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 .
Double Patenting
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the claims that are directed to the same invention so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101.
Claims 1-22 provisionally rejected under 35 U.S.C. 101 as claiming the same invention as that of claims 1-22 of copending Application No. 18/394,712 (reference application). This is a provisional statutory double patenting rejection since the claims directed to the same invention have not in fact been patented.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-4, 10-11, 13-17, and 19-22 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Raghunathan (Pub. No.: US 2019/0083784 A1).
Regarding claim 1, 21, and 22, Raghunathan discloses a method for performing neurostimulation therapy for a Restless Legs Syndrome (RLS) or Periodic Limb Movement Disorder (PLMD) patient (e.g. see abstract), the method comprising: commencing a present sensing period (e.g. see figure 20 step “activity, sleep monitoring triggered”, [0141]); determining a sleep arousal time period, during the present sensing period, associated with actual or predicted patient transition from asleep to awake (e.g. see figure 20 step “waking detected”, [0141]); initiating, during the sleep arousal time period during the present sensing period, delivery of a high-frequency (HF) electrostimulation signal (e.g. see [0141], “If sleep onset is detected, then therapy can be stopped or paused, such as until waking is detected, at which time therapy can be resumed, with further monitoring of activity or sleep”) to a target location of the patient at a frequency between 500 Hz and 15,000 Hz, the HF electrostimulation signal configured to mitigate an RLS or PLMD symptom (e.g. see [0051], [0079]); and controlling the HF electrostimulation signal toward parameters subthreshold to the patient waking while maintaining the delivery of the HF electrostimulation signal for mitigating the RLS or PLMD symptom (e.g. see [0051], [0064], [0076], [0079]).
Regarding claim 2, Raghunathan discloses the present sensing period is based on an indication of present or expected sleep onset of the patient, indication of sleep onset including a received user input (e.g. see figure 20 step “Patient Turns on Therapy at Bedtime”, [0141], figure 12 step 1202A, [0115]-[0116])
Regarding claim 3, Raghunathan discloses determining the indication of present or expected sleep onset of the patient based on at least one of: motion sensor data (e.g. see figure 12 step 1202B) or impedance data (e.g. see figure 12 step 1202D) of the electrostimulation device.
Regarding claim 4, Raghunathan discloses the determining the sleep arousal time period is based at least in part on electrostimulation therapy data that includes movement data detected from the patient during the present sensing period (e.g. see figure 12 step 1202B).
Regarding claim 10, Raghunathan discloses receiving sensor data corresponding with the patient during the present sensing period (e.g. see figure 12 steps 1202A-1202D, figure 20 “activity, sleep monitoring triggered”); and wherein controlling the HF electrostimulation signal toward parameters subthreshold to the patient waking (e.g. see [0051], [0064], [0076], [0079]) includes establishing or adjusting at least one parameter of the HF electrostimulation signal based on the received sensor data (e.g. see figure 12 steps 1202A-1202D, figure 20 “activity, sleep monitoring triggered”. Note: The sensor data will dictate stimulation timing, which is a parameter).
Regarding claim 11, Raghunathan discloses the sensor data includes accelerometer data (e.g. see figure 12 step 1202B).
Regarding claim 13, Raghunathan discloses commencing the present sensing period is based on an indication that an electrostimulation device corresponding with the HF electrostimulation signal is being worn by the patient (e.g. see figure 20 “patient turns on therapy at bedtime” will read on “indication that an electrostimulation device corresponding with the HF electrostimulation signal is being worn by the patient”).
Regarding claim 14, Raghunathan discloses controlling the HF electrostimulation signal toward parameters subthreshold to the patient waking includes establishing or adjusting at least one signal parameter of the HF electrostimulation signal based on inertial measurement unit (IMU) data received during the sensing period and from a sensor circuit of an electrostimulation device associated with the HF electrostimulation signal (e.g. see figure 13 step 1202B and 1210).
Regarding claim 15, Raghunathan discloses processing the IMU data to classify a first subset of the IMU data subset indicating at least one of potential leg movement or potential increased symptoms (e.g. see figure 13 step 1202B).
Regarding claim 16, Raghunathan discloses calculating at least one feature set of the first subset of the IMU data, the first feature set including an indication of a change in position of an IMU sensor using a gravitational vector or angle of inclination (e.g. see figure 13 step 1202B).
Regarding claim 17, Raghunathan discloses using the calculated at least one feature set as an input to train a machine learning model to predict a range of target HF electrostimulation signal parameters for delivery to the patient that will be subthreshold to the patient waking while mitigating the RLS or PLMD symptom (e.g. see figure 13 step 1202B, [0124]).
Regarding claim 19, Raghunathan discloses using the calculated at least one feature set as an input to train a machine learning algorithm to predict a range of target HF electrostimulation signal parameters for delivery to the patient that will be subthreshold to the patient waking while mitigating the RLS or PLMD symptom (e.g. see [0124]).
Regarding claim 20, Raghunathan discloses controlling the HF electrostimulation signal includes establishing or adjusting at least one signal parameter of the HF electrostimulation signal based on at least one clinical variable corresponding with the patient, the clinical variable including at least one of a user input (e.g. see [0141], “The patient can turn on the therapy at bedtime, such as by actuating a switch or other user interface device”).
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) 5 and 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Raghunathan view of Toong et al. (Pub. No.: US 2021/0023360 A1); hereinafter referred to as “Toong”.
Regarding claim 5, Raghunathan discloses the invention but is silent as to determining the sleep arousal time period is based at least in part on electrostimulation therapy data that includes historical data from at least one previous sensing period associated with the same patient and occurring before the present sensing period. Toong teaches it is known to use such a modification as set forth in [0126] to provide Al techniques such as pattern recognition and correlation analysis to correlate real-time data recordings of the user with larger population databases to produce comparative or predictive analyses (e.g. see [0137]). It would have been obvious to one having ordinary skill in the art at the time the invention was made to use historical sleep data as taught by Toong in the system/method of Raghunathan, since said modification would provide the predictable results of Al techniques such as pattern recognition and correlation analysis to correlate real-time data recordings of the user with larger population databases to produce comparative or predictive analyses.
Regarding claim 8, Raghunathan discloses the invention but is silent as to determining the sleep arousal time period includes forecasting when a patient is likely to awaken during the present sensing period based on the historical data from the at least one previous sensing period. Toong teaches it is known to use such a modification as set forth in [0126] to provide Al techniques such as pattern recognition and correlation analysis to correlate real-time data recordings of the user with larger population databases to produce comparative or predictive analyses (e.g. see [0137]). It would have been obvious to one having ordinary skill in the art at the time the invention was made to use historical sleep data as taught by Toong in the system/method of Raghunathan, since said modification would provide the predictable results of Al techniques such as pattern recognition and correlation analysis to correlate real-time data recordings of the user with larger population databases to produce comparative or predictive analyses.
Regarding claim 9, Raghunathan discloses the invention but is silent as to the electrostimulation therapy data includes historical data corresponding with respective sensing periods corresponding with a plurality of different RLS or PLMD patients. Toong teaches it is known to use such a modification as set forth in [0126] to provide Al techniques such as pattern recognition and correlation analysis to correlate real-time data recordings of the user with larger population databases to produce comparative or predictive analyses (e.g. see [0137]). It would have been obvious to one having ordinary skill in the art at the time the invention was made to use historical sleep data as taught by Toong in the system/method of Raghunathan, since said modification would provide the predictable results of Al techniques such as pattern recognition and correlation analysis to correlate real-time data recordings of the user with larger population databases to produce comparative or predictive analyses.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Raghunathan in view of Ferree et al. (Pub. No.: US 2020/0030604 A1); hereinafter referred to as “Ferree”.
Regarding claim 12, Raghunathan discloses the claimed invention except for the sensor data includes temperature sensor data. Ferree teaches that it is known to use such a modification as set forth in figure 4 element 107 to support novel approaches for detecting when the user is asleep, and novel metrics for analyzing the sleep of the user, and novel means to quantify body and leg motions associated with poor sleep quality and/or disorders such as restless leg syndrome, and novel methods for providing enhanced TENS therapy using the same (e.g. see [0037]). It would have been obvious to one having ordinary skill in the art at the time the invention was made to use a temperature sensor for sleep detection as taught by Ferree in the system/method of Raghunathan, since said modification would provide the predictable results of supporting novel approaches for detecting when the user is asleep, and novel metrics for analyzing the sleep of the user, and novel means to quantify body and leg motions associated with poor sleep quality and/or disorders such as restless leg syndrome, and novel methods for providing enhanced TENS therapy using the same.
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Raghunathan in view of Nazari (Pub. No.: US 2016/0106344 A1).
Regarding claim 18, Raghunathan discloses calculating at least one feature set of HF electrostimulation signal frequency data, corresponding the first subset of IMU data (e.g. see figure 13 step 1202B) but does not disclose using a fast Fourier transform (FFT). Nazari teaches that it is known to use such a modification as set forth in figure 3 to better distinguish those frequencies of disorders including but not limited to the Restless Leg Syndrome limbs' motion from other type of movements such as driving, walking, running and etc (e.g. see [0036]). It would have been obvious to one having ordinary skill in the art at the time the invention was made to use a fast Fourier transform as taught by Nazari in the system/method of Raghunathan, since said modification would provide the predictable results of distinguishing those frequencies of disorders including but not limited to the Restless Leg Syndrome limbs' motion from other type of movements such as driving, walking, running and etc.
Conclusion
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/P.C.E/Examiner, Art Unit 3792
/UNSU JUNG/Supervisory Patent Examiner, Art Unit 3792