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 .
Status of Claims
2. Claims 21-40 are pending and currently under consideration for patentability under 37 CFR 1.104.
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
In the instant case, lines 1-2 state “In an embodiment, a wearable device for vibratory stimulation is presented. The wearable device includes a sensor…” which includes language that can be implied. The Examiner suggests --A wearable device for vibratory stimulation, including a sensor…--.
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.
Claim Objections
Claims 21, 29, 31, and 39 are objected to because of the following informalities:
Claim 21, lines 10-11 recite “wherein waveform parameter selection system” and the Examiner suggests --wherein the waveform parameter selection system-- to clarify the antecedent basis.
Claim 21, lines 12-13 recite “new set of stimulation parameters of the stimulation” and the Examiner suggests deleting the phrase “of the stimulation” because it is redundant and also lacks antecedent basis.
Claim 29, line 1 recites “the each waveform output” and the Examiner suggests --each waveform output--.
Claim 31, line 6 recites “the waveform parameter systems” and the Examiner suggests --the waveform parameter system--.
Claim 31, lines 7-8 recite “new set of stimulation parameters of the stimulation” and the Examiner suggests deleting the phrase “of the stimulation” because it is redundant and also lacks antecedent basis.
Claim 31, the last two lines recite “the processing unit is limits” and the Examiner suggests --the processing unit limits--.
Claim 39, line 1 recites “the each waveform output” and the Examiner suggests --each waveform output--.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
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 22, 24-25, 32, and 34-35 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 22, line 2 recites “the user” which lacks antecedent basis. The Examiner suggests --the subject--.
Claim 24, line 2 recites “the user” which lacks antecedent basis. The Examiner suggests --the subject--.
Claim 25, line 2 recites an “argmax(FFT) process” which is indefinite because it is unclear what type of processes would be considered an “argmax(fft)” process. The process is not described in the specification with any clarity. Additionally, the acronym should be written out in full form.
Claim 32, line 2 recites “the user” which lacks antecedent basis. The Examiner suggests --the subject--.
Claim 34, line 2 recites “the user” which lacks antecedent basis. The Examiner suggests --the subject--.
Claim 35, line 2 recites an “argmax(FFT) process” which is indefinite because it is unclear what type of processes would be considered an “argmax(fft)” process. The process is not described in the specification with any clarity. Additionally, the acronym should be written out in full form.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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) 21-24, 27-28, 30-34, 37-38, and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Moaddeb et al. (2021/0330547) in view of Rosenbluth et al. (2019/0001129).
Regarding claim 21, Moaddeb discloses a wearable device for mitigating a movement disorder of a subject (wearable tremor control system 100, Fig. 8), the device comprising:
a sensor (sensing elements 132, 134, Fig. 8; the sensor may comprise an accelerometer or gyroscope, see para. [0036]) configured to be attached to the subject (attached to the subject’s wrist via band 104, Fig. 8) and to provide a sensor output (“signals are received from one or more of the sensing elements 132, 134 that are in a range that is indicative to active tremor” see para. [0040]);
a processing unit (circuit board 190 with controller 192, Fig. 15) operationally coupled to the sensor and configured to receive the sensor output (“controller 192 … may be configured (via software or firmware) to receive one or more signals from the sensing elements 132, 134, and to automatically adjust the vibration mode, either turning it on or off, or adjusting it between low, medium, and high vibration” see [0047]); and
an electric transducer (the vibration element(s) 136, 138, Fig. 15) configured to be attached to the subject (vibration element(s) 136, 138, are disposed within the band 104, see Fig. 8, Fig. 15) and operationally coupled to the processing unit (“The vibration mode in some embodiments may be automatically adjustable, via servo control or other methods, such that the vibration elements 136, 138 are caused to activate in a manner which is proportional to or matches in some way the reduction or increase in amplitude, intensity and/or prevalence of tremor” see the last nine lines of [0047]), wherein the electric transducer (136, 138) is configured to provide stimulation to proprioceptive nerves of the subject (configured to apply vibrations to the wrist/forearm area of the user as seen in Fig. 5. There are a plurality of proprioceptive nerves in this area) through a waveform output (vibration output);
wherein the processing unit (circuit board 190 with controller 192, Fig. 15) is further configured, as part of a waveform parameter selection system (vibration servo control), to adjust parameters of the waveform output (such as the amplitude of the vibration waveform), wherein waveform parameter selection system receives the sensor output and determines a new set of stimulation parameters of the stimulation based on the sensor output (“The vibration mode in some embodiments may be automatically adjustable, via servo control or other methods, such that the vibration elements 136, 138 are caused to activate in a manner which is proportional to or matches in some way the reduction or increase in amplitude, intensity and/or prevalence of tremor” see the last nine lines of [0047]).
Moaddeb is silent regarding wherein the waveform parameter selection system receives current stimulation parameters of the waveform output and bases the new set of stimulation parameters on the current stimulation parameters; and wherein the processing unit is further configured to limit sensing of the sensor to off phases of the stimulation.
Rosenbluth teaches a related multi-modal stimulation device for treating tremor (Fig. 1, see Title) including a processing unit (processor 797, Fig. 7A), a sensor (sensor 780, Fig. 7A) and a stimulator (effector 730, Fig. 7A; “the ‘effector’ may be electrical stimulation of the nerve or mechanical stimulation of proprioceptors” see the last sentence of [0107]). The processing unit includes a waveform parameter selection system (parameter optimization algorithm in Figure 22, see para. [0220]) that receives sensor output (“sensor detects 2202 and records tremor characteristics, including tremor amplitude, frequency, phase, and other characteristics described herein” see the second sentence of [0221] and Fig. 22) and current stimulation parameters of the waveform output (parameters 2200, Fig. 22) and determines a new set of stimulation parameters (“new stimulation parameters are set 2224” see the penultimate sentence of [0221] and see Fig. 22) based on the sensor output and the current stimulation parameters (2200, 2202, Fig. 22). The waveform parameter selection system is configured to optimize the stimulation parameters to “achieve the greatest tremor reduction with the most comfort in each patient” (see para. [0220], the first sentence of [0221], and Fig. 22). The processing unit limits sensing of the sensor to off phases of the stimulation (see para. [0221] and the flowchart of Figure 22. The sensing is limited to step 2202: “sensor detects 2202 and records tremor characteristics including tremor amplitude, frequency, phase.” Stimulation is only applied if the detected tremor characteristics exceed the target tremor characteristics, “if a predetermined target tremor condition is exceeded, then stimulation can be turned on 2210” see steps 2206, 2208, 2210. “Once the stimulation has exceeded the set on-time duration 2212, then the stimulation is turned off 2214, and the algorithm proceeds back to the detection step 2202.” Thus, the sensing step 2202 is limited to the off phases of the stimulation, as the initial sensing step 2202 is performed prior to turning stimulation on at 2210, and subsequent sensing steps 2202 are performed after stimulation is turned off at 2214). This provides an expected result that the tremor amplitude, frequency, etc., can be detected before and after each stimulation to help determine which particular stimulation parameters produce the greatest tremor reduction.
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the processing unit and waveform parameter selection system of Moaddeb to include a parameter optimizing algorithm that receives the sensor output and current stimulation parameters to determine a new set of stimulation parameters, and the processing unit limits sensing of the sensor to off phases of the stimulation as taught by Rosenbluth so the stimulation parameters can be detected before and after each stimulation to help determine which particular stimulation parameters produce the greatest tremor reduction.
Regarding claim 22, the modified Moaddeb/Rosenbluth device discloses wherein the sensor (sensing elements 132, 134, Fig. 8 of Moaddeb, as modified by Rosenbluth) is configured to detect a symptom severity of the user (the sensor is able to detect tremor amplitude, intensity, see the last two sentences of [0047] of Moaddeb. See also the second sentence of [0221] of Rosenbluth. The amplitude or intensity of the tremor reads on a severity of the tremor).
Regarding claim 23, the modified Moaddeb/Rosenbluth device as currently combined is silent regarding wherein the processing unit is further configured to generate a multichannel waveform by generating multiple waveform parameters for multiple proprioceptive channels.
However, Rosenbluth additionally teaches that its tremor reduction processing unit may generate a multichannel waveform by generating multiple waveform parameters for multiple proprioceptive channels (at least two waveform parameters are generated to stimulate two proprioceptive channels, “a first stimulation actuator configured to apply a first stimulation mode to a first peripheral nerve (e.g., proprioceptor, afferent, A-fiber, B-fiber, C-fiber, etc.), a second stimulation actuator configured to apply a second stimulation mode to a second peripheral nerve (e.g., proprioceptor, afferent, A-fiber, B-fiber, C-fiber, etc.)” see para. [0040]; “The second stimulus may be the same as the first stimulus, but with different parameters (e.g., different amplitudes, duration, frequency, etc.).” See the last two sentences of [0021], and para. [0048]). For example, “the first stimulation actuator may be configured to be positioned on a wrist of the subject. The second stimulation actuator may be configured to be positioned on a finger of the subject … [or] on an ankle of the subject” (see para. [0046]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the processing unit of Moaddeb/Rosenbluth to be further configured to generate a multichannel waveform by generating multiple waveform parameters for multiple proprioceptive channels as taught by Rosenbluth so the tremor reduction stimulation can be applied to a plurality of peripheral nerves, if desired.
Regarding claim 24, the modified Moaddeb/Rosenbluth device discloses wherein the electric transducer (the vibration element(s) 136, 138, Fig. 15 of Moaddeb) is housed within a band (band 104, Fig. 8 of Moaddeb) of the wearable device (wearable tremor control system 100, Fig. 8), the band configured to attach the wearable device to the user (“To attach the wearable tremor control system 100 to the user's wrist 38, the user 42 (or a person aiding the user 42) slips a first end 114 of the band 104 through an opening 116 of the loop 106 and, while applying traction on the first end 114, pulls one or more of the wedges 110 through the opening 116 of the loop 106, until the band 104 is at a comfortable tightness around the user's wrist 38” see para. [0038] and Fig. 5).
Regarding claim 27, the modified Moaddeb/Rosenbluth device discloses wherein the waveform parameter system compares a tremor amplitude with the current stimulation parameters to a tremor amplitude (tremor amplitude is detected at 2202, Fig. 22 of Rosenbluth) observed with a previous set of waveform parameters (the parameters set at step 2200, Fig. 22 of Rosenbluth) to determine which waveform parameters results in a lowest tremor amplitude (see the parameter optimization algorithm of Figure 22 of Rosenbluth. “The data can be processed by a controller 2222 which can optimize the stimulation parameters using various algorithms, including machine learning algorithms. Once the parameters are optimized, the new stimulation parameters are set 2224” see the last three sentences of [0221] of Rosenbluth. This parameter optimization algorithm is performed to “achieve the greatest tremor reduction with the most comfort in each patient” (see para. [0220] of Rosenbluth).
Regarding claim 28, the modified Moaddeb/Rosenbluth device discloses wherein the processing unit is further configured to generate a train of waveform outputs (a series of impulses may be applied, see the last two sentences of [0066] of Moaddeb, and the “control unit is configured to repeat the activation cycle one or more times.” See para. [0068] of Moaddeb).
Regarding claim 30, the modified Moaddeb/Rosenbluth device discloses wherein the processing unit (circuit board 190 with controller 192, Fig. 15 of Moaddeb) is further configured to: receive a selection of a waveform parameter from a user through a user interface (user interface 101, Fig. 8 of Moaddeb) and implement the selected waveform parameter in the waveform output (“controller 192 may be configured to allow the user/patient to control some or all of these parameter adjustments, for example, via the user interface 101” see [0059] of Moaddeb. Adjusting the parameters will implement the selected parameters in the waveform output).
Regarding claim 31, Moaddeb discloses a method for mitigating a movement disorder of a subject using a wearable device (the user uses the wearable tremor control system 100, Fig. 8), comprising:
receiving sensor output (“signals are received from one or more of the sensing elements 132, 134 that are in a range that is indicative to active tremor” see para. [0040]) from a sensor (sensing elements 132, 134, Fig. 8) of the wearable device (100, Fig. 8);
generating, by a processing unit (circuit board 190 with controller 192, Fig. 15) operationally coupled to the sensor (“controller 192 … may be configured (via software or firmware) to receive one or more signals from the sensing elements 132, 134, and to automatically adjust the vibration mode, either turning it on or off, or adjusting it between low, medium, and high vibration” see [0047]), a waveform output (vibration output);
adjusting, as part of a waveform parameter selection system (vibration servo control), parameters of the waveform output (such as the amplitude of the vibration waveform), wherein the waveform parameter selection systems receives the sensor output and determines a new set of stimulation parameters of the stimulation based on the sensor output (“The vibration mode in some embodiments may be automatically adjustable, via servo control or other methods, such that the vibration elements 136, 138 are caused to activate in a manner which is proportional to or matches in some way the reduction or increase in amplitude, intensity and/or prevalence of tremor” see the last nine lines of [0047]); and
applying the waveform output (vibration output) to a body part of a patient (applied to the wrist 38, arm 40, Fig. 5, via vibration element(s) 136, 138, Fig. 15).
Moaddeb is silent regarding wherein the waveform parameter selection system receives current stimulation parameters of the waveform output and bases the new set of stimulation parameters on the current stimulation parameters; and wherein the processing unit [is] limits sensing of the sensor to off phases of the waveform output.
Rosenbluth teaches a related multi-modal stimulation device for treating tremor (Fig. 1, see Title) including a processing unit (processor 797, Fig. 7A), a sensor (sensor 780, Fig. 7A) and a stimulator (effector 730, Fig. 7A; “the ‘effector’ may be electrical stimulation of the nerve or mechanical stimulation of proprioceptors” see the last sentence of [0107]). The processing unit includes a waveform parameter selection system (parameter optimization algorithm in Figure 22, see para. [0220]) that receives sensor output (“sensor detects 2202 and records tremor characteristics, including tremor amplitude, frequency, phase, and other characteristics described herein” see the second sentence of [0221] and Fig. 22) and current stimulation parameters of the waveform output (parameters 2200, Fig. 22) and determines a new set of stimulation parameters (“new stimulation parameters are set 2224” see the penultimate sentence of [0221] and see Fig. 22) based on the sensor output and the current stimulation parameters (2200, 2202, Fig. 22). The waveform parameter selection system is configured to optimize the stimulation parameters to “achieve the greatest tremor reduction with the most comfort in each patient” (see para. [0220], the first sentence of [0221], and Fig. 22). The processing unit limits sensing of the sensor to off phases of the stimulation (see para. [0221] and the flowchart of Figure 22. The sensing is limited to step 2202: “sensor detects 2202 and records tremor characteristics including tremor amplitude, frequency, phase.” Stimulation is only applied if the detected tremor characteristics exceed the target tremor characteristics, “if a predetermined target tremor condition is exceeded, then stimulation can be turned on 2210” see steps 2206, 2208, 2210. “Once the stimulation has exceeded the set on-time duration 2212, then the stimulation is turned off 2214, and the algorithm proceeds back to the detection step 2202.” Thus, the sensing step 2202 is limited to the off phases of the stimulation, as the initial sensing step 2202 is performed prior to turning stimulation on at 2210, and subsequent sensing steps 2202 are performed after stimulation is turned off at 2214). This provides an expected result that the tremor amplitude, frequency, etc., can be detected before and after each stimulation to help determine which particular stimulation parameters produce the greatest tremor reduction.
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the processing unit and waveform parameter selection system of Moaddeb to include a parameter optimizing algorithm that receives the sensor output and current stimulation parameters to determine a new set of stimulation parameters, and the processing unit limits sensing of the sensor to off phases of the stimulation as taught by Rosenbluth so the stimulation parameters can be detected before and after each stimulation to help determine which particular stimulation parameters produce the greatest tremor reduction.
Regarding claim 32, the modified Moaddeb/Rosenbluth method discloses further comprising detecting, by the sensor (sensing elements 132, 134, Fig. 8 of Moaddeb, as modified by Rosenbluth), a symptom severity of the user (the sensor is able to detect tremor amplitude, intensity, see the last two sentences of [0047] of Moaddeb. See also the second sentence of [0221] of Rosenbluth. The amplitude or intensity of the tremor reads on a severity of the tremor).
Regarding claim 33, the modified Moaddeb/Rosenbluth method as currently combined is silent regarding further comprising generating, by the processing unit, a multichannel waveform by generating multiple waveform parameters for multiple proprioceptive channels.
However, Rosenbluth additionally teaches that its tremor reduction processing unit may generate a multichannel waveform by generating multiple waveform parameters for multiple proprioceptive channels (at least two waveform parameters are generated to stimulate two proprioceptive channels, “a first stimulation actuator configured to apply a first stimulation mode to a first peripheral nerve (e.g., proprioceptor, afferent, A-fiber, B-fiber, C-fiber, etc.), a second stimulation actuator configured to apply a second stimulation mode to a second peripheral nerve (e.g., proprioceptor, afferent, A-fiber, B-fiber, C-fiber, etc.)” see para. [0040]; “The second stimulus may be the same as the first stimulus, but with different parameters (e.g., different amplitudes, duration, frequency, etc.).” See the last two sentences of [0021], and para. [0048]). For example, “the first stimulation actuator may be configured to be positioned on a wrist of the subject. The second stimulation actuator may be configured to be positioned on a finger of the subject … [or] on an ankle of the subject” (see para. [0046]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the processing unit of Moaddeb/Rosenbluth to generate a multichannel waveform by generating multiple waveform parameters for multiple proprioceptive channels as taught by Rosenbluth so the tremor reduction stimulation can be applied to a plurality of peripheral nerves, if desired.
Regarding claim 34, the modified Moaddeb/Rosenbluth method discloses wherein the electric transducer (the vibration element(s) 136, 138, Fig. 15 of Moaddeb) is housed within a band (band 104, Fig. 8 of Moaddeb) of the wearable device (wearable tremor control system 100, Fig. 8), the band configured to attach the wearable device to the user (“To attach the wearable tremor control system 100 to the user's wrist 38, the user 42 (or a person aiding the user 42) slips a first end 114 of the band 104 through an opening 116 of the loop 106 and, while applying traction on the first end 114, pulls one or more of the wedges 110 through the opening 116 of the loop 106, until the band 104 is at a comfortable tightness around the user's wrist 38” see para. [0038] and Fig. 5).
Regarding claim 37, the modified Moaddeb/Rosenbluth method discloses further comprising comparing, by the waveform parameter selection system, a tremor amplitude with the current stimulation parameters to a tremor amplitude (tremor amplitude is detected at 2202, Fig. 22 of Rosenbluth) observed with a previous set of waveform parameters (the parameters set at step 2200, Fig. 22 of Rosenbluth) to determine which waveform parameters results in a lowest tremor amplitude (see the parameter optimization algorithm of Figure 22 of Rosenbluth. “The data can be processed by a controller 2222 which can optimize the stimulation parameters using various algorithms, including machine learning algorithms. Once the parameters are optimized, the new stimulation parameters are set 2224” see the last three sentences of [0221] of Rosenbluth. This parameter optimization algorithm is performed to “achieve the greatest tremor reduction with the most comfort in each patient” (see para. [0220] of Rosenbluth).
Regarding claim 38, the modified Moaddeb/Rosenbluth method discloses further comprising generating, by the processing unit, a train of waveform outputs (a series of impulses may be applied, see the last two sentences of [0066] of Moaddeb, and the “control unit is configured to repeat the activation cycle one or more times.” See para. [0068] of Moaddeb).
Regarding claim 40, the modified Moaddeb/Rosenbluth method discloses further comprising: receiving, by the processing unit (circuit board 190 with controller 192, Fig. 15 of Moaddeb), a selection of a waveform parameter from a user through a user interface (user interface 101, Fig. 8 of Moaddeb); and implementing, by the processing unit, the selected waveform parameter in the waveform output (“controller 192 may be configured to allow the user/patient to control some or all of these parameter adjustments, for example, via the user interface 101” see [0059] of Moaddeb. Adjusting the parameters will implement the selected parameters in the waveform output).
Claim(s) 25 and 35, as best understood, are rejected under 35 U.S.C. 103 as being unpatentable over Moaddeb et al. (2021/0330547) in view of Rosenbluth et al. (2019/0001129) as applied to claims 21 and 31 above, and further in view of Valero et al. (EP 2757391 A2).
Regarding claim 25, the modified Moaddeb/Rosenbluth device discloses that a Fourier transformation may be applied to the raw sensor data to extract motion data (see lines 1-9 of [0196] of Rosenbluth) but is silent regarding wherein the waveform parameter selection system is an argmax(FFT) process. However, it is noted that there does not appear to be any particular criticality to this algorithm, as it is not described in any detail and is merely listed as one of numerous possible “processes” that one of ordinary skill in the art would recognize could be included (see para. [0039] of the Specification).
Valero teaches a related algorithm for processing frequency-based sensor data (see title, see para [0076]), wherein an estimate of the center frequency parameter can be set from the FFT of a windowed signal based upon an argmax(FFT) process (see equation 22 and para. [0076]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the waveform parameter selection system of Rosenbluth so the dominant tremor frequency can be determined based upon an argmax(FFT) process of the tremor motion data, as generally taught by Valero because this will allow the central frequency parameter of the tremor motion to be estimated.
Regarding claim 35, the modified Moaddeb/Rosenbluth method discloses wherein the waveform parameter selection system is an argmax(FFT) process. However, it is noted that there does not appear to be any particular criticality to this algorithm, as it is not described in any detail and is merely listed as one of numerous possible “processes” that one of ordinary skill in the art would recognize could be included (see para. [0039] of the Specification).
Valero teaches a related algorithm for processing frequency-based sensor data (see title, see para [0076]), wherein an estimate of the center frequency parameter can be set from the FFT of a windowed signal based upon an argmax(FFT) process (see equation 22 and para. [0076]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the waveform parameter selection system of Rosenbluth so the dominant tremor frequency can be determined based upon an argmax(FFT) process of the tremor motion data, as generally taught by Valero because this will allow the central frequency parameter of the tremor motion to be estimated.
Claim(s) 26 and 36 are rejected under 35 U.S.C. 103 as being unpatentable over Moaddeb et al. (2021/0330547) in view of Rosenbluth et al. (2019/0001129) as applied to claims 21 and 31 above, and further in view of Rom (2012/0016435).
Regarding claim 26, the modified Moaddeb/Rosenbluth device discloses wherein the waveform parameter selection system utilizes a Q-learning model. However, it is noted that there does not appear to be any particular criticality to this algorithm, as it is not described in any detail and is merely listed as one of numerous possible “processes” that one of ordinary skill in the art would recognize could be included (see para. [0039] of the Specification).
Rom teaches a related stimulation system for treating movement disorders such as Parkinson’s Disease (see Fig. 1, see the first sentence of [0002]), where the system includes a mechanical body motion sensor such as an accelerometer to detect tremor (physiological sensor 30, Fig. 1; “a mechanical body motion sensor … at least one of an accelerometer, a tremor sensor and a rigidity sensor” see para. [0024]). The system uses a Q-learning model as a machine learning means to optimize stimulation parameters (“Q-learning (QL) is a reinforcement learning technique that works by learning an action-value function that gives the expected utility of taking a given action in a given state and following a fixed policy thereafter. One of the strengths of Q-learning is that it is able to compare the expected utility of the available actions without requiring a model of the environment.” See the first two sentences of [0010]; “modifying the programmed stimulation parameters thereby learning optimal stimulation parameters responsive to feedback from the provided physiological sensor; storing reward function values responsive to the modified stimulation parameters and resultant inputs from the physiological sensor in a QL look up table; and in the event that the learning optimal stimulation parameters converges within predetermined parameters, switching to an adaptive QL state in which brain stimulation is provided via the provided multi-electrode DBS lead alternately responsive to inputs from the physiologic sensor and inputs stored in the QL look up table, the alternate selection responsive to a probabilistic scheme.” See para. [0021]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the waveform parameter selection system of Moaddeb/Rosenbluth to utilize a Q-learning model as taught by Rom because this type of algorithm has strengths such as being able to compare the expected utility of the available actions without requiring a model of the environment.
Regarding claim 36, the modified Moaddeb/Rosenbluth method discloses wherein the waveform parameter selection system utilizes a Q- learning model. However, it is noted that there does not appear to be any particular criticality to this algorithm, as it is not described in any detail and is merely listed as one of numerous possible “processes” that one of ordinary skill in the art would recognize could be included (see para. [0039] of the Specification).
Rom teaches a related stimulation system for treating movement disorders such as Parkinson’s Disease (see Fig. 1, see the first sentence of [0002]), where the system includes a mechanical body motion sensor such as an accelerometer to detect tremor (physiological sensor 30, Fig. 1; “a mechanical body motion sensor … at least one of an accelerometer, a tremor sensor and a rigidity sensor” see para. [0024]). The system uses a Q-learning model as a machine learning means to optimize stimulation parameters (“Q-learning (QL) is a reinforcement learning technique that works by learning an action-value function that gives the expected utility of taking a given action in a given state and following a fixed policy thereafter. One of the strengths of Q-learning is that it is able to compare the expected utility of the available actions without requiring a model of the environment.” See the first two sentences of [0010]; “modifying the programmed stimulation parameters thereby learning optimal stimulation parameters responsive to feedback from the provided physiological sensor; storing reward function values responsive to the modified stimulation parameters and resultant inputs from the physiological sensor in a QL look up table; and in the event that the learning optimal stimulation parameters converges within predetermined parameters, switching to an adaptive QL state in which brain stimulation is provided via the provided multi-electrode DBS lead alternately responsive to inputs from the physiologic sensor and inputs stored in the QL look up table, the alternate selection responsive to a probabilistic scheme.” See para. [0021]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the waveform parameter selection system of Moaddeb/Rosenbluth to utilize a Q-learning model as taught by Rom because this type of algorithm has strengths such as being able to compare the expected utility of the available actions without requiring a model of the environment.
Claim(s) 29 and 39 are rejected under 35 U.S.C. 103 as being unpatentable over Moaddeb et al. (2021/0330547) in view of Rosenbluth et al. (2019/0001129) as applied to claims 28 and 38 above, and further in view of Heldman et al. (2014/0074179).
Regarding claim 29, the modified Moaddeb/Rosenbluth device is silent regarding wherein the each waveform output of the train of waveform outputs has a higher frequency than a previous waveform output of the train of waveform outputs.
Heldman teaches a related wearable device for movement disorder therapy (Fig. 1), wherein the processor (“micro-controller or processor” described in para. [0086]) is configured to provide a train (series) of waveform outputs through a transducer (electrical stimulation electrodes, such as for deep brain stimulation, see para. [0005]), wherein each waveform output of the train of waveform outputs has a higher frequency than a previous waveform output (see the trained tuning algorithm in Fig. 16, there is an initial set of stimulation parameters 200 that are increased incrementally at 202, and then it is determined whether there are any side effects 204 or if symptoms improve 206, and the parameters continue to increase until they reach their maximum setting or until symptoms are not improving. See para. [0190]: “If, however, the depicted algorithm determines that a) the subject's symptoms did improve under the provided therapy parameters or settings 206; b) symptoms did not improve and fewer than eight parameters/settings or groups thereof have been tested 214; or c) at least eight parameters/settings or group have been tested, but there has been some improvement within the last four iterations 216, then the algorithm next determines whether the therapy parameters or settings are being provided at a maximum value for one of those parameters or settings 208--in the depicted case pulse amplitude. Pre-determined parameter or settings limits may be defined to keep the therapy device operating within safe limits in order to protect the subject. If the specific parameter or setting that is being tested (in the depicted case, pulse amplitude) has not reached its maximum value, then the algorithm may again increase the value of that parameter or setting 202 and repeat the process. If the maximum value has already been reached for the particular parameter or setting, then that variable cannot be increased any further, and the algorithm determines whether another individual parameter or setting is at its maximum level 210--in the depicted case frequency. If the frequency has not reached its maximum value yet, then the algorithm reduces the amplitude to zero, increases the frequency 220 and then again begins the iterative process by initially increasing the amplitude 202 to provide therapy to the subject at the new levels of parameters or settings” and see para. [0191]: “This decision process is repeated by the intelligent algorithm until it determines that the best combination of contact(s) and parameters or settings has been achieved, resulting in an optimized therapy that takes into account the subject's side effects, symptoms, and/or other constraints”). Although Heldman is adjusting a parameter (frequency) of deep brain stimulation, one of ordinary skill in the art would have recognized that this guess-and-check type of optimization algorithm would be equally applicable to other stimulation modalities, including vibration frequency. Based upon Moaddeb, the symptom that would be intended to be reduced is the tremor frequency and/or amplitude.
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the processor of Moaddeb/Rosenbluth to provide a train of waveform outputs through the transducer, wherein each waveform output of the train of waveform outputs has a higher frequency than a previous waveform output of the train of waveform outputs as generally taught by Heldman because this algorithm helps determine the particular stimulation parameters that are best suited to reduce symptoms while taking into account possible side effects.
Regarding claim 39, the modified Moaddeb/Rosenbluth method is silent regarding wherein the each waveform output of the train of waveform outputs has a higher frequency than a previous waveform output of the train of waveform outputs.
Heldman teaches a related wearable device for movement disorder therapy (Fig. 1), wherein the processor (“micro-controller or processor” described in para. [0086]) is configured to provide a train (series) of waveform outputs through a transducer (electrical stimulation electrodes, such as for deep brain stimulation, see para. [0005]), wherein each waveform output of the train of waveform outputs has a higher frequency than a previous waveform output (see the trained tuning algorithm in Fig. 16, there is an initial set of stimulation parameters 200 that are increased incrementally at 202, and then it is determined whether there are any side effects 204 or if symptoms improve 206, and the parameters continue to increase until they reach their maximum setting or until symptoms are not improving. See para. [0190]: “If, however, the depicted algorithm determines that a) the subject's symptoms did improve under the provided therapy parameters or settings 206; b) symptoms did not improve and fewer than eight parameters/settings or groups thereof have been tested 214; or c) at least eight parameters/settings or group have been tested, but there has been some improvement within the last four iterations 216, then the algorithm next determines whether the therapy parameters or settings are being provided at a maximum value for one of those parameters or settings 208--in the depicted case pulse amplitude. Pre-determined parameter or settings limits may be defined to keep the therapy device operating within safe limits in order to protect the subject. If the specific parameter or setting that is being tested (in the depicted case, pulse amplitude) has not reached its maximum value, then the algorithm may again increase the value of that parameter or setting 202 and repeat the process. If the maximum value has already been reached for the particular parameter or setting, then that variable cannot be increased any further, and the algorithm determines whether another individual parameter or setting is at its maximum level 210--in the depicted case frequency. If the frequency has not reached its maximum value yet, then the algorithm reduces the amplitude to zero, increases the frequency 220 and then again begins the iterative process by initially increasing the amplitude 202 to provide therapy to the subject at the new levels of parameters or settings” and see para. [0191]: “This decision process is repeated by the intelligent algorithm until it determines that the best combination of contact(s) and parameters or settings has been achieved, resulting in an optimized therapy that takes into account the subject's side effects, symptoms, and/or other constraints”). Although Heldman is adjusting a parameter (frequency) of deep brain stimulation, one of ordinary skill in the art would have recognized that this guess-and-check type of optimization algorithm would be equally applicable to other stimulation modalities, including vibration frequency. Based upon Moaddeb, the symptom that would be intended to be reduced is the tremor frequency and/or amplitude.
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the processor of Moaddeb/Rosenbluth to provide a train of waveform outputs through the transducer, wherein each waveform output of the train of waveform outputs has a higher frequency than a previous waveform output of the train of waveform outputs as generally taught by Heldman because this algorithm helps determine the particular stimulation parameters that are best suited to reduce symptoms while taking into account possible side effects.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Pracar et al. (2015/0073310) discloses a related tremor detecting system that filters out artifacts. Tass (2021/0401664) discloses a related vibrotactile multi-channel stimulation system for treating brain disorders. Sharma et al. (2022/0001164) discloses a related tremor treatment device with filtering to remove voluntary motion. Kim et al. (2018/0140503) discloses a related vibratory stimulation device that waits several minutes after vibration before measuring body motion. Giuffrida et al. (2014/0005743) discloses a related wearable device for monitoring and treating a movement disorder. Zhang et al. (2018/0356890) discloses a related wearable device with sensors to detect tremors or involuntary motion. Wong et al. (9,802,041) discloses a related wearable device with tremor detection. Ross et al. (2021/0402172) discloses a related wearable neurostimulation device with sensors to detect tremor during different tasks. Choi et al. (2023/0123383) discloses a related neurostimulation device that detects tremor frequency, and may use Q-learning reinforcement learning strategies.
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/CHRISTOPHER E MILLER/ Examiner, Art Unit 3785
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