are thus as follows:
We should focus on understanding the conditions that humans feel unsafe rather than they feel safe. The quantifiable measures occur under unsafe conditions
…
The key influencing factors of perceived safety are identified as comfort, experience/familiarity, predictability, sense of control, transparency, and trust.
These factors should be considered in HRI design decisions for safe HRI. The consequences of robot-related factors (refer to (Akalin et al., 2019a) for the factors) should not result in discomfort, lack of control, and distrust of its users. Moreover, the robot behaviors should be familiar, predictable, and transparent)
Akalin's research suggests that safe human-robot interaction is correlated with trust of the user. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Godlasky's success evaluation by incorporating trust in order to promote safe human-robot interaction.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over 103 Godlasky and Lanzkowsky as applied above, and further in view of Hamad ("A Concise Overview of Safety Aspects in Human-Robot Interaction").
Claim 13
Godlasky teaches the limitations of claim 1 as outlined above. Godlasky may not explicitly teach the following limitations in combination. However, Hamad teaches
wherein the control parameters associated with the operator have a positive correlation with at least one of a mental load associated with the user or a predictability of the HMI device.
(Hamad - [p.10, Additional middle-ware safety considerations] To adequately address the human diversity related to both safety and security, some customization and individualization are necessary. …
On the other hand, employing physiological measurements to perform online assessment of operators' mental states is crucial in
HRI. To progress towards interactive robotic systems that would dynamically adapt to operators' affective states, in [84] operator's recorded physiological data streams were analyzed to assess the engagement during HRI and the impact of the robot's operative
mode ( autonomous versus manual). Furthermore, a software framework that is compatible with both laboratory and consumer-grade sensors, while it includes essential tools and processing algorithms for affective state estimation, was recently proposed in [85] to support real-time integration of physiological adaptation in HRI.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Godlasky's control by incorporating Hamad’s suggestion to vary the autonomy of the device based on the user's engagement in order to allow the device to adapt to the user's current level of engagement.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES MILLER WATTS whose telephone number is (703)756-1249. The examiner can normally be reached 7:30-5:30 M-TH.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Adam Mott can be reached at 571-270-5376. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JAMES MILLER WATTS III/Examiner, Art Unit 3657
/ADAM R MOTT/Supervisory Patent Examiner, Art Unit 3657
Prosecution Insights