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 .
Claims Pending
Claims 1-21 are currently under examination.
Claim Objections
Claims 13 and 15 objected to because of the following informalities:
In claim 13, “the peak response time from a largest acceleration”, should read -the peak response time is from a largest acceleration-
In claim 15, “strok”, should read -Stroke-
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 16 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 16 recites the limitation “providing the characteristics to a trained machine learning model trained on sets of patient condition labeled characteristics of prior collected data on multiple patients; and receiving an output of the trained machine learning model comprising the patient condition”, where the applicant’s specification lacks sufficient detail regarding the structure of the machine learning model that outputs the patient condition. The applicant’s specification does state “the telemetry data can be logged locally for later download, or aggregated and compiled for real time direction for machine learning or other database-derivative type analysis and comparison. The database or machine learning can be a local computer application” (Par. 42 of applicant’s spec.), further provides an overview of artificial neural networks (Par. 62-66 of applicant’s spec.), and states “training data may comprise sampled values of one or more of the curves shown in FIG. 6. The sampling rate may be varied in different examples such as every 0. 1 seconds or 0.00 1 seconds. The sampled values may be provided to a deep neural network or other suitable network for generation of a model to classify the sampled signals”. However, simply reciting an overview of artificial neural networks and further reciting the use of machine learning does not amount to sufficient support. The applicants specification lacks sufficient detail in regards to the exact weights, biases, or layers present within the machine learning model itself that leads to the indicated output comprising the patient condition. As such, the claim is rejected.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 11-21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 11 recites the limitation “correlating the characteristics of the received data to medically characterize a patient condition”, which fails to effectively define the metes and bounds of the claim as it is unclear as to how the characteristics are correlated and what they are correlated to in order to characterize a patient condition. As such, the claim is indefinite as the applicant has failed to effectively define the metes and bounds of the claim. For examination purposes, this will be interpreted as being correlated to one or more patient conditions, which include normal responses (Par. 58 of applicant’s spec.).
The term “sudden” in claim 12 is a relative term which renders the claim indefinite. The term “sudden” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear as to how much “deceleration” a “sudden” deceleration would be. As such, the claim is indefinite as the applicant has failed to effectively define the metes and bounds of the claim. For examination purposes, this will be interpreted as without the word “sudden”.
Claim 16 recites the limitation “providing the characteristics to a trained machine learning model trained on sets of patient condition labeled characteristics of prior collected data on multiple patients; and receiving an output of the trained machine learning model comprising the patient condition”, which fails to effectively define the metes and bounds of the claim as it is unclear as to the structure of the machine learning model that performs the indicated functions. The applicant’s specification does state “the telemetry data can be logged locally for later download, or aggregated and compiled for real time direction for machine learning or other database-derivative type analysis and comparison. The database or machine learning can be a local computer application” (Par. 42 of applicant’s spec.), further provides an overview of artificial neural networks (Par. 62-66 of applicant’s spec.), and states “training data may comprise sampled values of one or more of the curves shown in FIG. 6. The sampling rate may be varied in different examples such as every 0. 1 seconds or 0.00 1 seconds. The sampled values may be provided to a deep neural network or other suitable network for generation of a model to classify the sampled signals”. However, simply reciting an overview of artificial neural networks and further reciting the use of machine learning does not amount to sufficient support. The applicants specification lacks sufficient detail in regards to the exact weights, biases, or layers present within the machine learning model itself that leads to the indicated output comprising the patient condition. Additionally, a term cannot be defined by itself. As such, the claim is indefinite as the applicant has failed to effectively define the metes and bounds of the claim. For examination purposes, this will be interpreted as any generic algorithm capable of the indicated functions.
Claim 17 recites the limitation “comparing the first pressure data to a low impact force threshold; and in response to the first pressure data being lower than the impact force threshold, characterizing the strike as not a valid strike”, which fails to effectively define the metes and bounds of the claim as it is unclear how the pressure data can have a force threshold. It would be expected that the pressure data would have a pressure threshold. Is the pressure data simply force data? The applicant’s spec. states “data is generated by the force sensors 130, 135 representative of pounds per square inch (PSI) or pascals on the sensor.” As such, the claim is indefinite as the applicant has failed to effectively define the metes and bounds of the claim. For examination purposes, the pressure data will be interpreted as being the same as force data.
Claim 18 recites the limitation “determining a time of strike of the tendon based on the acceleration data collected from the first accelerometer”, which fails to effectively define the metes and bounds of the claim as it is unclear whether this is the same as the first acceleration data as in claim 11, which claim 18 is dependent on, or if this is different acceleration data. To clarify, claim 11 recites “receiving first acceleration data from a first accelerometer”, which specifically states “first acceleration data”. Is there a reason behind the switch? Is this the same data? This creates confusion as to what data is being referred to. As such, the claim is indefinite as the applicant has failed to effectively define the metes and bounds of the claim. For examination purposes, this data will be interpreted as the first data.
Claim 18 recites the limitation “determining a time of patient response based on the acceleration data collected from the second accelerometer”, which fails to effectively define the metes and bounds of the claim as it is unclear whether this is the same as the second acceleration data as in claim 11, which claim 18 is dependent on, or if this is different acceleration data. To clarify, claim 11 recites “receiving second acceleration data from a second accelerometer”, which specifically states “second acceleration data”. Is there a reason behind the switch? Is this the same data? This creates confusion as to what data is being referred to. As such, the claim is indefinite as the applicant has failed to effectively define the metes and bounds of the claim. For examination purposes, this data will be interpreted as the second data.
Claim 19 recites the limitation "the…” “…force data” in lines 2-3. There is insufficient antecedent basis for this limitation in the claim. As such, the claim is indefinite as the applicant has failed to effectively define the metes and bounds of the claim. For examination purposes, this will be interpreted as being the same as the pressure data.
Claim 21 recites the limitation "the…” “…force data” in lines 3-4. There is insufficient antecedent basis for this limitation in the claim. As such, the claim is indefinite as the applicant has failed to effectively define the metes and bounds of the claim. For examination purposes, this will be interpreted as being the same as the pressure data.
Claims 12-21 are dependent on claim 11, and as such are also rejected.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The claims are generally directed towards a computer-implemented method. The method comprises receiving acceleration and force data from a reflex hammer used to strike a tendon, receiving acceleration data from a second accelerometer coupled to a limb of a patient, and processing the received data to characterize the patient response to the strike.
Claim(s) 1-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Slepian (US Pub. No. 20210275152) hereinafter Slepian, and further in view of Naotaka (JP 2006034809 A) hereinafter Naotaka and Adekanmbi (US Pub. No. 20210361336) hereinafter Adekanmbi.
Regarding claim 1, Slepian discloses A computer implemented method (Par. 30, “FIG. 1 shows one system 100 for digital reflex quantization and signature analysis. System 100 may be used for analyzing superficial reflexes for one or more of biceps, triceps, patellar and Achilles reflexes. System 100 includes a computer 110 with at least one processor 112 that is communicatively coupled with a memory 114 and a sensor interface 116…”)(Par. 34, “A reflex analyzer 130, implemented as machine readable instructions stored in memory 114 and executed by processor 112, processes stimuli data 120, EMG data 122, and motion data 124 to generate a motion signature 132 and an EMG signature 134, each having a time reference with respect to a time of stimulation determined from stimuli data 120…”) (Par. 50, “FIG. 15 is a flowchart illustrating one example method 1500 for digital reflex quantization and signature analysis. Method 1500 is for example implemented at least in part within sensors 104 and computer 110 of FIG. 1.”) comprising:
receiving first acceleration data from a first accelerometer of a reflex hammer used to strike a patient tendon (Par. 52, “a doctor uses stimulating device 106 to strike a patellar tendon of patient 102”)(Par. 52, “In step 1510, method 1500 captures motion of the striking device. In one example of step 1510, computer 110 receives data from sensor 104(1) of captured motion of stimulating device 106…”) (Par. 33, “Sensors 104 may each include one or more accelerometers and gyroscopes for detecting movement, including rotation in the x, y, and z-axes, and may sense accelerative force (G-force) and angular velocity (degrees/second) of the limb/appendage of patient 102…”) (Par. 30, “As shown in FIG. 1, sensor 104(1) is applied to a stimulating device 106 (e.g., a reflex hammer) that is used to provide a stimulus or stimuli”);
receiving second acceleration data from a second accelerometer coupled to a limb of the patient in response to the strike of the patient tendon (Par. 52, “In step 1514, method 1500 captures movement data of the limb/appendage. In one example of step 1514, computer 110 receives data from sensor 104(3) and stores the data as motion data 124 within memory 114.”) (Par. 31, “sensor 104(3) is placed on the limb attached to or associated with the muscle. When the stimulating device 106 strikes the patellar tendon, the sensor 104(3) captures the movement of the distal leg as motion data 124, which is processed to generate motion signature 132 that provides an indication of the actuation of a proximate muscle in response to the physical stimuli…”); and
processing the received data to characterize the patient response to time strike (Par. 53, “step 1518, reflex analyzer 130 processes stimuli data 120 and motion data 124 to generate motion signature 132. In step 1520, method 1500 analyzes signatures to determine a quantitative evaluation of the reflex response…”) (Par. 34, “A reflex analyzer 130, implemented as machine readable instructions stored in memory 114 and executed by processor 112, processes stimuli data 120, EMG data 122, and motion data 124 to generate a motion signature 132…”).
Slepian fails to explicitly disclose force data from a force sensor of a reflex hammer used to strike a patient tendon.
Slepian does disclose a force sensor (Par. 33, “Sensors 104 may each include one or more accelerometers and gyroscopes for detecting movement, including rotation in the x, y, and z-axes, and may sense accelerative force (G-force)”).
Naotaka teaches force data from a force sensor of a reflex hammer used to strike a patient tendon (Par. 11, “the present invention provides a stretch reflex measuring device 1 comprising a reflex hammer 2 with a built-in force sensor 2d…” “…stretch reflex induced when the tendon 7b of the person being measured 7 is stimulated with the reflex hammer 2”)(Par. 76, “Figure 15 is a flowchart of the analysis process performed by the analysis device of the stretch reflection measuring device of the present invention.”) (Par. 37, “the head 2a is mainly cylindrical in shape, and a force sensor 2d for detecting striking force is located near the base of the striking cap 2c at the tip”) (Par. 40, “The force sensor 2d detects the strength of the force applied when the striking cap 2c strikes the person being measured 7.”) (Par. 91, “The analysis process 10 is characterized by receiving impact force data from the force sensor 2d, acceleration data in the three directions…”) (Par. 104, “Transient response occurs when the impact force data is below a certain value…”) (Par. 105, “A steady-state response is observed when the impact force data exceeds a certain value…”).
Adekanmbi teaches a plurality of sensors in a hammer (Par. 79 and 123)(Fig. 7B (hammer – 10, force sensor – 40, IMU - 30)).
Slepian, Naotaka, and Adekanmbi are considered to be analogous art to the claimed invention as they are involved with hammers with sensors.
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the method of Slepian with that of Naotaka and Adekanmbi to include force data from a force sensor of a reflex hammer used to strike a patient tendon through the combination of references as differing sensors are known (Slepian (Par. 33)), using more than one sensor type in a hammer is a known variation in the art (Adekanmbi (Par. 79 and 123)), and it would have yielded the predictable result of providing force information regarding the strike (Naotaka (Par. 104-105) (Par. 91)).
Regarding claim 8, Slepian discloses A machine-readable storage device having instructions for execution by a processor of a machine to cause the processor to perform operations to perform a method (Par. 30, “FIG. 1 shows one system 100 for digital reflex quantization and signature analysis. System 100 may be used for analyzing superficial reflexes for one or more of biceps, triceps, patellar and Achilles reflexes. System 100 includes a computer 110 with at least one processor 112 that is communicatively coupled with a memory 114 and a sensor interface 116…”)(Par. 34, “A reflex analyzer 130, implemented as machine readable instructions stored in memory 114 and executed by processor 112, processes stimuli data 120, EMG data 122, and motion data 124 to generate a motion signature 132 and an EMG signature 134, each having a time reference with respect to a time of stimulation determined from stimuli data 120…”) (Par. 50, “FIG. 15 is a flowchart illustrating one example method 1500 for digital reflex quantization and signature analysis. Method 1500 is for example implemented at least in part within sensors 104 and computer 110 of FIG. 1.”), the operations comprising:
receiving first acceleration data from a first accelerometer of a reflex hammer used to strike a patient tendon (Par. 52, “a doctor uses stimulating device 106 to strike a patellar tendon of patient 102”)(Par. 52, “In step 1510, method 1500 captures motion of the striking device. In one example of step 1510, computer 110 receives data from sensor 104(1) of captured motion of stimulating device 106…”) (Par. 33, “Sensors 104 may each include one or more accelerometers and gyroscopes for detecting movement, including rotation in the x, y, and z-axes, and may sense accelerative force (G-force) and angular velocity (degrees/second) of the limb/appendage of patient 102…”) (Par. 30, “As shown in FIG. 1, sensor 104(1) is applied to a stimulating device 106 (e.g., a reflex hammer) that is used to provide a stimulus or stimuli”);
receiving second acceleration data from a second accelerometer coupled to a limb of the patient in response to the strike of the patient tendon (Par. 52, “In step 1514, method 1500 captures movement data of the limb/appendage. In one example of step 1514, computer 110 receives data from sensor 104(3) and stores the data as motion data 124 within memory 114.”) (Par. 31, “sensor 104(3) is placed on the limb attached to or associated with the muscle. When the stimulating device 106 strikes the patellar tendon, the sensor 104(3) captures the movement of the distal leg as motion data 124, which is processed to generate motion signature 132 that provides an indication of the actuation of a proximate muscle in response to the physical stimuli…”); and
processing the received data to characterize the patient response to the strike (Par. 53, “step 1518, reflex analyzer 130 processes stimuli data 120 and motion data 124 to generate motion signature 132. In step 1520, method 1500 analyzes signatures to determine a quantitative evaluation of the reflex response…”) (Par. 34, “A reflex analyzer 130, implemented as machine readable instructions stored in memory 114 and executed by processor 112, processes stimuli data 120, EMG data 122, and motion data 124 to generate a motion signature 132…”).
Slepian fails to explicitly disclose force data from a force sensor of a reflex hammer used to strike a patient tendon.
Slepian does disclose a force sensor (Par. 33, “Sensors 104 may each include one or more accelerometers and gyroscopes for detecting movement, including rotation in the x, y, and z-axes, and may sense accelerative force (G-force)”).
Naotaka teaches force data from a force sensor of a reflex hammer used to strike a patient tendon (Par. 11, “the present invention provides a stretch reflex measuring device 1 comprising a reflex hammer 2 with a built-in force sensor 2d…” “…stretch reflex induced when the tendon 7b of the person being measured 7 is stimulated with the reflex hammer 2”) (Par. 76, “Figure 15 is a flowchart of the analysis process performed by the analysis device of the stretch reflection measuring device of the present invention.”) (Par. 37, “the head 2a is mainly cylindrical in shape, and a force sensor 2d for detecting striking force is located near the base of the striking cap 2c at the tip”) (Par. 40, “The force sensor 2d detects the strength of the force applied when the striking cap 2c strikes the person being measured 7.”) (Par. 91, “The analysis process 10 is characterized by receiving impact force data from the force sensor 2d, acceleration data in the three directions…”) (Par. 104, “Transient response occurs when the impact force data is below a certain value…”) (Par. 105, “A steady-state response is observed when the impact force data exceeds a certain value…”).
Adekanmbi teaches a plurality of sensors in a hammer (Par. 79 and 123)(Fig. 7B (hammer – 10, force sensor – 40, IMU - 30)).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the method of Slepian with that of Naotaka and Adekanmbi to include force data from a force sensor of a reflex hammer used to strike a patient tendon through the combination of references as differing sensors are known (Slepian (Par. 33)), using more than one sensor type in a hammer is a known variation in the art (Adekanmbi (Par. 79 and 123)), and it would have yielded the predictable result of providing force information regarding the strike (Naotaka (Par. 104-105) (Par. 91)).
Regarding claim 2, modified Slepian further discloses wherein processing the received data comprises:
determining a time of strike of the patient tendon based on the first acceleration data (Slepian (Par. 30, “system 100 may coordinate stimuli strike vs reflex time lag, for example. Sensor 104(1) captures a strike by stimulating device 106, sensor 104(2) captures the EMG response of the muscle, and the sensor 104(3) captures the movement of the limb…”));
determining a time of patient response based on the second acceleration (Slepian (Par. 30, “system 100 may coordinate stimuli strike vs reflex time lag, for example. Sensor 104(1) captures a strike by stimulating device 106, sensor 104(2) captures the EMG response of the muscle, and the sensor 104(3) captures the movement of the limb…”) (Par. 31, “sensor 104(3) is placed on the limb attached to or associated with the muscle. When the stimulating device 106 strikes the patellar tendon, the sensor 104(3) captures the movement of the distal leg as motion data 124, which is processed to generate motion signature 132 that provides an indication of the actuation of a proximate muscle in response to the physical stimuli.”));
and determining a delay of response from the time of strike and time of patient response (Slepian (Par. 30, “system 100 may coordinate stimuli strike vs reflex time lag, for example. Sensor 104(1) captures a strike by stimulating device 106, sensor 104(2) captures the EMG response of the muscle, and the sensor 104(3) captures the movement of the limb…”) (Par. 33, “Sensors 104 may each include one or more accelerometers and gyroscopes for detecting movement, including rotation in the x, y, and z-axes, and may sense accelerative force (G-force) and angular velocity (degrees/second) of the limb/appendage of patient 102…” “…Each reflex produces a small lag time, or twitch interval, between the time of the initial stimulating device strike and the time the reflex occurred. This twitch interval may be quantitated (e.g., quantitatively defined/described) within both EMG signature 134 and motion signature 132…”)).
Regarding claim 3, modified Slepian further discloses wherein the second acceleration data is received via a wireless transmission (Slepian (Par. 30, “sensors 104(1), 104(2), and 104(3) transmit (wired and/or wirelessly) the captured data to sensor interface 116 and the data is stored in memory 114 as stimuli data 120, EMG data 122, and motion data 124, respectively”)).
Regarding claim 4, modified Slepian fails to explicitly disclose the limitations of the claim.
However, Slepian does disclose wherein the first acceleration data a is synchronized with the second acceleration data (Slepian (Par. 30, “system 100 may coordinate stimuli strike vs reflex time lag, for example. Sensor 104(1) captures a strike by stimulating device 106, sensor 104(2) captures the EMG response of the muscle, and the sensor 104(3) captures the movement of the limb…”) (Par. 33, “Sensors 104 may each include one or more accelerometers and gyroscopes for detecting movement, including rotation in the x, y, and z-axes, and may sense accelerative force (G-force) and angular velocity (degrees/second) of the limb/appendage of patient 102…” “…Each reflex produces a small lag time, or twitch interval, between the time of the initial stimulating device strike and the time the reflex occurred. This twitch interval may be quantitated (e.g., quantitatively defined/described) within both EMG signature 134 and motion signature 132…”)).
Naotaka further teaches force data (Naotaka (Par. 91, “The analysis process 10 is characterized by receiving impact force data from the force sensor 2d, acceleration data in the three directions…”) (Par. 104, “Transient response occurs when the impact force data is below a certain value…”) (Par. 105, “A steady-state response is observed when the impact force data exceeds a certain value…”)).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the method of Slepian, Naotaka, and Adekanmbi with that of Naotaka to include wherein the first acceleration data and force data of Naotaka is synchronized with the second acceleration data through the combination of references for the reasoning as indicated in claim 1 above and it would have yielded the predictable result determining the reflex response (Slepian (Par. 30)).
Regarding claim 5, modified Slepian further discloses further comprising displaying a representation of the patient response (Slepian (Par. 53, “In step 1522, method 1500 displays the quantitative evaluation. In one example of step 1522, reflex analyzer 130 displays quantitative evaluation 136 on display 150.”)).
Regarding claim 6, modified Slepian fails to explicitly disclose the limitations of the claim.
However, Slepian does disclose further comprising determining a magnitude and angle of approach to strike and magnitude and angle of strike rebound with respect to the patient tendon as a function of the first acceleration data (Slepian (Par. 33, “Sensors 104 may each include one or more accelerometers and gyroscopes for detecting movement, including rotation in the x, y, and z-axes, and may sense accelerative force (G-force) and angular velocity (degrees/second) of the limb/appendage of patient 102…”) (Par. 35, “stimulating device 106 may be automated such that computer 110 controls an actuator 107 that causes stimulating device 106 to create a reproducible force that's applied to the reflex of the patient 102…”) (Par. 53, “reflex analyzer 130 processes stimuli data 120 and motion data 124 to generate motion signature 132. In step 1520, method 1500 analyzes signatures to determine a quantitative evaluation of the reflex response.”)).
Naotaka further teaches force data (Naotaka (Par. 91, “The analysis process 10 is characterized by receiving impact force data from the force sensor 2d, acceleration data in the three directions…”) (Par. 104, “Transient response occurs when the impact force data is below a certain value…”) (Par. 105, “A steady-state response is observed when the impact force data exceeds a certain value…”)).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the method of Slepian, Naotaka, and Adekanmbi with that of Naotaka to include further comprising determining a magnitude and angle of approach to strike and magnitude and angle of strike rebound with respect to the patient tendon as a function of the first acceleration data and force data of Naotaka through the combination of references as it would have yielded the predictable result of providing additional detail regarding the patient’s response to the strike, the strike itself, and providing force information regarding the strike (Naotaka (Par. 104-105) (Par. 91)).
Regarding claim 7, modified Slepian fails to explicitly disclose the limitations of the claim.
However, Naotaka further teaches further comprising determining whether the strike was an indirect or glancing blow (Naotaka (Par. 126, “The waveform of the striking force data 15a tends to show a pointed shape when bone is struck by mistake. This allows us to check whether the striking force data 15a indicates a striking error, thereby improving the accuracy of the data.”)).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the method of Slepian, Naotaka, and Adekanmbi with that of Naotaka to include further comprising determining whether the strike was an indirect or glancing blow through the combination of references as it would have yielded the predictable result of determining whether there was a striking error (Naotaka (Par. 126)).
Regarding claim 9, modified Slepian further discloses wherein the second acceleration data is received via a wireless transmission (Slepian (Par. 30, “sensors 104(1), 104(2), and 104(3) transmit (wired and/or wirelessly) the captured data to sensor interface 116 and the data is stored in memory 114 as stimuli data 120, EMG data 122, and motion data 124, respectively”)).
Slepian fails to explicitly disclose wherein the first acceleration data and force data is synchronized with the second acceleration data.
However, Slepian does disclose wherein the first acceleration data is synchronized with the second acceleration data (Slepian (Par. 30, “system 100 may coordinate stimuli strike vs reflex time lag, for example. Sensor 104(1) captures a strike by stimulating device 106, sensor 104(2) captures the EMG response of the muscle, and the sensor 104(3) captures the movement of the limb…”) (Par. 33, “Sensors 104 may each include one or more accelerometers and gyroscopes for detecting movement, including rotation in the x, y, and z-axes, and may sense accelerative force (G-force) and angular velocity (degrees/second) of the limb/appendage of patient 102…” “…Each reflex produces a small lag time, or twitch interval, between the time of the initial stimulating device strike and the time the reflex occurred. This twitch interval may be quantitated (e.g., quantitatively defined/described) within both EMG signature 134 and motion signature 132…”)).
Naotaka further teaches force data (Naotaka (Par. 91, “The analysis process 10 is characterized by receiving impact force data from the force sensor 2d, acceleration data in the three directions…”) (Par. 104, “Transient response occurs when the impact force data is below a certain value…”) (Par. 105, “A steady-state response is observed when the impact force data exceeds a certain value…”)).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the method of Slepian, Naotaka, and Adekanmbi with that of Naotaka to include wherein the first acceleration data and force data of Naotaka is synchronized with the second acceleration data through the combination of references for the reasoning as indicated in claim 1 above and it would have yielded the predictable result determining the reflex response (Slepian (Par. 30)).
Regarding claim 10, modified Slepian fails to explicitly disclose determining a magnitude and angle of approach of the strike and magnitude and angle of a strike rebound with respect to the patient tendon as a function of the first acceleration data and force data.
However, Slepian does disclose determining a magnitude and angle of approach of the strike and magnitude and angle of a strike rebound with respect to the patient tendon as a function of the first acceleration data (Slepian (Par. 33, “Sensors 104 may each include one or more accelerometers and gyroscopes for detecting movement, including rotation in the x, y, and z-axes, and may sense accelerative force (G-force) and angular velocity (degrees/second) of the limb/appendage of patient 102…”) (Par. 35, “stimulating device 106 may be automated such that computer 110 controls an actuator 107 that causes stimulating device 106 to create a reproducible force that's applied to the reflex of the patient 102…”) (Par. 53, “reflex analyzer 130 processes stimuli data 120 and motion data 124 to generate motion signature 132. In step 1520, method 1500 analyzes signatures to determine a quantitative evaluation of the reflex response.”)).
Naotaka further teaches force data (Naotaka (Par. 91, “The analysis process 10 is characterized by receiving impact force data from the force sensor 2d, acceleration data in the three directions…”) (Par. 104, “Transient response occurs when the impact force data is below a certain value…”) (Par. 105, “A steady-state response is observed when the impact force data exceeds a certain value…”)).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the method of Slepian, Naotaka, and Adekanmbi with that of Naotaka to include determining a magnitude and angle of approach of the strike and magnitude and angle of a strike rebound with respect to the patient tendon as a function of the first acceleration data and force data of Naotaka through the combination of references as it would have yielded the predictable result of providing additional detail regarding the patient’s response to the strike, the strike itself, and providing force information regarding the strike (Naotaka (Par. 104-105) (Par. 91)).
Modified Slepian fails to explicitly disclose determining whether the strike was an indirect or glancing blow.
However, Naotaka further teaches determining whether the strike was an indirect or glancing blow (Naotaka (Par. 126, “The waveform of the striking force data 15a tends to show a pointed shape when bone is struck by mistake. This allows us to check whether the striking force data 15a indicates a striking error, thereby improving the accuracy of the data.”)).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the method of Slepian, Naotaka, and Adekanmbi with that of Naotaka to include determining whether the strike was an indirect or glancing blow through the combination of references as it would have yielded the predictable result of determining whether there was a striking error (Naotaka (Par. 126)).
Claim(s) 11-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Slepian, and further in view of Yu (CN 106725625 A) hereinafter Yu and Adekanmbi.
Regarding claim 11, Slepian discloses A computer implemented method (Par. 30, “FIG. 1 shows one system 100 for digital reflex quantization and signature analysis. System 100 may be used for analyzing superficial reflexes for one or more of biceps, triceps, patellar and Achilles reflexes. System 100 includes a computer 110 with at least one processor 112 that is communicatively coupled with a memory 114 and a sensor interface 116…”)(Par. 34, “A reflex analyzer 130, implemented as machine readable instructions stored in memory 114 and executed by processor 112, processes stimuli data 120, EMG data 122, and motion data 124 to generate a motion signature 132 and an EMG signature 134, each having a time reference with respect to a time of stimulation determined from stimuli data 120…”) (Par. 50, “FIG. 15 is a flowchart illustrating one example method 1500 for digital reflex quantization and signature analysis. Method 1500 is for example implemented at least in part within sensors 104 and computer 110 of FIG. 1.”) comprising:
receiving first acceleration data from a first accelerometer coupled to a reflex hammer used to strike a patient tendon (Par. 52, “a doctor uses stimulating device 106 to strike a patellar tendon of patient 102”)(Par. 52, “In step 1510, method 1500 captures motion of the striking device. In one example of step 1510, computer 110 receives data from sensor 104(1) of captured motion of stimulating device 106…”) (Par. 33, “Sensors 104 may each include one or more accelerometers and gyroscopes for detecting movement, including rotation in the x, y, and z-axes, and may sense accelerative force (G-force) and angular velocity (degrees/second) of the limb/appendage of patient 102…”) (Par. 30, “As shown in FIG. 1, sensor 104(1) is applied to a stimulating device 106 (e.g., a reflex hammer) that is used to provide a stimulus or stimuli”);
receiving second acceleration data from a second accelerometer coupled to a limb of the patient in response to the strike of the patient tendon (Par. 52, “In step 1514, method 1500 captures movement data of the limb/appendage. In one example of step 1514, computer 110 receives data from sensor 104(3) and stores the data as motion data 124 within memory 114.”) (Par. 31, “sensor 104(3) is placed on the limb attached to or associated with the muscle. When the stimulating device 106 strikes the patellar tendon, the sensor 104(3) captures the movement of the distal leg as motion data 124, which is processed to generate motion signature 132 that provides an indication of the actuation of a proximate muscle in response to the physical stimuli…”); and
processing the received data to identify characteristics of the received data including one or more of time of strike (Par. 34, “A reflex analyzer 130, implemented as machine readable instructions stored in memory 114 and executed by processor 112, processes stimuli data 120, EMG data 122, and motion data 124 to generate a motion signature 132 and an EMG signature 134, each having a time reference with respect to a time of stimulation determined from stimuli data 120.”) (Par. 53, “step 1518, reflex analyzer 130 processes stimuli data 120 and motion data 124 to generate motion signature 132. In step 1520, method 1500 analyzes signatures to determine a quantitative evaluation of the reflex response…”) (Par. 34, “A reflex analyzer 130, implemented as machine readable instructions stored in memory 114 and executed by processor 112, processes stimuli data 120, EMG data 122, and motion data 124 to generate a motion signature 132…”), time of movement of the limb, time of reflex, peak response time, response decay, and impact force.
Slepian fails to explicitly disclose receiving first pressure data from a pressure sensor coupled to a bumper of the reflex hammer used to strike the patient tendon.
Slepian does disclose the reflex hammer used to strike the patient tendon (Par. 30, “As shown in FIG. 1, sensor 104(1) is applied to a stimulating device 106 (e.g., a reflex hammer) that is used to provide a stimulus or stimuli”).
However, Yu teaches receiving first pressure data from a pressure sensor coupled to a bumper of the reflex hammer used to strike the patient (Par. 27, “During detection, the end with the hammer head 3 is used to strike the patient's lesion. Based on the patient's reaction, the pressure sensor 4 controls the display screen 5 to display data”) (Par. 26, “A pressure sensor 4 is located between the hammer head 3 and the end of the hammer body 2”).
Adekanmbi teaches a plurality of sensors in a hammer (Par. 79 and 123)(Fig. 7B (hammer – 10, force sensor – 40, IMU - 30)).
Slepian, Yu, and Adekanmbi are considered to be analogous art to the claimed invention as they are involved with hammers with sensors.
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the method of Slepian with that of Yu and Adekanmbi to include receiving first pressure data from a pressure sensor coupled to a bumper of the reflex hammer of Slepian used to strike the patient tendon of Slepian through the combination of references as using more than one sensor type in a hammer is a known variation in the art (Adekanmbi (Par. 79 and 123)) and as it would have yielded the predictable result of enabling the evaluation of pressure data over time and monitor the applied pressure (Yu (Par. 27)).
Slepian fails to explicitly disclose correlating the characteristics of the received data to medically characterize a patient condition.
However, Slepian does teach in an example correlating the characteristics of the received data to medically characterize a patient condition (Par. 38, “Signatures 132 and 134 generated by reflex analyzer 130 allow detection of reflex abnormalities, and quantitative evaluation 136 indicates changes in reflex response, for example as compared to previously captured signatures of patient 102, or as compared to standardized signatures. Such changes in reflex response may predate overt symptoms and signs of a given disorder. For example, previously captured signatures of patient 102 and/or standardized signatures may be stored within a signature database 140 to allow comparison by reflex analyzer 130. In another example, database 140 may store signatures of healthy reflex responses and signatures of reflex responses corresponding to different diseases and/or disorders. Accordingly, through use of system 100, signatures 132, 134 may be matched to reference signatures to diagnose the disorder significantly earlier for patient 102 as compared to conventional subjective reflex evaluations.”).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the method of Slepian, Yu, and Adekanmbi with that of Slepian to include correlating the characteristics of the received data to medically characterize a patient condition through the combination of examples as it would have yielded the predictable result of detecting the early onset of a disorder (Slepian (Par. 38)).
Regarding claim 12, modified Slepian further discloses wherein the processing the received data identifies the time of strike of the patient tendon based on a beginning of sudden deceleration of the first acceleration data or beginning of a sudden increase in pressure of the first pressure data and identifies the time of movement of the limb as the time the second acceleration data shows movement of the limb by (Slepian (Par. 37, “Sensors 104(1)-(3) and corresponding data 120, 122, 124 allow quantitation of movement (dimension data), acceleration, muscle contraction and timing of reflex responses. Motion signature 132 and EMG signature 134, generated readily, allow quantitation of component elements and intervals associated with these signals.”) (Par. 30, “system 100 may coordinate stimuli strike vs reflex time lag, for example. Sensor 104(1) captures a strike by stimulating device 106, sensor 104(2) captures the EMG response of the muscle, and the sensor 104(3) captures the movement of the limb…”) (Par. 31, “sensor 104(3) is placed on the limb attached to or associated with the muscle. When the stimulating device 106 strikes the patellar tendon...”) (Par. 33, “Sensors 104 may each include one or more accelerometers and gyroscopes for detecting movement, including rotation in the x, y, and z-axes, and may sense accelerative force (G-force) and angular velocity (degrees/second) of the limb/appendage of patient 102…” “…Each reflex produces a small lag time, or twitch interval, between the time of the initial stimulating device strike and the time the reflex occurred. This twitch interval may be quantitated (e.g., quantitatively defined/described) within both EMG signature 134 and motion signature 132…”)(Par. 53, “In step 1518, method 1500 generates a motion signature using striking device motion and movement data. In one example of step 1518, reflex analyzer 130 processes stimuli data 120 and motion data 124 to generate motion signature 132. In step 1520, method 1500 analyzes signatures to determine a quantitative evaluation of the reflex response”)).
Regarding claim 13, modified Slepian further discloses wherein processing the received data identifies the time of reflex by subtracting time of impact from time the second acceleration data shows movement of the limb and wherein the peak response time from a largest acceleration value in the second acceleration data (Slepian (Par. 30, “By capturing both stimuli (sensor 104(1)) and response (sensors 104(2) and 104(3)), system 100 may coordinate stimuli strike vs reflex time lag, for example. Sensor 104(1) captures a strike by stimulating device 106, sensor 104(2) captures the EMG response of the muscle, and the sensor 104(3) captures the movement of the limb/appendage caused by the muscle…”)(Par. 31, “sensor 104(3) is placed on the limb attached to or associated with the muscle. When the stimulating device 106 strikes the patellar tendon...”) (Par. 33, “Sensors 104 may each include one or more accelerometers and gyroscopes for detecting movement, including rotation in the x, y, and z-axes, and may sense accelerative force (G-force) and angular velocity (degrees/second) of the limb/appendage of patient 102…” “…Each reflex produces a small lag time, or twitch interval, between the time of the initial stimulating device strike and the time the reflex occurred. This twitch interval may be quantitated (e.g., quantitatively defined/described) within both EMG signature 134 and motion signature 132…”)(Par. 53, “In step 1518, method 1500 generates a motion signature using striking device motion and movement data. In one example of step 1518, reflex analyzer 130 processes stimuli data 120 and motion data 124 to generate motion signature 132. In step 1520, method 1500 analyzes signatures to determine a quantitative evaluation of the reflex response”)(Par. 48, “Reflex analyzer 130 may also determine time parameters from the data 120, 122, and 124 and/or the signatures 132 and 134 signals, such as the time or the lag from initiation of muscle strike to movement, that's a variable. Reflex analyzer 130 may determine the peak width and height of the movement signal”)).
Regarding claim 14, modified Slepian fails to explicitly disclose the limitations of the claim.
However, Slepian does teach in an example wherein correlating the characteristics of the received data comprises comparing the characteristics of the received data to a chart having labels of different patient conditions correlated to prior collected data from multiple different patients (Slepian (Par. 38, “Signatures 132 and 134 generated by reflex analyzer 130 allow detection of reflex abnormalities, and quantitative evaluation 136 indicates changes in reflex response, for example as compared to previously captured signatures of patient 102, or as compared to standardized signatures. Such changes in reflex response may predate overt symptoms and signs of a given disorder. For example, previously captured signatures of patient 102 and/or standardized signatures may be stored within a signature database 140 to allow comparison by reflex analyzer 130. In another example, database 140 may store signatures of healthy reflex responses and signatures of reflex responses corresponding to different diseases and/or disorders. Accordingly, through use of system 100, signatures 132, 134 may be matched to reference signatures to diagnose the disorder significantly earlier for patient 102 as compared to conventional subjective reflex evaluations.”).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the method of Slepian, Yu, and Adekanmbi with that of Slepian to include wherein correlating the characteristics of the received data comprises comparing the characteristics of the received data to a chart having labels of different patient conditions correlated to prior collected data from multiple different patients for the reasoning as indicated in claim 11 above.
Regarding claim 15, modified Slepian fails to explicitly disclose the limitations of the claim.
However, Slepian does teach wherein the chart is based on a National Institute of Neurological Disorders and Strok (NINDS) scale for tendon reflex assessment containing the labels (Slepian (Par. 2, The traditional means of assessing reflexes is to use a hammer, typically either of round hammer or tomahawk shape, where a tap or strike of a defined force is made on the tendon and reflex responses are typically graded using a zero through five scale, with 0: absent reflex; 1+: trace, or seen only with reinforcement; 2+: normal; 3+: brisk; 4+: non-sustained clonus; and 5+: sustained clonus…”) (Par. 38, “Signatures 132 and 134 generated by reflex analyzer 130 allow detection of reflex abnormalities, and quantitative evaluation 136 indicates changes in reflex response, for example as compared to previously captured signatures of patient 102, or as compared to standardized signatures. Such changes in reflex response may predate overt symptoms and signs of a given disorder. For example, previously captured signatures of patient 102 and/or standardized signatures may be stored within a signature database 140 to allow comparison by reflex analyzer 130. In another example, database 140 may store signatures of healthy reflex responses and signatures of reflex responses corresponding to different diseases and/or disorders. Accordingly, through use of system 100, signatures 132, 134 may be matched to reference signatures to diagnose the disorder significantly earlier for patient 102 as compared to conventional subjective reflex evaluations. Sensors 104 are each conformal, adherent, wearable sensing electronics that readily allow capture of both motion (displacement, velocity and acceleration) as well as EMG signals of deep tendon reflexes.”)).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the method of Slepian, Yu, and Adekanmbi with that of Slepian to include wherein the chart is based on a National Institute of Neurological Disorders and Strok (NINDS) scale for tendon reflex assessment containing the labels through the combination of examples as it would have yielded the predictable result of quantifying the reflex to a set standard (Slepian (Par. 2)) and as it would have yielded the predictable result of detecting the early onset of a disorder (Slepian (Par. 38)).
Regarding claim 16, modified Slepian fails to explicitly disclose the limitations of the claim.
However, Slepian does teach in an example wherein correlating the characteristics of the received data comprises:
providing the characteristics to a trained machine learning model trained on sets of patient condition labeled characteristics of prior collected data on multiple patients (Slepian (Par. 38, “Signatures 132 and 134 generated by reflex analyzer 130 allow detection of reflex abnormalities, and quantitative evaluation 136 indicates changes in reflex response, for example as compared to previously captured signatures of patient 102, or as compared to standardized signatures…”)(Par. 39, “The above signature, stored in a database may be systematically analyzed and refined over time to further elicit more characteristic signatures of a given state, condition, disease or pharmacologically induced state. This may be accomplished through repetitive processing of inputted signature data, using machine learning, big data technique…”)) (Slepian (Par. 40, “The signature data—both individual and refined via machine learning may be integrated into the electronic health record—such as EPIC or Cerner.”)); and
receiving an output of the trained machine learning model comprising the patient condition (Slepian (Par. 40, “The signature data—both individual and refined via machine learning may be integrated into the electronic health record—such as EPIC or Cerner.”)) (Slepian (Par. 38, “Signatures 132 and 134 generated by reflex analyzer 130 allow detection of reflex abnormalities, and quantitative evaluation 136 indicates changes in reflex response, for example as compared to previously captured signatures of patient 102, or as compared to standardized signatures…”)(Par. 39, “The above signature, stored in a database may be systematically analyzed and refined over time to further elicit more characteristic signatures of a given state, condition, disease or pharmacologically induced state. This may be accomplished through repetitive processing of inputted signature data, using machine learning, big data technique…”)).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the method of Slepian, Yu, and Adekanmbi with that of Slepian to include wherein correlating the characteristics of the received data comprises: providing the characteristics to a trained machine learning model trained on sets of patient condition labeled characteristics of prior collected data on multiple patients; and receiving an output of the trained machine learning model comprising the patient condition through the combination of examples as it would have yielded the predictable result of improving the function of the analyzer (Slepian (Par. 38-39)) and as it would have yielded the predictable result of detecting the early onset of a disorder (Slepian (Par. 38)).
Regarding claim 17, modified Slepian fails to explicitly disclose the limitations of the claim.
However, Adekanmbi teaches comparing the data to a low impact force threshold (Adekanmbi (Par. 71-73 (IMU produces signals indicative of the hammer))(Par. 76 (red color with off-axis strike and green with an on-axis strike))); and in response to the data being lower than the impact force threshold, characterizing the strike as not a valid strike (Adekanmbi (Par. 71-73 (IMU produces signals indicative of the hammer))(Par. 76 (red color with off-axis strike and green with an on-axis strike))) and further teaches impact force thresholds (Adekanmbi (Par. 7, “a first light color may correspond to an impact force below a first threshold, a second light color may correspond to an impact force above a second threshold, and a third light color may correspond to an impact force between the thresholds. The impact force may be detected using a sensor, which may output a voltage to cause the illumination”)).
Yu teaches pressure data (Yu (Par. 27, “During detection, the end with the hammer head 3 is used to strike the patient's lesion. Based on the patient's reaction, the pressure sensor 4 controls the display screen 5 to display data”) (Par. 26, “A pressure sensor 4 is located between the hammer head 3 and the end of the hammer body 2”)).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the method of Slepian, Yu, and Adekanmbi with that of Adekanmbi to include comparing the first pressure data of Yu to a low impact force threshold; and in response to the first pressure data of Yu being lower than the impact force threshold, characterizing the strike as not a valid strike through the combination of references as it would have yielded the predictable result of providing feedback regarding the accuracy of the strikes (Adekanmbi (Par. 76)).
Regarding claim 18, modified Slepian further discloses wherein processing the data comprises determining a time of strike of the tendon based on the acceleration data collected from the first accelerometer (Slepian (Par. 30, “system 100 may coordinate stimuli strike vs reflex time lag, for example. Sensor 104(1) captures a strike by stimulating device 106, sensor 104(2) captures the EMG response of the muscle, and the sensor 104(3) captures the movement of the limb…”));
determining a time of patient response based on the acceleration data collected from the second accelerometer (Slepian (Par. 30, “system 100 may coordinate stimuli strike vs reflex time lag, for example. Sensor 104(1) captures a strike by stimulating device 106, sensor 104(2) captures the EMG response of the muscle, and the sensor 104(3) captures the movement of the limb…”) (Par. 31, “sensor 104(3) is placed on the limb attached to or associated with the muscle. When the stimulating device 106 strikes the patellar tendon, the sensor 104(3) captures the movement of the distal leg as motion data 124, which is processed to generate motion signature 132 that provides an indication of the actuation of a proximate muscle in response to the physical stimuli.”));
determining a delay of response from the first and second acceleration data (Slepian (Par. 30, “system 100 may coordinate stimuli strike vs reflex time lag, for example. Sensor 104(1) captures a strike by stimulating device 106, sensor 104(2) captures the EMG response of the muscle, and the sensor 104(3) captures the movement of the limb…”) (Par. 33, “Sensors 104 may each include one or more accelerometers and gyroscopes for detecting movement, including rotation in the x, y, and z-axes, and may sense accelerative force (G-force) and angular velocity (degrees/second) of the limb/appendage of patient 102…” “…Each reflex produces a small lag time, or twitch interval, between the time of the initial stimulating device strike and the time the reflex occurred. This twitch interval may be quantitated (e.g., quantitatively defined/described) within both EMG signature 134 and motion signature 132…”)).
Regarding claim 19, modified Slepian further discloses wherein the second acceleration data is received via a wireless transmission (Slepian (Par. 30, “sensors 104(1), 104(2), and 104(3) transmit (wired and/or wirelessly) the captured data to sensor interface 116 and the data is stored in memory 114 as stimuli data 120, EMG data 122, and motion data 124, respectively”)).
Slepian fails to explicitly disclose wherein the first acceleration data and force data is synchronized with the second acceleration data.
However, Slepian does disclose wherein the first acceleration data is synchronized with the second acceleration data (Slepian (Par. 30, “system 100 may coordinate stimuli strike vs reflex time lag, for example. Sensor 104(1) captures a strike by stimulating device 106, sensor 104(2) captures the EMG response of the muscle, and the sensor 104(3) captures the movement of the limb…”) (Par. 33, “Sensors 104 may each include one or more accelerometers and gyroscopes for detecting movement, including rotation in the x, y, and z-axes, and may sense accelerative force (G-force) and angular velocity (degrees/second) of the limb/appendage of patient 102…” “…Each reflex produces a small lag time, or twitch interval, between the time of the initial stimulating device strike and the time the reflex occurred. This twitch interval may be quantitated (e.g., quantitatively defined/described) within both EMG signature 134 and motion signature 132…”)).
Yu further teaches pressure data (Yu (Par. 27, “During detection, the end with the hammer head 3 is used to strike the patient's lesion. Based on the patient's reaction, the pressure sensor 4 controls the display screen 5 to display data”) (Par. 26, “A pressure sensor 4 is located between the hammer head 3 and the end of the hammer body 2”)).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the method of Slepian, Yu, and Adekanmbi with that of Yu to include wherein the first acceleration data and force data of Yu is synchronized with the second acceleration data through the combination of references for the reasoning as indicated in claim 1 above and it would have yielded the predictable result determining the reflex response (Slepian (Par. 30)).
Regarding claim 20, modified Slepian further discloses further comprising displaying a representation of the patient condition (Slepian (Par. 53, “In step 1522, method 1500 displays the quantitative evaluation. In one example of step 1522, reflex analyzer 130 displays quantitative evaluation 136 on display 150.”)).
Regarding claim 21, modified Slepian fails to explicitly disclose determining a magnitude and angle of approach to strike and magnitude and angle of strike rebound with respect to the patient tendon as a function of the first acceleration data and force data.
However, Slepian does disclose determining a magnitude and angle of approach to strike and magnitude and angle of strike rebound with respect to the patient tendon as a function of the first acceleration data (Slepian (Par. 33, “Sensors 104 may each include one or more accelerometers and gyroscopes for detecting movement, including rotation in the x, y, and z-axes, and may sense accelerative force (G-force) and angular velocity (degrees/second) of the limb/appendage of patient 102…”) (Par. 35, “stimulating device 106 may be automated such that computer 110 controls an actuator 107 that causes stimulating device 106 to create a reproducible force that's applied to the reflex of the patient 102…”) (Par. 53, “reflex analyzer 130 processes stimuli data 120 and motion data 124 to generate motion signature 132. In step 1520, method 1500 analyzes signatures to determine a quantitative evaluation of the reflex response.”)).
Yu further teaches pressure data (Yu (Par. 27, “During detection, the end with the hammer head 3 is used to strike the patient's lesion. Based on the patient's reaction, the pressure sensor 4 controls the display screen 5 to display data”) (Par. 26, “A pressure sensor 4 is located between the hammer head 3 and the end of the hammer body 2”)).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the method of Slepian, Yu, and Adekanmbi with that of Yu to include determining a magnitude and angle of approach to strike and magnitude and angle of strike rebound with respect to the patient tendon as a function of the first acceleration data and force data of Yu though the combination of references as it would have yielded the predictable result of providing additional information regarding the pressure (Yu (Par. 27)).
Modified Slepian fails to explicitly disclose determining whether the strike was an indirect or glancing blow and not a valid strike.
However, Adekanmbi further teaches determining whether the strike was an indirect or glancing blow and not a valid strike (Adekanmbi (Par. 71-73 (IMU produces signals indicative of the hammer))(Par. 76 (red color with off-axis strike and green with an on-axis strike))).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify the method of Slepian, Yu, and Adekanmbi with that of Adekanmbi to include determining whether the strike was an indirect or glancing blow and not a valid strike through the combination of references as it would have yielded the predictable result of providing feedback regarding the accuracy of the strikes (Adekanmbi (Par. 76)).
Conclusion
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/ARI S PADDA/Examiner, Art Unit 3791
/JASON M SIMS/Supervisory Patent Examiner, Art Unit 3791
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