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 .
Pursuant to communications filed on 06/04/2024, this is a First Action Non-Final Rejection on the Merits wherein claims 1-20 are currently pending in the instant application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/04/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
Double Patenting
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 18/733,687 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because at least independent claims 1, 13 and 20 have been amended and/or have been re-arranged in wording, but Examiner believes they are directed to the same scope of invention, as such, they are not patentably distinct from each other because the claimed device, method and non-transitory CRM with the associated components/steps recited are obvious over the device, method and non-transitory CRM recited in the co-pending claims 1, 14 and 20 of the co-pending application No. 18/733,687.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Godlasky (US 20230414430 A1) – “Godlasky”.
1, 13 and 20, Godlasky discloses an electronic device / the associated method and the associated non-transitory CRM (e.g., via an improved system, method and apparatus for rehabilitation therapy….and can be provided to the patient based on real-time input from the patient. See for example Fig. 1A where a patient is using the table with a frame and therapy device), comprising:
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circuitry (see [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:
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receive robot capability parameters (e.g. interpreted as for example degrees of freedom) associated with a human-machine interaction (HMI) device (see fig. 3; see [0109] In this embodiment, actuator 300 provides a 90-degree motion right or left, but may be positioned to provide a 360-degree rotation of therapy device 101 while it's attached to device support 104.);
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receive touch parameters associated with a physical-interaction of the HMI device and a user (see [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. See [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. See [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).
(See [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 physical-interaction of the HMI device (See [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). See [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)).;
control the physical interaction of the HMI device based on the received robot capability parameters, the received touch parameters, and the received control parameters (See [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)). See [0201] FIGS. 10A-10H illustrate a view of lever device attachments between the therapy device 101 and the frame 1000 via arms 1001 which is used as an example for adding additional axis of rotations. Arms 1101 are coupled to the device 101, and are attached to the frame 1001 via an attachment mechanism 1002, which allows for 90-degrees of articulation. FIG. 10B shows the device rotated 90-degrees. The device may be rotated using manual or mechanical levers. This rotation permits multiple angles of contact with the device on the patient's body. These additional axes of rotations may also be used to utilize the re-orientation of the therapy device 101 to point to the floor, perpendicular to floor, or to the ceiling which may be used to orient the device in a laying (FIG. 10D), standing (FIG. 10E), seated (FIG. 10G), side-laying (FIG. 10F), or footrest (FIG. 10H) therapeutic positions.);
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determine a physical response of the user, based on the control of the physical interaction of the HMI device; determine safety metrics associated with the user, based on the determined physical response, the received robot capability parameters, and the received touch parameters (See [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 control metrics associated with the operator (e.g., interpreted as a therapist), based on the received robot capability parameters, and the received control parameters (see [0116] Input data from therapist sessions may be accessed through network 190 for machine learning purposes. A therapist could choose a predetermined therapy program for the patient, based on the therapist's professional recommendation, and the therapist can input reasons they chose the particular program for that individual patient. The reasons for the selected program data can be used for machine learning purposes. The therapist may also self-manually control a therapy program for the user remotely, which provides therapist access to control patient device's actuators 120, 121, and 122, as well as device support 104 and attached device 101, while receiving signals from image sensors 130, 131, and 132, and pressure sensor 301, which can be referred to as a live session.); and
control the physical interaction of the HMI device, further based on the determined safety metrics and the determined control metrics (See [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). Thus, correlation between the applied strategy and patient pain level is obtained, and the control is modified accordingly.).
Regarding claims 2 and 14, Godlasky discloses wherein the robot capability parameters associated with the HMI device includes at least one of:
a form factor parameter of the HMI device (see [0111] In an embodiment, the therapy device support 104 allows for there to be an integrated depth sensor for the therapy device 101, and or a force sensor, in order to provide feedback possibly stop the operation of the therapy system. The therapy device support 104 and may be configured to accommodate different shapes and sizes of therapy devices and their respective accessories.),
a level of autonomy of the HMI device, or
a degree of freedom of the HMI device (See fig. 3; see [0109] In this embodiment, actuator 300 provides a 90-degree motion right or left, but may be positioned to provide a 360-degree rotation of therapy device 101 while it's attached to device support 104.).
Regarding claims 3 and 15, Godlasky discloses wherein the touch parameters include at least one of:
an orientation of the HMI device (see [0204] The orientations of the additional rotational axis 1003 may serve purpose for multiple systems of use and repositioning of the system and the patient for different modalities of therapy. This would apply to a patient being able to access multiple positions of therapy: laying (FIG. standing (FIG. 10E), seated (FIG. 10F), side-laying (FIG. 10G), or footrest (FIG. 10H), with the use of a single system. The orientations of the additional rotational axis may also allow medial/lateral therapy for the multiple positions of therapy with the use of a single system.),
a speed of a part of the HMI device (see [0182] a desired speed or acceleration is programmed by the system to move the therapeutic device from one point to another while in contact with a patient, but friction from the patient's clothing slows the desired programmed speed, the proportional integral derivative control algorithms restore the movement of the therapeutic device to the desired speed with minimal delay and overshoot by increasing the power of the actuators in a controlled manner in conjunction with the visual servo method.),
a force applied by the HMI device (see [0191] In an embodiment, during the massage program, the patient may have the option to input a higher or lower pressure setting. For example, to maintain a constant of 10 lbs of tactile force instead of 5 lbs throughout the massage program which the patient may input using the graphic interface 108 and/or remote controller 600. The feedback loop can provide data that compares the original Z path altered to the an current Z path which allows for the 101bs of constant sensed pressure.a time duration for an operation of the HMI device), or
a contact zone between the HMI device and the user (see [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.).
Regarding claims 4 and 16, Godlasky discloses wherein the control parameters associated with the operator include at least one of:
operator-control freedom parameters associated with the HMI device (see [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.),
a touch-based feedback from the patient (see [0130] During execution of the massage therapy session to the patient, the patient can provide feedback to the algorithm, 428. The patient feedback can include, or be based at least in part on, image signals 430, which may be obtained from image sensors.), or
a user interface control associated with the operator (see [0018] In another embodiment, the graphical user interface is configured to provide user input control signals to the processor; control motion of the Z-axis support member in the Z-axis; control motion of the X-axis support member in the X-axis and the Y-axis; and control the operation of a therapy device coupled to the mounting surface of the Z-axis support member.).
Regarding claims 5 and 17, Godlasky discloses wherein the determined safety metrics associated with the user includes at least one of:
a trust level of the user associated with the HMI device,
a comfort level of the user associated with the HMI device, or
a safety level of the user associated with the HMI device (See [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).
Regarding claims 6 and 18, Godlasky discloses wherein the physical response of the user includes at least one of:
a body movement of the user (see [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.),
a gaze of the user,
a verbal response of the user or
a facial response of the user.
Regarding claims 7 and 19, Godlasky discloses wherein the control metrics include at least one of:
a physical load on the operator,
a mental load on the operator,
a level of control of operator (see [0116] Input data from therapist sessions may be accessed through network 190 for machine learning purposes. A therapist could choose a predetermined therapy program for the patient, based on the therapist's professional recommendation, and the therapist can input reasons they chose the particular program for that individual patient. The reasons for the selected program data can be used for machine learning purposes. The therapist may also self-manually control a therapy program for the user remotely, which provides therapist access to control patient device's actuators 120, 121, and 122, as well as device support 104 and attached device 101, while receiving signals from image sensors 130, 131, and 132, and pressure sensor 301, which can be referred to as a live session. Similarly, the therapist can input reasons for their choosing of their self-controlled paths of their manual therapy program. The reasons input by the therapist, and the entire therapist-run movement of actuators 120, 121, and 122, and control of device support 104 and attached device 101, and input signals from image sensors 130, 131, and 132, and pressure sensor 301 during a live session, can be stored in memory and accessed by network and used for AI data analysis and machine learning.),
a level of awareness of the operator, or
a task performance by the HMI device (see [0214] 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. The evaluation of the strategy will be used for machine learning purposes, in order to improve strategies for diagnostic therapeutic programming over time. 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. See [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.)).
Regarding claim 8, Godlasky discloses wherein the circuitry is further configured to:
receive user-background parameters associated with the user, wherein the received user-background parameters include at least one of:
a physical condition of the user, an age of the user, a gender of the user, or an ethnicity of the user (see [0150] In an embodiment, a patient enters their input data of height, weight, age, sex, body fat type, lean body mass type, and ethnicity using the GUI 108 or smartphone application and the input data will be provided to processor 150. The input data will closely match the patient to the category of predefined model that most closely matches the patient input data, and the patient will be assigned that predefined 3D model. An algorithm, based on human statistical averages, will “skew”, or “stretch” or “compress” the predefined model 3D cloud point model to more closely match the exact patient input data. This algorithm will create a “new predefined model” which is a more tailored 3D cloud point model specific to the patient's input data of height, weight, age, sex, body fat type, lean body mass type, and ethnicity.).
Regarding claim 9, Godlasky discloses wherein the received user-background parameters are correlated with a trust level of the user associated with the HMI device (see [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.).
Regarding claim 10, Godlasky discloses wherein the circuitry is further configured to: receive operator-background parameters associated with the operator, wherein the received operator-background parameters include at least one of:
an identity of the operator, a type of operation of the operator (see [0206] Another embodiment of this disclosure is a remote control of the holster. In this embodiment, a remote therapist, or other remote operator may control operation of the instrument remotely. Thus, the remote operator can control the location, pressure and movement of the arms, as described herein as well as operation of the instrument, via the holster, from a remote location.), a gender of the operator, and a relationship between a patient and the operator.
Regarding claim 11, Godlasky discloses wherein the received operator-background parameters are correlated with a trust level of the user associated with the HMI device (see [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.).
Regarding claim 12, Godlasky discloses wherein the circuitry is further configured to: determine first communication metrics associated with the user and the HMI device; and determine second communication metrics associated with the user and the operator, wherein a trust level of the user of the HMI device is correlated with the determined first communication metrics and the determined second communication metrics (see [0121-0123] disclosing the live communications between patient and therapist).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See attached form PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jaime Figueroa whose telephone number is (571)270-7620. The examiner can normally be reached on Monday-Friday 9-5.
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, Wade Miles can be reached on 571-270-7777. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAIME FIGUEROA/Primary Patent Examiner, Art Unit 3656