DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
In response to amendments, filed November 22, 2025, claims 50, 52-56, 59-60, 62-63, 65, and 67 have been amended. Claims 51, 64, and 66 have been cancelled. Claims 70-71 have been added. Claims 50, 52-63, 65, and 67-71 are pending.
Response to Arguments
Applicant’s arguments, see Remarks, filed November 22, 2025, with respect to the claim objections and 112(b) rejections have been fully considered and are persuasive. The claim objections and rejections under 35 USC 112(b) have been withdrawn.
Applicant's arguments with respect to the prior art rejections have been fully considered but they are not persuasive. Regarding applicants argument that the combination of Pathak/Kempf/Murphy fails to disclose three algorithmic models directed to three sets of features/scores reflecting presence of a motor control disorder, severity, and progression, respectively, Examiner respectfully disagrees. Examiner also respectfully disagrees that the combination fails to disclose wherein the identified first set features is not identical to the identified second set of features, and disagrees that the combination fails to disclose calculating a contribution made by each of the first, second or third set of features to each of a plurality of movement characteristics that are attributable to movement dysfunction in the subject, and the analyser sums the contribution made by each of the features to each of the plurality of movement characteristics to determine a collective contribution to each of the plurality of movement characteristics.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
The first set of features is the “relatively high-frequency vibrations of the pencil lead 3 and the relatively low-frequency oscillations of the occurring finger forces” from the pressure sensor 5/piezo-layer 8B to identify neuromotor disease (Kempf pg 5 [6] and pg 5 [11]-pg 6 [1]). Meanwhile the second and third set features incorporate relative positioning with “Position sensors 235 are relative sensors that measure the relative positions of the outputs of actuator assembly 115 relative to handle 120. In one embodiment, position sensors 235 are hall-effect sensors that monitor the position of the outputs of actuators 125 and 130 by measuring the positions of linkages 135 and 140 … for determining how much auto-leveling a user needs and thereby diagnosing the severity and progress of a given user” (Pathak [0029]). Further regarding the third set, Murphy describes, in Col 13 lines 24-27, “the health model could compare different samples of the same characteristic that were generated during different, non-overlapping periods of time in order to determine an amount of progression of a disease.” These first and second sets are not identical, as one detects pressure using a piezo-layer, while the other detects orientation using accelerometers, gyroscopes, and other position sensors.
Murphy’s descriptions of “using such a health model could include determining a mean, standard deviation, distribution shape, or other properties of one or more of the samples of characteristics and/or of the information detected during the wearer's performance of the prompted motor task(s),” per Col 13 lines 10-19, apply to the pressure sensor based first feature set and the position sensor based second and third feature sets, for applying “such determined properties, or the sensor signals themselves, to a linear regression model, a nonlinear regression model, a neural network, a principal components model, or some other model or algorithm to generate a disease severity score or other health state information.” In doing so, the respective algorithms calculate a contribution made by each of three sets of features to each of a plurality of movement characteristics and sum the contribution made by each of the features to each of the plurality of movement characteristics to determine a collective contribution to each of the plurality of movement characteristics, reflected in the displayed scores and health state information related to the presence, progression, severity, and other properties of a disease state or process (Murphy Col 15 lines 5-10 and Col 22 lines 57-59).
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
Movement detection device in claims 50, 55, 57-58, 60-63, and 68 – [0073] “Movement detection device 200 ideally simulates or is incorporated into an object of daily living such as a cup or drinking vessel, spoon, knife, fork or comb, and typically comprises a pressure sensor and a motion sensor comprising an accelerometer and a gyroscope.”
Analyser in claims 50 and 70 – [0011] “The analyser comprises a processor and a memory containing code”
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 50, 52-62, 65 and 67-71 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pathak (US 20170100272 A1) in view of Kempf (WO 2007003417 A2) and Murphy (US 11194888 B1).
Regarding claim 50, Pathak teaches a movement monitoring system for objectively quantifying a motor control disorder in a subject (Handheld tool 100, user-assistive device 105, cup-holder device 400), the system comprising: (a) a movement detection device generating movement data representing movement of a limb of the subject, wherein the movement detection device comprises sensors measuring at least motion of the device ([0016] “The illustrated embodiment of handheld tool 100 includes leveling IMU 145 disposed on attachment arm 145, which is rigidly connected to user-assistive device 105 to measure motions and orientation of user-assistive device 105. Leveling IMU 145 outputs feedback data indicative of the measured motions and orientation to motion control system 150. Leveling IMU 145 may be implemented with a gyroscope and accelerometer, or even additionally include a magnetometer.”). However, Pathak fails to disclose measuring pressure applied to the device.
Kempf teaches a fluidic stylus for recording neuromotor data of a hand-guided movement. Kempf discloses and pressure applied to the device by the subject (Pg 4 [18] “The fluid pressure P within the fluid chamber 4 corresponds to the force occurring during a hand-guided movement. This can be any hand-guided movement in space, d. H. three-dimensional, or on a writing pad, d. H. two-dimensional, acting. The forces occurring during the hand-guided movement comprise, on the one hand, the forces of inertia which occur during an acceleration a and, on the other hand, the finger pressure forces which are applied by the gripping fingers 6 and which arise as a result of muscle contraction.” Pressure sensor 5, piezoelectric layer 8B).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Pathak to include pressure applied to the device by the subject as disclosed in Kempf to detect with high sensitivity and selectivity the neuromotor properties of the fine motor skills of a person's fingers, hand, arm and shoulder (Kempf Pg 2 [8]).
The combination of Pathak/Kempf further discloses and (b) an analyser for analysing the movement data, the analyser comprising a processor and a memory containing code which (Pathak: [0026] “motion control system 205 is software/firmware logic executed on system controller 215 and stored in system memory 210”), when executed by the processor:
(i) receives the movement data generated by the movement detection device (Pathak: [0017] “motion control system 150 polls leveling IMU 145 for linear accelerations, angular velocity, and orientation relative to a frame of reference (e.g., gravity vector) of user-assistive device 105 at a given instant. Motion control system 150 then executes an algorithm to estimate the orientation of user-assistive device 105 in three-dimensional (“3D”) space relative to the frame of reference;” Kempf: Pg 5 [4] “The pressure sensor 5 detects the fluid pressure P and converts it into an electrical signal for further data processing by a data processing unit.”);
(ii) applies the received movement data to an algorithmic model stored in the memory and identifies one or more features from the movement data that represent disordered movement by the subject (Pathak: [0023] “Additionally, system controller 160 can be programmed to monitor and collect data about the severity of the user's condition (e.g., ability to maintain a level orientation, amount of feedback control assistance needed, amount of unintentional tremor motions, etc.) and store this data into a log within system memory 165 for eventual output via communication interface 170. The log can be analyzed and provided to a healthcare provider to diagnose and treat the user/patient's condition. The active control provided by motion control system 150 can further be programmed to automatically adjust in small increments overtime as part of a therapy plan.” Kempf: Pg 5 [6] “By means of data processing software, neuromotor characteristics are obtained from the fluidic sensor data of pen 1, which provide a detailed, quantitative and rapid analysis … on the neuromotor behavior of the fingers 6, the wrist and the arm”).
While the combination of Pathak/Kempf collects and outputs information about the severity of the subject’s condition, the combination fails to explicitly disclose a score. Murphy teaches a system is provided to monitor, over time, one or more physical variables related to a severity or progression of a movement disorder and/or of symptoms thereof. Murphy discloses and (iii) calculates from the one or more identified features a score corresponding to the existence of the motor control disorder in the subject (Col 13 lines 10-19 “Using such a health model could include determining a mean, standard deviation, distribution shape, or other properties of one or more of the samples of characteristics and/or of the information detected during the wearer's performance of the prompted motor task(s). The health model could apply such determined properties, or the sensor signals themselves, to a linear regression model, a nonlinear regression model, a neural network, a principal components model, or some other model or algorithm to generate a disease severity score or other health state information.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Pathak/Kempf to include a score corresponding to the existence of the motor control disorder as disclosed in Murphy to determine a standard clinical rating or score, or to determine some other information related to the presence, progression, severity, or other properties of a disease state or process (Murphy Col 15 lines 5-10).
The combination of Pathak/Kempf/Murphy further discloses wherein the analyser applies the received movement data to one or more of:
a first algorithmic model to identify a first set of features used by the processor to calculate a selection score which is indicative of presence or absence of the motor control disorder in the subject (Kempf: pressure sensor 5, piezoelectric foil layer 8B, Pg 5 [11] – pg 6 [1] “The piezo-foil preferably simultaneously detects a high-frequency vibration of the pencil lead and a low-frequency oscillation which is exerted on the first fluid chamber portion by a finger in the hand-guided movement.” Pg 9 [1] “The writing instrument 1 according to the invention is suitable for computer-aided diagnosis and therapy of neuromotor-specific diseases and for the quantitative analysis of the neuromotor effect of drugs, drugs or stress.” Kempf pg 5 [11]-pg 6 [1]). “As a result, the piezo layer 8B of the elastic sleeve 8 according to FIG. 4b electrically detects both the finger forces exerted on the first fluid chamber section 4A and body vibrations of the pin body 2. Vibrations of the pencil lead 3 are transmitted to the pin body 2 and are detected in the region of the direct contact of the sleeve 2 with the pin body 2 by the piezo-layer 8B and applied as sensor signals to the data processing unit 9. The piezo-layer 8B thus simultaneously detects the relatively high-frequency vibrations of the pencil lead 3 and the relatively low-frequency oscillations of the occurring finger forces. These signal components can be separated with electric filters. The piezo foils 7 or piezo layers 8B used in the embodiments according to FIGS. 3, 4 are preferably designed as strips which are arranged in the longitudinal direction of the pin 1. In this case, an associated strip-shaped piezo foil is preferably provided for each finger 6, ie for the thumb, the middle finger and the index finger, which hold the pin 1. Capture the different piezo films separately applied by the respective finger pressure forces and thus give additional information regarding the neuromotor function of the various fingers. To increase the pressure sensitivity, the piezo foils are coated with an elastic material and installed several times folded or rolled up.”);
a second algorithmic model to identify a second set of features used by the processor to calculate a severity score which is indicative of severity of the motor control disorder in the subject (Pathak: [0017] “motion control system 150 polls leveling IMU 145 for linear accelerations, angular velocity, and orientation relative to a frame of reference (e.g., gravity vector) of user-assistive device 105 at a given instant.” [0029] “The relative position information output by position sensors 235 may be recorded to a log within system memory 210 for determining how much auto-leveling a user needs and thereby diagnosing the severity and progress of a given user.”); and
a third algorithmic model to identify a third set of features used by the processor to calculate a progression score which is indicative of progression of the motor control disorder in the subject (Murphy: Col 13 lines 24-27 “The health model could receive multiple different samples of the same characteristic from the same wearer that were generated during different periods of time, that correspond to different activities, or that differ with respect to some other consideration. For example, the health model could compare different samples of the same characteristic that were generated during different, non-overlapping periods of time in order to determine an amount of progression of a disease.”); and
wherein the identified first set features is not identical to the identified second set of features (The first set features being the “relatively high-frequency vibrations of the pencil lead 3 and the relatively low-frequency oscillations of the occurring finger forces” from the piezo-layer 8B described in Kempf pg 5 [6] and pg 5 [11]-pg 6 [1] to identify neuromotor disease, while the second set features incorporate, from Pathak [0016], a “Leveling IMU 145 may be implemented with a gyroscope and accelerometer, or even additionally include a magnetometer” and, from Pathak [0029], where “Position sensors 235 are relative sensors that measure the relative positions of the outputs of actuator assembly 115 relative to handle 120. In one embodiment, position sensors 235 are hall-effect sensors that monitor the position of the outputs of actuators 125 and 130 by measuring the positions of linkages 135 and 140… for determining how much auto-leveling a user needs and thereby diagnosing the severity and progress of a given user”).
Regarding claim 52, the combination of Pathak/Kempf/Murphy discloses the movement monitoring system according to claim 50, wherein the severity score calculated by the processor corresponds to a score obtained according to a clinical scale (Murphy: Col 13 lines 43-50 “the process 150 could include prompting the wearer to perform the UPDRS and/or MSFC assessment tasks and detecting one or more signals related to the wearer's performance of the prompted tasks, such that a UPDRS and/or MSFC score may be determined, from the detected one or more signals, based on the wearer's performance of the prompted UPDRS and/or MSFC assessment tasks.”).
Regarding claim 53, the combination of Pathak/Kempf/Murphy discloses the movement monitoring system according to claim 50, wherein the first set of features used by the processor to calculate the selection score comprises one of the following feature sets:
(a) PrRF (Kempf: pressure sensor 5, piezoelectric foil layer 8B, Pg 5 [11] – pg 6 [1] “The piezo-foil preferably simultaneously detects a high-frequency vibration of the pencil lead and a low-frequency oscillation which is exerted on the first fluid chamber portion by a finger in the hand-guided movement.” Murphy: Col 13 lines 10-14 “Using such a health model could include determining a mean, standard deviation, distribution shape, or other properties of one or more of the samples of characteristics and/or of the information detected during the wearer's performance of the prompted motor task(s).);
or (b) PrRF (Kempf: pressure sensor 5, piezoelectric foil layer 8B), AccRFx, GyroMRxyz (Pathak: [0036] accelerometer 350, gyroscope 345, [0019] “If user-assistive device 105 is pitched or rolled relative to the fixed reference frame (e.g., gravity vector), the motion control system 150 will command actuator assembly 115 to move user-assistive device 105 in opposite directions to compensate and retain a level orientation or even provide an offsetting orientation to counteract a tremor.”; Murphy: Col 20 line 64 - Col 21 line 4 “The signal 400 could be an output from a sensor (e.g., an output of an accelerometer that is related to the amplitude of a tremor exhibited by the hand or arm of the person) or the signal 400 could be a property of the person's motor activity determined from one or more sensor signals (e.g., a continuously or near-continuously determined fraction of time the person spent engaged in locomotion during a previous period of time)”), Sm (“Accelerometer data Δ.sub.A is low pass filtered to remove high frequency changes due to sudden jerks, such as tremor motions, which are less useful for the low frequency auto-leveling function”), At (Murphy: Col 19 lines 40-41 “a latency or other time characteristic of performance of the motor task following a prompt”), and PrM (Kempf: pressure sensor 5, piezoelectric foil layer 8B; Murphy: Col 13 lines 10-19 “Using such a health model could include determining a mean, standard deviation, distribution shape, or other properties of one or more of the samples of characteristics and/or of the information detected during the wearer's performance of the prompted motor task(s). The health model could apply such determined properties, or the sensor signals themselves, to a linear regression model, a nonlinear regression model, a neural network, a principal components model, or some other model or algorithm to generate a disease severity score or other health state information.”), wherein: PrRF is the resonant frequency of pressure, AccRFx r is the resonant frequency of acceleration in X-axis, GyroMRxyz is the magnitude at resonance of abs. angular velocity in XYZ-axis, Sm is the movement smoothness using dimensionless jerk (dominant direction), At is the accommodation time, PrM is the mean time period per pressure cycle, and [Symbol font/0x71]cRF is the resonant frequency of pitch angle using complimentary filter.
Regarding claim 54, the combination of Pathak/Kempf/Murphy discloses the movement monitoring system according to claim 50, wherein the second set of features used by the processor to calculate the severity score (Murphy: Col 13 lines 10-19 “Using such a health model could include determining a mean, standard deviation, distribution shape, or other properties of one or more of the samples of characteristics and/or of the information detected during the wearer's performance of the prompted motor task(s). The health model could apply such determined properties, or the sensor signals themselves, to a linear regression model, a nonlinear regression model, a neural network, a principal components model, or some other model or algorithm to generate a disease severity score or other health state information.”) is selected from a group comprising PrRF (Kempf: pressure sensor 5, piezoelectric foil layer 8B, Pg 5 [11] – pg 6 [1] “The piezo-foil preferably simultaneously detects a high-frequency vibration of the pencil lead and a low-frequency oscillation which is exerted on the first fluid chamber portion by a finger in the hand-guided movement.”), PrM (Kempf: pressure sensor 5, piezoelectric foil layer 8B; Murphy: Col 13 lines 10-11 “Using such a health model could include determining a mean”), At (Murphy: Col 19 lines 40-41 “a latency or other time characteristic of performance of the motor task following a prompt”), and [Symbol font/0x71]cRF (Pathak: [0019] “If user-assistive device 105 is pitched or rolled relative to the fixed reference frame (e.g., gravity vector), the motion control system 150 will command actuator assembly 115 to move user-assistive device 105 in opposite directions to compensate and retain a level orientation or even provide an offsetting orientation to counteract a tremor.”), wherein: PrRF is the resonant frequency of pressure, PrM is the mean time period per pressure cycle, At is the accommodation time, and [Symbol font/0x71]cRF is the resonant frequency of pitch angle using complimentary filter.
Regarding claim 55, the combination of Pathak/Kempf/Murphy discloses the movement monitoring system according to claim 50, wherein the third set of features used by the processor to calculate the progression score is selected from a group comprising: (a) MRpr, SRFgyr, MSEaccTMF2 , Sv1-HTgyr , ROM[Symbol font/0x71], MRveI, SRFacc, Sv1-HTpr (Kempf: pressure sensor 5, piezoelectric foil layer 8B. Pathak: [0036] accelerometer 350, gyroscope 345, [0019] “If user-assistive device 105 is pitched or rolled relative to the fixed reference frame (e.g., gravity vector), the motion control system 150 will command actuator assembly 115 to move user-assistive device 105 in opposite directions to compensate and retain a level orientation or even provide an offsetting orientation to counteract a tremor.” Murphy: Col 11 lines 55-62 “the process 150 could include determining a velocity of locomotion, a regularity in time or space of footsteps taken during locomotion, a magnitude or frequency of a tremor, a period of time taken to perform the prompted task, a latency between a prompt to perform a motor task and performance of such motor task, a period of time taken to stand and/or to sit, or some other information about the prompted motor task.” Col 13 lines 10-19 “Using such a health model could include determining a mean, standard deviation, distribution shape, or other properties of one or more of the samples of characteristics and/or of the information detected during the wearer's performance of the prompted motor task(s). The health model could apply such determined properties, or the sensor signals themselves, to a linear regression model, a nonlinear regression model, a neural network, a principal components model, or some other model or algorithm to generate a disease severity score or other health state information.” Col 15 lines 5-10 “determine a standard clinical rating or score, or to determine some other information related to the presence, progression, severity, or other properties of a disease state or process”), wherein: MRopis the magnitude at resonance of pressure, SRFvr is the second peak of angular acceleration after application of FFT for the Gyro signal, MSETMRis the Mean Square Energy of the second Intrinsic Mode Function obtained from acceleration, Svl-HTQVr is the first singular value of the Hilbert spectra of acceleration, ROMe is the range of motion of the movement detection device, MRveIis the magnitude at resonance of velocity, SRFacc is the second resonance peak of linear acceleration after application of FFT,Soz-HTpr is the first singular value of the Hilbert spectra of pressure.
Regarding claim 56, the combination of Pathak/Kempf/Murphy discloses the movement monitoring system according to claim 50, comprising one or both of: wherein the plurality of movement characteristics correlate to clinically accepted descriptions of movement disorder; and wherein the clinically accepted descriptions relate to one or more of stability, timing, accuracy and rhythmicity of the movement (Murphy: Col 13 lines 28-39 “ the process 150 could include determining a clinical standard score or some other rating of a wearer's disease state and/or performance of one or more motor tasks. For example, the process 150 could include determining a UPDRS and/or MSFC score for the wearer. Such a score or rating could be determined based on known relationship between measured and/or determined properties of the wearer's motor activities and the corresponding score or rating (e.g., based on relationship determined by measuring both the score or rating for a population of wearers and determined properties of the wearers' motor activities).”).
Regarding claim 57, the combination of Pathak/Kempf/Murphy discloses the movement monitoring system according to claim 50, wherein the movement detection device simulates or is incorporated into an object of daily living (Pathak: Handheld tool 100, user-assistive device 105, cup-holder device 400) and comprises one or more of: (a) a pressure sensor (Kempf: pressure sensor 5, piezoelectric foil layer 8B); (b) an accelerometer; and (c) a gyroscope (Pathak: accelerometer 350, gyroscope 345).
Regarding claim 58, the combination of Pathak/Kempf/Murphy discloses the movement monitoring system according to claim 50, wherein the movement detection device comprises a canister with a grasping portion (Pathak: Handheld tool 100, user-assistive device 105, cup-holder device 400) and a pressure sensor for measuring pressure applied to the grasping portion by the subject (Kempf: pressure sensor 5, piezoelectric foil layer 8B; Pg 4 [18] “the finger pressure forces which are applied by the gripping fingers 6 and which arise as a result of muscle contraction.”).
Regarding claim 59, the combination of Pathak/Kempf/Murphy discloses the movement monitoring system according to claim 50, wherein the motor control disorder is spasticity, and features identified in the movement data that are used to indicate presence of spasticity include
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(Kempf: pressure sensor 5, piezoelectric foil layer 8B, Pg 5 [11] – pg 6 [1] “The piezo-foil preferably simultaneously detects a high-frequency vibration of the pencil lead and a low-frequency oscillation which is exerted on the first fluid chamber portion by a finger in the hand-guided movement.” fluidic pressure sensor 5; Murphy: Col 13 lines 10-14 “Using such a health model could include determining a mean, standard deviation, distribution shape, or other properties of one or more of the samples of characteristics and/or of the information detected during the wearer's performance of the prompted motor task(s).”), wherein: PrsD is the pressure variation during compress cycle, PrMS is the RMS value of pressure, PrMRis the magnitude at resonance of pressure, and PrRF is the resonant frequency of pressure.
Regarding claim 60, the combination of Pathak/Kempf/Murphy discloses a movement detection device for use with the system of claim 50, the movement detection device comprising:
(a) a grasping portion (Pathak: Handheld tool 100, user-assistive device 105, cup-holder device 400, Fig. 4); and
(b) a movement sensor comprising at least a pressure sensor generating pressure data representing pressure applied to the grasping portion (Kempf: Pressure sensor 5, piezoelectric layer 8B, fluid chamber 4; Fig. 4A) and a motion sensor generating motion data representing movement of the device in multiple axes (Pathak: [0016] “Leveling IMU 145 may be implemented with a gyroscope and accelerometer, or even additionally include a magnetometer”);
wherein the movement detection device simulates or is incorporated into an object of daily living (cup-holder device 400).
Regarding claim 61, the combination of Pathak/Kempf/Murphy discloses the movement detection device according to claim 60, wherein the object of daily living is selected from a group comprising: (a) a cup or drinking vessel (Pathak: Handheld tool 100, user-assistive device 105, cup-holder device 400); (b) a spoon or eating utensil (Pathak: Figs. 1A-D).
Regarding claim 62, the combination of Pathak/Kempf/Murphy discloses the movement detection device according to claim 60, comprising a canister simulating a cup or drinking vessel (Pathak: cup-holder device 400), the canister comprising one or both of: a flexible body portion forming a fluid filled chamber and defining the grasping portion, and wherein the pressure sensor is a differential pressure sensor with a first input in fluid communication with the chamber and a second input in fluid communication with atmospheric pressure (Kempf: Pressure sensor 5, piezoelectric layer 8B, fluid chamber 4; Fig. 4A).
Regarding claim 65, Pathak teaches an automated method for objectively quantifying a motor control disorder in a subject ([0011] “method of operation for providing auto-leveling of a user-assistive device of a handheld tool”). However, Pathak fails to disclose pressure data.
The combination of Pathak/Kempf discloses, comprising the steps of:
(a) receiving movement data at a processor, corresponding to movements of a limb of the subject, the movement data comprising at least pressure data and motion data (Pathak: [0017] “motion control system 150 polls leveling IMU 145 for linear accelerations, angular velocity, and orientation relative to a frame of reference (e.g., gravity vector) of user-assistive device 105 at a given instant. Motion control system 150 then executes an algorithm to estimate the orientation of user-assistive device 105 in three-dimensional (“3D”) space relative to the frame of reference;” Kempf: Pg 5 [4] “The pressure sensor 5 detects the fluid pressure P and converts it into an electrical signal for further data processing by a data processing unit.”);
(b) the processor applying the received movement data to an algorithmic model and identifying one or more features from the movement data that represent disordered movement in the subject (Pathak: [0023] “Additionally, system controller 160 can be programmed to monitor and collect data about the severity of the user's condition (e.g., ability to maintain a level orientation, amount of feedback control assistance needed, amount of unintentional tremor motions, etc.) and store this data into a log within system memory 165 for eventual output via communication interface 170. The log can be analyzed and provided to a healthcare provider to diagnose and treat the user/patient's condition. Kempf: Pg 5 [6] “By means of data processing software, neuromotor characteristics are obtained from the fluidic sensor data of pen 1, which provide a detailed, quantitative and rapid analysis … on the neuromotor behavior of the fingers 6, the wrist and the arm”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Pathak to include pressure applied to the device by the subject as disclosed in Kempf to detect with high sensitivity and selectivity the neuromotor properties of the fine motor skills of a person's fingers, hand, arm and shoulder (Kempf Pg 2 [8]).
While the combination of Pathak/Kempf collects and outputs information about the severity of the subject’s condition, the combination fails to explicitly disclose a score.
Murphy discloses (c) the processor calculating, from the one or more identified features, a score quantifying the motor control disorder in the subject; and (d) the processor generating a display signal causing the calculated score to be presented on a display device (Col 13 lines 10-19 “Using such a health model could include determining a mean, standard deviation, distribution shape, or other properties of one or more of the samples of characteristics and/or of the information detected during the wearer's performance of the prompted motor task(s). The health model could apply such determined properties, or the sensor signals themselves, to a linear regression model, a nonlinear regression model, a neural network, a principal components model, or some other model or algorithm to generate a disease severity score or other health state information.” Col 22 lines 57-59 “The display 570 may be configured to display a visual indication of an alert, recommendation, health state, or other information.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Pathak/Kempf to include displaying a score quantifying the motor control disorder as disclosed in Murphy to determine a standard clinical rating or score, or to determine some other information related to the presence, progression, severity, or other properties of a disease state or process (Murphy Col 15 lines 5-10).
The combination of Pathak/Kempf/Murphy further discloses:
wherein the processor applies the received movement data to one or more of:
a first algorithmic model to identify a first set of features used by the processor to calculate a selection score which is indicative of presence or absence of the motor control disorder in the subject (Kempf: pressure sensor 5, piezoelectric foil layer 8B, Pg 5 [11] – pg 6 [1] “The piezo-foil preferably simultaneously detects a high-frequency vibration of the pencil lead and a low-frequency oscillation which is exerted on the first fluid chamber portion by a finger in the hand-guided movement.” Pg 9 [1] “The writing instrument 1 according to the invention is suitable for computer-aided diagnosis and therapy of neuromotor-specific diseases and for the quantitative analysis of the neuromotor effect of drugs, drugs or stress.” Kempf pg 5 [11]-pg 6 [1]). “As a result, the piezo layer 8B of the elastic sleeve 8 according to FIG. 4b electrically detects both the finger forces exerted on the first fluid chamber section 4A and body vibrations of the pin body 2. Vibrations of the pencil lead 3 are transmitted to the pin body 2 and are detected in the region of the direct contact of the sleeve 2 with the pin body 2 by the piezo-layer 8B and applied as sensor signals to the data processing unit 9. The piezo-layer 8B thus simultaneously detects the relatively high-frequency vibrations of the pencil lead 3 and the relatively low-frequency oscillations of the occurring finger forces. These signal components can be separated with electric filters. The piezo foils 7 or piezo layers 8B used in the embodiments according to FIGS. 3, 4 are preferably designed as strips which are arranged in the longitudinal direction of the pin 1. In this case, an associated strip-shaped piezo foil is preferably provided for each finger 6, ie for the thumb, the middle finger and the index finger, which hold the pin 1. Capture the different piezo films separately applied by the respective finger pressure forces and thus give additional information regarding the neuromotor function of the various fingers. To increase the pressure sensitivity, the piezo foils are coated with an elastic material and installed several times folded or rolled up.”);
a second algorithmic model to identify a second set of features used by the processor to calculate a severity score which is indicative of severity of the motor control disorder in the subject (Pathak: [0017] “motion control system 150 polls leveling IMU 145 for linear accelerations, angular velocity, and orientation relative to a frame of reference (e.g., gravity vector) of user-assistive device 105 at a given instant.” [0029] “The relative position information output by position sensors 235 may be recorded to a log within system memory 210 for determining how much auto-leveling a user needs and thereby diagnosing the severity and progress of a given user.”); and
a third algorithmic model to identify a third set of features used by the processor to calculate a progression score which is indicative of progression of the motor control disorder in the subject (Murphy: Col 13 lines 24-27 “The health model could receive multiple different samples of the same characteristic from the same wearer that were generated during different periods of time, that correspond to different activities, or that differ with respect to some other consideration. For example, the health model could compare different samples of the same characteristic that were generated during different, non-overlapping periods of time in order to determine an amount of progression of a disease.”);
wherein the identified first set features is not identical to the identified second set of features (The first set features being the “relatively high-frequency vibrations of the pencil lead 3 and the relatively low-frequency oscillations of the occurring finger forces” from the piezo-layer 8B described in Kempf pg 5 [6] and pg 5 [11]-pg 6 [1] to identify neuromotor disease, while the second set features incorporate, from Pathak [0016], a “Leveling IMU 145 may be implemented with a gyroscope and accelerometer, or even additionally include a magnetometer” and, from Pathak [0029], where “Position sensors 235 are relative sensors that measure the relative positions of the outputs of actuator assembly 115 relative to handle 120. In one embodiment, position sensors 235 are hall-effect sensors that monitor the position of the outputs of actuators 125 and 130 by measuring the positions of linkages 135 and 140… for determining how much auto-leveling a user needs and thereby diagnosing the severity and progress of a given user”).
Regarding claim 67, the combination of Pathak/Kempf/Murphy discloses the automated method according to claim 65,
comprising the step of categorising movement dysfunction in the subject, by the processor calculating a contribution made by each of the first or second set of features to each of a plurality of movement characteristics that are attributable to movement dysfunction in the subject (Kempf: pressure sensor 5, piezoelectric foil layer 8B, Pg 5 [11] – pg 6 [1] “The piezo-foil preferably simultaneously detects a high-frequency vibration of the pencil lead and a low-frequency oscillation which is exerted on the first fluid chamber portion by a finger in the hand-guided movement.” Pg 9 [1] “The writing instrument 1 according to the invention is suitable for computer-aided diagnosis and therapy of neuromotor-specific diseases and for the quantitative analysis of the neuromotor effect of drugs, drugs or stress.” Murphy: Col 13 lines 10-19 “Using such a health model could include determining a mean, standard deviation, distribution shape, or other properties of one or more of the samples of characteristics and/or of the information detected during the wearer's performance of the prompted motor task(s). The health model could apply such determined properties, or the sensor signals themselves, to a linear regression model, a nonlinear regression model, a neural network, a principal components model, or some other model or algorithm to generate a disease severity score or other health state information.”).
Regarding claim 68, the combination of Pathak/Kempf/Murphy discloses the automated method according to claim 65, wherein the received movement data is obtained from a movement detection device (Pathak: Handheld tool 100, user-assistive device 105, cup-holder device 400) and comprises at least one or both of: pressure data corresponding to pressure applied to the device by the subject (Kempf: Pg 5 [4] “The pressure sensor 5 detects the fluid pressure P and converts it into an electrical signal for further data processing by a data processing unit.”); and motion data comprising one or more of position of the limb, acceleration of the limb and angular position of the limb (Pathak: [0017] “motion control system 150 polls leveling IMU 145 for linear accelerations, angular velocity, and orientation relative to a frame of reference (e.g., gravity vector) of user-assistive device 105 at a given instant. Motion control system 150 then executes an algorithm to estimate the orientation of user-assistive device 105 in three-dimensional (“3D”) space relative to the frame of reference”).
Regarding claim 69, the combination of Pathak/Kempf/Murphy discloses the automated method according to claim 65, wherein the received movement data is collected while the subject performs a movement task and preferably wherein the movement task is or simulates an activity of daily living (Pathak: “user-assistive device 105 may be implemented as a variety of different eating or drinking utensils (e.g., spoon, knife, fork, cup-holder), personal hygiene tools (e.g., toothbrush, floss pick), grooming tools (e.g., makeup applicator, comb), occupational tools (e.g., surgical tools), pointing devices (e.g., laser pointer or stick pointer), or otherwise. The auto-leveling (and optional tremor stabilization) functionality can help users who have uncoordinated (and/or unintentional) muscle movements to have improved quality of life by providing greater independence and self-control over routine tasks.”).
Regarding claim 70, the combination of Pathak/Kempf/Murphy discloses the movement monitoring system according to claim 50, wherein the analyser categorises movement dysfunction in the subject by the processor calculating a contribution made by each of the first, second or third set of features to each of a plurality of movement characteristics that are attributable to movement dysfunction in the subject (Kempf: pressure sensor 5, piezoelectric foil layer 8B, Pg 5 [11] – pg 6 [1] “The piezo-foil preferably simultaneously detects a high-frequency vibration of the pencil lead and a low-frequency oscillation which is exerted on the first fluid chamber portion by a finger in the hand-guided movement.” Pathak: [0017] “motion control system 150 polls leveling IMU 145 for linear accelerations, angular velocity, and orientation relative to a frame of reference (e.g., gravity vector) of user-assistive device 105 at a given instant. Motion control system 150 then executes an algorithm to estimate the orientation of user-assistive device 105 in three-dimensional (“3D”) space relative to the frame of reference;” [0029] “Position sensors 235 are relative sensors that measure the relative positions of the outputs of actuator assembly 115 relative to handle 120. In one embodiment, position sensors 235 are hall-effect sensors that monitor the position of the outputs of actuators 125 and 130 by measuring the positions of linkages 135 and 140 … for determining how much auto-leveling a user needs and thereby diagnosing the severity and progress of a given user.”), and the analyser sums the contribution made by each of the features to each of the plurality of movement characteristics to determine a collective contribution to each of the plurality of movement characteristics (Murphy: Col 13 lines 10-19 “Using such a health model could include determining a mean, standard deviation, distribution shape, or other properties of one or more of the samples of characteristics and/or of the information detected during the wearer's performance of the prompted motor task(s). The health model could apply such determined properties, or the sensor signals themselves, to a linear regression model, a nonlinear regression model, a neural network, a principal components model, or some other model or algorithm to generate a disease severity score or other health state information.”).
Regarding claim 71, the combination of Pathak/Kempf/Murphy discloses the automated method according to claim 65, comprising one or both of: wherein the plurality of movement characteristics correlate to clinically accepted descriptions movement disorder; and wherein the clinically accepted descriptions relate to one or more of stability, timing, accuracy and rhythmicity of the movement (Murphy: Col 13 lines 28-39 “the process 150 could include determining a clinical standard score or some other rating of a wearer's disease state and/or performance of one or more motor tasks. For example, the process 150 could include determining a UPDRS and/or MSFC score for the wearer. Such a score or rating could be determined based on known relationship between measured and/or determined properties of the wearer's motor activities and the corresponding score or rating (e.g., based on relationship determined by measuring both the score or rating for a population of wearers and determined properties of the wearers' motor activities).”).
Claim(s) 63 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pathak (US 20170100272 A1) in view of Kempf (WO 2007003417 A2) and Murphy (US 11194888 B1), and in further view of Bullington (US 20160361006 A1) and Smith (Mug for Tremors Senior Project, Cal Poly).
Regarding claim 63, the combination of Pathak/Kempf/Murphy discloses the movement detection device according to claim 60. However, the combination of Pathak/Kempf/Murphy fails to disclose a valve.
Bullington teaches an apparatus that includes a housing, defining an inner volume, and an actuator mechanism movably disposed therein. Bullington discloses comprising one or both of: a one-way valve for releasable coupling with a fluid source to restore fluid pressure in the chamber ([0079] “an inlet port can include a valve configured to transition to an open configuration in response to a negative pressure within a fluid reservoir and transition of a closed configuration in response to a positive pressure within the fluid reservoir… one-way valve”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Pathak/Kempf/Murphy to include a one-way valve to enable the addition of fluid that creates positive pressure within the fluid reservoir (Bullington [0079]). However, the combination of Pathak/Kempf/Murphy/Bullington fails to disclose a rigid base containing a microcontroller or wireless communication module.
Smith teaches a mug with stabilizing gimbal technology for use by individuals with hand tremors. Smith discloses wherein the canister comprises a rigid base containing one or both of a microcontroller and a wireless communication module (see controller board in rigid base of mug in figure below).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Pathak/Kempf/Murphy/Bullington to include a rigid base containing a microcontroller and wireless communication module by the subject as disclosed in Smith to enhance stabilization of the mug and more easily access the electronics for calibration and testing (Smith, Pg 14 Electronics section [1]).
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Screenshots of Smith (Mug for Tremors Team Senior Project, Cal Poly College of Engineering), bottom exploded view
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.
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/M.H./Examiner, Art Unit 3791
/DEVIN B HENSON/Primary Examiner, Art Unit 3791