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 .
Election/Restrictions
Applicant's election with traverse of Invention I in the reply filed on 25 September 2025 is acknowledged. The traversal is on the ground(s) that the fields of search are believed to be co-extensive for the groups identified by the Examiner. This has been found persuasive.
The requirement is WITHDRAWN.
Claim Objections
Claim 8 is objected to because of the following informalities: line 2 recites “…using a combination or high frequency and/or low frequency…”. The “or” is considered to properly read “of”. Appropriate correction is required.
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, 2, 9, 12, 13-16, and 19-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sanghera et al. (US Publication no. 2019/0299010).
In regard to claim 1, Sanghera et al. disclose a system for identifying and reducing noise in a therapeutic procedure, comprising:
a pulse generator 102 configured to generate a therapeutic electrical signal (figure 3, para 62-68);
one or more leads 312/314 in communication with the pulse generator and configured to transmit the therapeutic electrical signal to a plurality of electrodes (para 66);
the plurality of electrodes in communication with the one or more leads 312/314 (para 66-67), the plurality of electrodes configured to apply the therapeutic electrical signal to an anatomical element of a patient and configured to measure a physiological response (para 68, a plurality of electrodes are utilized to create sensing and delivery vectors);
a processor (para 65, controller 302 includes a processor); and
a memory storing data for processing by the processor (para 64, controller 32 includes storage 308) the data, when processed, causes the processor to (para 64, electronic storage 308 can store instructions configured to be implemented by the controller to control the functions of pulse generator 102):
measure, via one or more of the plurality of electrodes, one or more signals with cardiac activity of the patient (para 174, 192);
identify one or more sources of noise that are distorting the one or more signals with cardiac activity (para 193-195, many sources of noise may corrupt cardiac signal detection, signal preparation 1702 apply filters to these signals to identify noise contained within these signals);
reduce the one or more sources of noise from the one or more signals with cardiac activity (para 194-195, signal preparation 1702 prepares the signals by applying hardware filters 1710, software filters 1712 and gain amplifiers 1714 for removing noise or interference signals 1706 detected by sensors of the cardiac pacing system);
determine one or more aggregate cardiac-derived metrics and/or save processed cardiac data based at least in part on the one or more signals with cardiac activity with the one or more sources of noise reduced (para 193, determine which signals are acceptable for use and then select the preferred signals for downstream sensing algorithms, wherein the “acceptable for use” signals are the “processed cardiac data with noise reduced”, and statement that “selects” preferred signals from acceptable signal implies that the acceptable signals are “set-aside” or “saved” in order to select which preferred signals are suitable for processing by downstream algorithms); and
determine one or more parameters for applying the therapeutic electrical signal to the anatomical element based at least in part on the one or more aggregate cardiac-derived metrics and/or saved processed cardiac data (para 226-227, the cardiac pacing system can use the resultant processed sensing signal(s) to analyze whether pacing a patient's heart is appropriate. Accurately sensing atrial and ventricular activity enables the cardiac pacing system to assess the need for therapy delivery, and the appropriate pacing mode for the patient).
In regard to claim 2, Sanghera et al. teaches that the memory stores further data for processing by the processor that, when processed, causes the processor to: process one or more portions of the one or more signals with cardiac activity, the one or more portions corresponding to time points when the one or more sources of noise are expected to occur after the therapeutic electrical signal is applied to the anatomical element (para 224, the smart switch algorithm can anticipate the inappropriateness of the signal selection based on the detected event and prophylactically switches to alternative sensing signal(s)).
In regard to claim 9, Sanghera et al. teach that the data stored in the memory that, when processed causes the processor to reduce the one or more sources of noise from the one or more signals with cardiac activity further causes the system to: filter the one or more signals with cardiac activity to reduce the one or more sources of noise (para 200, software filters can be executed by one or more processors of the cardiac pacing system, such as being embodied in controller of pulse generator; Software filters 1712 can be executed by one or more processors of a programmer wherein it is considered that the software filter is data stored in the storage element 308 to be executed by the processor).
In regard to claim 12, Saghera et al. teach that the one or more signals with cardiac activity of the patient are measured using a continuously running noise filter, wherein the noise filter is adjusted when the one or more sources of noise meet or exceed a predetermined threshold, wherein the adjusted noise filter uses more power than the non-adjusted noise filter (para 207, at 1734, a determination is made as to whether the signal meets the one or more criteria being assessed 1732. In response to a determination that the signal does not meet the criteria, no additional filter changes are required, and the signal is ready to be passed to downstream algorithms. At 1736, in response to a determination that the signal does meet the criteria, a determination can be made as to whether to alter the dynamic filter response).
In regard to claim 13, Sanghera et al. is equipped to output, via a user interface (programmer 320), the one or more aggregate cardiac-derived metrics and/or the saved processed cardiac data, wherein the one or more parameters for applying the therapeutic electrical signal to the anatomical element are determined based at least in part on outputting the one or more aggregate cardiac-derived metrics and/or the saved processed cardiac data (para 71-73 and 202, programmer is used by a clinician to define how signals are sensed and therapy is delivered, additionally the programmer may be programmed to enable a user or automatically modify its filtering upon meeting certain conditions).
In regard to claim 14, Sanghera et al. teach that the one or more signals with cardiac activity are measured when the patient is stationary or in a specific position (para 208, dynamic filtering assessments when obtaining patient input during the setup procedure for normalizing the cardiac pacing system, especially when conducting, for example, posture analysis, exercising, and the like; para 258, such signals are used to create templates that reflect the template when the patient is in different postures, has different heart rates, for different sense vectors, and for other like parameters. Furthermore, the selection of the appropriate template for comparison can be automatically updated based upon factors including pacing mode, heart rate, posture, time of day or other measures of the input signals).
In regard to claim 15, Sanghera et al. teaches that the one or more sources of noise are caused by the patient walking or moving, a stimulation artifact, environmental noise, evoked compound action potential activity, evoked compound muscle action potential activity, or a combination thereof (para 176; accelerometers ca be used to identify potential noise in signals; para 194, noise may come from muscle tissue movements.
In regard to claim 16, Sanghera et al. disclose a system for identifying and reducing noise in a therapeutic procedure, comprising:
a processor (para 65, controller 302 includes a processor); and
a memory storing data for processing by the processor (para 64, controller 32 includes storage 308) the data, when processed, causes the processor to (para 64, electronic storage 308 can store instructions configured to be implemented by the controller to control the functions of pulse generator 102):
measure, via one or more of the plurality of electrodes, one or more signals with cardiac activity of the patient (para 174, 192);
identify one or more sources of noise that are distorting the one or more signals with cardiac activity (para 193-195, many sources of noise may corrupt cardiac signal detection, signal preparation 1702 apply filters to these signals to identify noise contained within these signals);
reduce the one or more sources of noise from the one or more signals with cardiac activity (para 194-195, signal preparation 1702 prepares the signals by applying hardware filters 1710, software filters 1712 and gain amplifiers 1714 for removing noise or interference signals 1706 detected by sensors of the cardiac pacing system);
determine one or more aggregate cardiac-derived metrics and/or save processed cardiac data based at least in part on the one or more signals with cardiac activity with the one or more sources of noise reduced (para 193, determine which signals are acceptable for use and then select the preferred signals for downstream sensing algorithms, wherein the “acceptable for use” signals are the “processed cardiac data with noise reduced”, and statement that “selects” preferred signals from acceptable signal implies that the acceptable signals are “set-aside” or “saved” in order to select which preferred signals are suitable for processing by downstream algorithms); and determine one or more parameters for applying the therapeutic electrical signal to the anatomical element based at least in part on the one or more aggregate cardiac-derived metrics and/or saved processed cardiac data (para 226-227, the cardiac pacing system can use the resultant processed sensing signal(s) to analyze whether pacing a patient's heart is appropriate. Accurately sensing atrial and ventricular activity enables the cardiac pacing system to assess the need for therapy delivery, and the appropriate pacing mode for the patient).
In regard to claim 19, Sanghera et al. disclose a system for identifying and reducing noise in a therapeutic procedure, comprising:
a pulse generator 102 configured to generate a therapeutic electrical signal (figure 3, para 62-68);
one or more leads 312/314 in communication with the pulse generator and configured to transmit the therapeutic electrical signal to a plurality of electrodes (para 66);
the plurality of electrodes in communication with the one or more leads 312/314 (para 66-67), the plurality of electrodes configured to apply the therapeutic electrical signal to an anatomical element of a patient and configured to measure a physiological response (para 68, a plurality of electrodes are utilized to create sensing and delivery vectors);
wherein the therapeutic electrical signal is applied to the anatomical element (para 226-227, the cardiac pacing system can use the resultant processed sensing signal(s) to analyze whether pacing a patient's heart is appropriate. Accurately sensing atrial and ventricular activity enables the cardiac pacing system to assess the need for therapy delivery, and the appropriate pacing mode for the patient) according to one or more parameters determined from one or more aggregate cardiac metrics and/or saved processed cardiac data derived from one or more signals with cardiac activity measured via one or more of the plurality of electrodes (para 193, determine which signals are acceptable for use and then select the preferred signals for downstream sensing algorithms, wherein the “acceptable for use” signals are the “processed cardiac data with noise reduced”, and statement that “selects” preferred signals from acceptable signal implies that the acceptable signals are “set-aside” or “saved” in order to select which preferred signals are suitable for processing by downstream algorithms), the one or more signals with cardiac activity having one or more sources of noise reduced from the one or more signals with cardiac activity (para 194-195, signal preparation 1702 prepares the signals by applying hardware filters 1710, software filters 1712 and gain amplifiers 1714 for removing noise or interference signals 1706 detected by sensors of the cardiac pacing system).
In regard to claim 20, Sanghera et a. teach that the one or more sources of noise are caused by the patient walking or moving, a stimulation artifact, environmental noise, evoked compound action potential activity, evoked compound muscle action potential activity, or a combination thereof (para 176; accelerometers ca be used to identify potential noise in signals; para 194, noise may come from muscle tissue movements).
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) 3 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sanghera et al. (US Publication no. 2019/0299010) in view of Sholder (US Patent no. 4,974,589).
In regard to claim 3 and 17, Sanghera et al. describes the invention as claimed, however does not teach that the one or more portions of the one or more signals with cardiac activity are blanked immediately before, during, and/or after individual pulses of the therapeutic electrical signal are applied to the anatomical element. Sholder teaches that is known in the art to utilized blanking intervals in a pacemaker disable sensing channels following delivery of stimulation pulse in order to avoid sensing noise (col 2 lines 43-54). In view of this, it is considered to have been obvious to one of ordinary skill in the art to blank a cardiac signals before, during, or after a therapeutic pulse since the technique is explicitly taught as being conventional and well known in the art by Sholder. The modification is considered to comprise the application of a known technique to a known device to yield a predictable result.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sanghera et al. (US Publication no. 2019/0299010) in view of Sholder (US Patent no. 4,974,589), further in view of Schie et al. (US Publication no. 2015/0045684).
In regard to claim 4, Sanghera et al. in view of Sholder suggest the invention as claimed, however do not teach that the one or more portions of the one or more signals with cardiac activity are processed based at least in part on a template subtraction method, spectral filtering, wavelet filtering, or a combination thereof. Schie et al. teaches that wavelet decomposition is a common technique for filtering cardiac signals (para 40). Therefore the modification to implement in the software/hardware filters of Sanghera et al. a wavelet decomposition algorithm is considered to have been obvious to one of ordinary skill in the art as the application of a known technique to a known device to yield a predictable result. Moreover, the template subtraction method and spectral filtering are considered suitable alternative equivalents of wavelet filtering that one of ordinary skill in the art may substitute.
Claim(s) 5, 6, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sanghera et al. (US Publication no. 2019/0299010) in view of Karantonis et al. (US Publication no. 2019/0239768).
In regard to claims 5, 6, and 19, Sanghera et al. describe the invention as claimed, however does not teach that the he memory stores further data for processing by the processor that, when processed, causes the processor to: generate one or more growth curves based at least in part on applying the therapeutic electrical signal to the anatomical element; determine a threshold based at least in part on the one or more growth curves, wherein the one or more aggregate cardiac-derived metrics are collected when evoked signals are below the determined threshold; and perform signal processing for stimuli that evoke signals larger than the threshold to measure the one or more signals with cardiac activity, wherein the one or more sources of noise are reduced based at least in part on performing the signal processing for stimuli that evoke signals larger than the threshold, wherein the stimuli that evoke signals larger than the threshold comprise stimulation amplitudes for added processing. Karantonis et al. teaches and depicts in figures 10 and 11, generate one or more growth curves based at least in part on applying the therapeutic electrical signal to the anatomical element; determine a threshold based at least in part on the one or more growth curves, wherein the one or more aggregate cardiac-derived metrics are collected when evoked signals are below the determined threshold; and perform signal processing for stimuli that evoke signals larger than the threshold to measure the one or more signals with cardiac activity, wherein the one or more sources of noise are reduced based at least in part on performing the signal processing for stimuli that evoke signals larger than the threshold, wherein the stimuli that evoke signals larger than the threshold comprise stimulation amplitudes for added processing (para 85, the stimulus threshold, above which compound action potentials are being evoked by the applied stimuli, is a critical parameter for most neuromodulation applications, the slope of the growth curve above the stimulus threshold, another critical parameter in many neuromodulation applications, is much less affected by noise). Therefore it is considered to have been obvious to one of ordinary skill in the art to derive growth curve since Karantonis et al. demonstrates that growth curves are well known for to estimate thresholds for which as stimulus evokes a response. The modification is considered to comprise the application of a known technique to a known device to yield predictable results.
Claim(s) 7 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sanghera et al. (US Publication no. 2019/0299010) in view of Vallejo et al. (US Publication no. 2020/0353256).
In regard to claim 7, Sanghera et al. describe the invention as claimed, however does not teach that the data stored in the memory that, when processed causes the processor to reduce the one or more sources of noise from the one or more signals with cardiac activity further causes the system to: cycle one or more parameters used for applying the therapeutic electrical signal to the anatomical element and/or one or more parameters for measuring the one or more signals with cardiac activity between ‘on’ phases and ‘off’ phases, wherein the one or more signals with cardiac activity are measured during the ‘off’ phases. The present specification teaches that this technique is known in the art as differential target multiplexing “DTM” (para 61 and 91, DTM leverages cycling off times to record the signals). Vallejo et al. incorporates DTM for pain treatment. In view of this, it is considered to have been obvious to one of ordinary skill in the art to cycle one of more parameters when reducing noise in the collected signal since Vallejo et al. teaches that this technique known as “DTM” is commonly applied in the art. The modification would comprise the application of a known technique to a known device to yield an improvement in noise reduction.
In regard to claim 8, Vallejo et al. further teaches that the DTM technique comprises applying the therapeutic electrical signal to the anatomical element using a combination of high frequency and/or low frequency stimulations and/or high and/or low amplitudes, and the ‘off’ phases comprise applying the therapeutic electrical signal to the anatomical element using low frequency stimulations, lower ratios of cycling, low amplitudes, or a combination thereof (para 8). The limitations are considered to comprise conventional aspects of the DTM technique.
Claim(s) 10 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sanghera et al. (US Publication no. 2019/0299010) in view of Kogure (US Publication no. 2019/0159695).
In regard to claim 10, Sanghera et al. describe the invention as claimed, however does not teach that the data stored in the memory that, when processed causes the processor to reduce the one or more sources of noise from the one or more signals with cardiac activity further causes the system to: identify time points when the one or more signals with cardiac activity have been corrupted by the one or more sources of noise; and remove portions of the one or more signals with cardiac activity corresponding to the identified time points to reduce the one or more sources of noise from the one or more signals with cardiac activity. Kogure teaches that to reduce the one or more sources of noise identifies time points when the one or more signals with cardiac activity have been corrupted by the one or more sources of noise; (para 60-61, an evaluating unit evaluates the reliability of the biological information value on the basis of an amplitude and variation of waveforms after filtering, and if there is variation in the amplitude, the reliability evaluating unit determines that the reliability is relatively low; the features or amplitude or variation are considered to comprise time points in the signal). It is considered that the hardware/software filters of Sanghera et al. can remove the low reliability signals identified by Kogure in order to improve reliability and accuracy of sensed signals. In view of this, it is considered to have been obvious to one of ordinary skill in the art time points in a signal that are corrupted as determined by Kogure into the filtering technique of Sanghera et al. in order to remove signal of low reliability. The modification would comprise the application of a known technique to a known device to yield an improvement in signal processing.
In regard to claim 11, the technique of Kogure as relied on above teaches that the time points are identified based at least in part on time intervals when a given R-R interval is non-physiological and/or differs from R-R intervals detected for preceding heartbeats by a defined threshold (para 61, Kogure determines variations in R-R- interval variability).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN T GEDEON whose telephone number is (571)272-3447. The examiner can normally be reached M-F 8:00 am to 5:30 PM ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David E. Hamaoui can be reached at 571-270-5625. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BRIAN T GEDEON/Primary Examiner, Art Unit 3796 12 December 2025