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 .
Examiner’s Note
Attorney of Record, John Brice, was contacted on 06/03/2026 to inform that a single species hadn’t been elected, as required on pages 5-6 of the 04/08/2026 Restriction Requirement. Applicant informed Examiner that Species B (Claim 9) was elected without traverse. Please see Election/Restriction section below.
Election/Restrictions
Claims 17-20 withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to nonelected inventions, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on 05/14/2026.
Claim 8 withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected species, there being no allowable generic or linking claim. Election was made without traverse in the phone call with Attorney of Record, John Brice, on 06/03/2026 (see above “Examiner’s Note).
Applicant’s election without traverse of Species B (Claim 9) in the phone call with Attorney of Record, John Brice, on 06/03/2026 is acknowledged.
Applicant's election with traverse of Group I (Claims 1-16) in the reply filed on 05/14/2026 is acknowledged. The traversal is on the ground(s) that there would be a serious search and/or examination burden if restriction were not required. The Applicant argues that the separate classification of the subject matter under the classification system of the USPTO is not conclusive proof of divisibility. The Applicant also argues that there would be overlap in the possible art found during separate searches of the alleged groups given that each group recites the shared features of receiving a data signal from a device (e.g., a sensor or an electrode) and assigning a classification to one or more portions of a waveform associated with the data signal. This is not found persuasive because the invention groups (i.e., groups I-III) are found to have a serious search and/or examination burden if restriction were not required. In addition to having acquired a separate status in the art in view of their different classification, the inventions also have divergent subject matter and require a different field of search (e.g., employing different search strategies or search queries). For instance, Inventions I and II will require different fields of searches because Invention I requires receiving the data signal from a broader class of “sensor(s)”, whereas Invention II requires receiving the data signal specifically from one or more electrodes. There is a much larger and divergent field of search required for Invention I than Invention II, as there are many types of sensors (i.e., more than just electrodes) that can receive a data signal in response to therapy delivered to a patient. Also, the type of data signals and the information they convey is potentially much larger and divergent than can be received from all types of sensors over strictly electrodes. Similar reasoning is found for Inventions II and III having a serious search and/or examination burden. For Inventions I and III, the process of Invention III can be practiced by hand, and does not require a processor or a memory, as the apparatus of Invention I does. Therefore, these two inventions will also require a different field of search. Therefore, the Applicant’s arguments are not found persuasive. The requirement is still deemed proper and is therefore made FINAL.
Claims 1-7 and 9-16 are examined herein, with claims 8 and 17-20 being withdrawn.
Information Disclosure Statement
The Information Disclosure Statements filed 11/26/2024 and 01/06/2025 have been considered by the Examiner.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Claim 2 objected to because of the following informalities:
Line 1: “by the processor to::” should be changed to “by the processor to:”. Please remove second colon.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-7 and 9-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process of assigning a classification portions of a waveform associated with a data signal) without significantly more.
Step 1
Independent claim 1 is directed to a system and thus meets the requirements for step 1.
Step 2A, Prong 1
Claim 1 recites the following limitations which are interpreted as potentially inclusive of an abstract idea:
“assign a classification to one or more portions of a waveform associated with the data signal based on characteristic information associated with the one or more portions of the waveform” – This limitation is a mental process when given its broadest reasonable interpretation. As discussed in MPEP 2106.04(a)(2)(III), the mental process grouping includes observations, evaluation, judgements, and opinions. In this case, a human could mentally assign a classification (i.e., make a mental judgement) to one or more portions of a waveform based on observed or received characteristic information.
Step 2A, Prong 2
a processor – A processor is recited by the Applicant with a high level of generality, as demonstrated in Applicant’s Par. [0049]. The involvement of the processor is insignificant extra-solution activity in that it amounts to generic computer implementation of the abstract idea [MPEP 2106.04(a)(2)(III)(C)].
a memory storing data – Memory is recited by the Applicant with a high level of generality, as demonstrated in Applicant’s Par. [0065]. The involvement of memory is insignificant extra-solution activity in that it amounts to generic computer implementation of the abstract idea [MPEP 2106.04(a)(2)(III)(C)].
Receive a data signal from one or more sensors associated with the system in response to therapy delivered to a patient – insignificant pre-solution activity, i.e. mere data gathering [MPEP 2106.05(g)]
One or more sensors associated with the system – Sensors are recited with a high level of generality in the Applicant’s specification as being for example, an electrode, an accelerometer, a sensor integrated or coupled to the wearable device, etc. (Par. [0112]). The involvement of the sensors is insignificant extra-solution activity in that it is merely used to gather and collect data [MPEP 2106.05(g)].
Therefore, the claim is directed to an abstract idea without a practical application.
Step 2B
The additional elements of claim 1, when considered either individually or in an ordered combination, are not enough to qualify as significantly more than the abstract idea. As discussed above with respect to the integration of the abstract idea into a practical application, the processor and memory, along with their associated functions and components, are recited with a high level of generality and simply amount to implementing the abstract idea on a computer. The additional elements that were considered insignificant extra-solution activity have been re-analyzed and do not amount to anything more than what is well-understood, routine, and conventional. Also, simply appending well-understood, routine, and conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception is not indicative of an inventive concept [MPEP 2106.05(d)].
Receive a data signal from one or more sensors associated with the system in response to therapy delivered to a patient – MPEP 2106.05(d)(II)(“i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”); Wang, et al. (US 2011/0301663) – Fig. 2; Par. [0019]: one embodiment of the device 100 illustrated in FIG. 2 comprises circuitry for applying electrical stimulation waveforms to the muscular tissue, in which the fixation element 120 is embedded, and/or for sensing electrical signals received by the electrodes 104, in accordance with conventional implantable electronic stimulator devices known in the art. ; Par. [0021]: The electrodes 104 are configured to receive an electrical stimulation signal or waveform 136 from a stimulator device in accordance with conventional techniques.
Therefore, the claims are directed to an abstract idea without a practical application and without significantly more.
Dependent claims
Regarding dependent claims 2-5, 10-11, and 13-16, the limitations only further define the abstract idea.
Regarding dependent claims 6-7, the limitations only further define insignificant extra-solution activity of generic computer implementation of the abstract idea.
Regarding dependent claims 6, 9, and 12-13, the limitations only further define insignificant extra-solution activity of gathering data.
Therefore, claims 1-7 and 9-16 are unpatentable under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6 and 9-16 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Steinke, et al. (US 2022/0266022).
Regarding claim 1, Steinke teaches (Fig. 10, # 1000) a system (Abstract – systems; Par. [0011]; Par. [0015]; Pars. [0079-0080] – system 1000) comprising:
(Fig. 5, # 70, 72 – computing device, 88 – control circuitry, i.e. processor; Fig. 10, # 70) a processor (Par. [0061] – control circuitry 88 such as microprocessors); and
(Fig. 5, # 70, 72, 84 – clinician programmer software stored on the computing device, 86 – memory; Fig. 10, # 70) a memory storing data thereon that, when processed by the processor (Par. [0061]), cause the processor to:
(Fig. 9, # 902) receive a data signal from one or more sensors associated with the system in response to therapy delivered to a patient (Par. [0078] – This disclosure particularly relates to methods and systems for using recorded neural activity as a biomarker to inform aspects of neuromodulation therapy, such as DBS therapy… According to the workflow 900, electrical activity (i.e., field potentials and the like) occurring at implanted electrodes [i.e., sensors] may be recorded 902. Such recorded electrical activity may include evoked and/or innate neural activity, as well as other activity, such as stimulation artifacts.);
(Fig. 8, # 908; Fig. 13) assign a classification to one or more portions of a waveform associated with the data signal based on characteristic information associated with the one or more portions of the waveform (Par. [0078] – The detected peaks or other features may be classified 908 to determine if they possess the right temporal or frequency characteristics (or other characteristics) to potentially serve as biomarkers that may be useful for informing aspects of therapy.; Par. [0096] – FIG. 13 illustrates one example of how frequency domain peaks can be classified.).
Therefore, claim 1 is unpatentable over Steinke, et al.
Regarding claim 2, Steinke teaches the system of claim 1, wherein the data is further executable by the processor to::
(Fig. 9, # 910, 912) provide, based on the classification, one or more parameters associated with delivering the therapy (Par. [0078] – If a given peak/feature does meet the criteria for serving as a biomarker, the peak/feature may be analyzed to extract certain features or metrics 910. Such features or metrics can serve as feedback to inform various actions 912 related to neuromodulation therapy.).
Therefore, claim 2 is unpatentable over Steinke, et al.
Regarding claim 3, Steinke teaches the system of claim 2, wherein (Fig. 13, # 1300) the classification is comprised in a set of classifications (Par. [0096]) comprising:
(Fig. 13, # 1304) a first classification indicating the one or more portions of the waveform as an electrical response by one or more anatomical elements of the patient in association with delivering the therapy (Par. [0096] – In the illustrated plot, peaks 1304 (represented by x's) are classified as corresponding to a relevant neural signal (in this case, ERNA signals));
(Fig. 13, # 1306) a second classification indicating the one or more portions of the waveform as a non-response by the one or more anatomical elements in association with delivering the therapy (Par. [0096] – Other peaks, such as peaks 1306 derived from ECAPs (represented by +'s) and 1308 derived from noise (represented by o's) are not classified as relevant neural signals.); and
(Fig. 13, # 1308) a third classification indicating the one or more portions of the waveform as noise (Par. [0096] – Other peaks, such as peaks 1306 derived from ECAPs (represented by +'s) and 1308 derived from noise (represented by o's) are not classified as relevant neural signals.).
Therefore, claim 3 is unpatentable over Steinke, et al.
Regarding claim 4, Steinke teaches the system of claim 2, wherein (Fig. 9, # 910, 912) the one or more parameters comprise one or more stimulation parameters associated with delivering the therapy (Par. [0078] – the determined features/metrics may be indicative of the therapeutic efficacy of the stimulation, which may be tied to aspects of stimulation, such as lead placement, stimulation placement, electrode configuration, stimulation parameters, and the like. Thus, the features/metrics may be used to direct actions related to those aspects.).
Therefore, claim 4 is unpatentable over Steinke, et al.
Regarding claim 5, Steinke teaches the system of claim 2, wherein the data is further executable by the processor to:
(Fig. 9, # 910, 912; Fig. 14) provide, based on the classification, a first electrode configuration associated with delivering the therapy, a second electrode configuration associated with sensing a response to delivering the therapy, or both (Par. [0078] – the determined features/metrics may be indicative of the therapeutic efficacy of the stimulation, which may be tied to aspects of stimulation, such as lead placement, stimulation placement, electrode configuration, stimulation parameters, and the like. Thus, the features/metrics may be used to direct actions related to those aspects.; Par. [0102]).
Therefore, claim 5 is unpatentable over Steinke, et al.
Regarding claim 6, Steinke teaches the system of claim 2, wherein the data is further executable by the processor to:
(Fig. 15) provide at least a portion of the data signal to one or more machine learning models; and
receive an output from the one or more machine learning models in response to the one or more machine learning models processing at least the portion of the data signal, wherein the output comprises the classification (Par. [0103] – The classification and extraction techniques described above can then be used to analyze the extracted interesting signal components. The resulting components and their contributions may provide a “fingerprint” of the recording montage for the stimulation electrode. Iteration over the stimulation electrodes can be used to determine the electrode(s) at which stimulation results in the most desirable neural response signal fingerprint. Machine learning or other statistical techniques may be used to infer current fractionalization parameters that will elicit the strongest neural response fingerprint.).
Therefore, claim 6 is unpatentable over Steinke, et al.
Regarding claim 9, Steinke teaches the system of claim 1, wherein (Fig. 9, # 904) the waveform comprises a raw waveform corresponding to the data signal (Par. [0078] – the recorded signals may be preprocessed; Examiner notes that word “may” insinuates that the data signal isn’t required to be preprocessed; Par. [0091] – It should be noted here that amplitude can mean the raw signal, the signal after some pre-processing (esp. filtering), and especially the signal normalized, e.g. by all recorded channels, by the maximum from a set of recordings, against background noise (e.g. as a ratio of noise floor or lower-level), or the like.).
Therefore, claim 9 is unpatentable over Steinke, et al.
Regarding claim 10, Steinke teaches the system of claim 2, wherein (Fig. 9, # 908; Fig. 13, # 1306 and 1308) the classification indicates the one or more portions of the waveform as a non-response or noise, based on comparing the one or more portions of the waveform to one or more reference artifacts (Par. [0078]; Par. [0082-0083] – Such an indication may be based on historical data [i.e., reference data] correlating one or more of the features/metrics with therapeutic efficacy, models based on historical data, or modeled data; Par. [0096] – Other peaks, such as peaks 1306 derived from ECAPs (represented by +'s) and 1308 derived from noise (represented by o's) are not classified as relevant neural signals.; Par. [0101] – determining confidence interval around a region of the spectrum when (or where) a neural signal is expected (i.e. reference artifact); Par. [0116] – Any of the techniques described above for classifying and extracting neural response metrics can be performed on the patient and the patient's neural response metrics (or fingerprints) can be compared to historical data for patients that have (or have not) responded well to coordinated reset stimulation to identify potential coordinated reset candidates.).
Therefore, claim 10 is unpatentable over Steinke, et al.
Regarding claim 11, Steinke teaches the system of claim 2, wherein (Fig. 9, # 908; Fig. 13, # 1304 and 1306) the classification indicates the one or more portions of the waveform as an evoked response, based on comparing the one or more portions of the waveform to one or more waveform templates associated with a reference evoked response (Par. [0078]; Par. [0082-0083] – Such an indication may be based on historical data [i.e., reference data] correlating one or more of the features/metrics with therapeutic efficacy, models based on historical data, or modeled data; Par. [0096] – In the illustrated plot, peaks 1304 (represented by x's) are classified as corresponding to a relevant neural signal (in this case, ERNA signals); Par. [0101] – determining confidence interval around a region of the spectrum when (or where) a neural signal is expected (i.e. reference artifact); Par. [0116] – Any of the techniques described above for classifying and extracting neural response metrics can be performed on the patient and the patient's neural response metrics (or fingerprints) can be compared to historical data for patients that have (or have not) responded well to coordinated reset stimulation to identify potential coordinated reset candidates.).
Therefore, claim 11 is unpatentable over Steinke, et al.
Regarding claim 12, Steinke teaches the system of claim 1, wherein (Fig. 8; Fig. 13, # 1306) the data signal comprises an evoked compound action potential (ECAP) signal, an evoked compound muscle action potential (ECMAP) signal, or a combination thereof (Par. [0077] – The recorded response may also contain an evoked compound action potential (ECAP) occurring 1-2 milliseconds after the stimulation pulse, though the ECAP in trace (B) is obscured by the stimulation artifact;. Par. [0096] – Other peaks, such as peaks 1306 derived from ECAPs (represented by +'s)).
Therefore, claim 12 is unpatentable over Steinke, et al.
Regarding claim 13, Steinke teaches the system of claim 2, wherein (Fig. 9, # 908; Fig. 13) assigning the classification is further based on at least one of:
temporal information associated with the data signal;
frequency information associated with the data signal;
accelerometer data corresponding to one or more sensors associated with monitoring physiological information associated with the patient;
impedance data corresponding to the one or more sensors; and
measured values associated with the physiological information (Par. [0078] – The detected peaks or other features may be classified 908 to determine if they possess the right temporal or frequency characteristics (or other characteristics) to potentially serve as biomarkers that may be useful for informing aspects of therapy.; Par. [0096]).
Therefore, claim 13 is unpatentable over Steinke, et al.
Regarding claim 14, Steinke teaches the system of claim 2, wherein (Fig. 9, # 908; Fig. 13) assigning the classification is absent a temporal window associated with detecting the data signal by the one or more sensors (Par. [0078] – The detected peaks or other features may be classified 908 to determine if they possess the right temporal or frequency characteristics (or other characteristics) to potentially serve as biomarkers that may be useful for informing aspects of therapy.; Examiner notes that Steinke teaches classification that is not based on temporal characteristics (i.e., could be frequency or other characteristics); Par. [0089-0091] – Amplitude Threshold is an example classification criteria that can be used to classify the signal [not requiring a temporal window]; Par. [0096] – classification may be based on relative band power (i.e., not requiring a temporal window)).
Therefore, claim 14 is unpatentable over Steinke, et al.
Regarding claim 15, Steinke teaches the system of claim 2, wherein (Fig. 9, # 908; Fig. 13) assigning the classification is absent a threshold value associated with the waveform (Par. [0096] – The peaks may also be classified based on their FWHM, which is proportional to the decay constant of a decaying sinusoidal signal, whereby only peaks corresponding to a signal with a significantly long decay are classified. Peaks may be classified based on their confidence interval, as described in more detail below. In general, the disclosed methods involve using predetermined criteria believed to correspond to a relevant neural signal, analyzing the frequency domain signal to determine if those criteria are met, and classifying the signal as an actionable signal only if those criteria are met.; Par. [0101] – One method of determining confidence may comprise determining a first confidence interval around a region of the spectrum when (or where) a neural signal is not expected to occur and determining a second confidence interval around a region of the spectrum when (or where) a neural signal is expected to occur. If the confidence intervals do not overlap, then the presence of the neural signal can be declared.; Examiner notes that classification based on FWHM and confidence intervals are absent a threshold value).
Therefore, claim 15 is unpatentable over Steinke, et al.
Regarding claim 16, Steinke teaches the system of claim 2, wherein (Fig. 9, # 908, 912; Fig. 13) the classification comprises an indication of at least one of:
a signal type associated with the data signal;
anatomical information associated with the patient and the data signal;
mapping information corresponding to the one or more sensors, one or more second sensors associated with delivering the therapy, or both;
the one or more parameters associated with delivering the therapy; and state information associated with the patient (Par. [0078] – The detected peaks or other features may be classified 908 to determine if they possess the right temporal or frequency characteristics (or other characteristics) to potentially serve as biomarkers that may be useful for informing aspects of therapy… the determined features/metrics may be indicative of the therapeutic efficacy of the stimulation, which may be tied to aspects of stimulation, such as lead placement, stimulation placement, electrode configuration, stimulation parameters, and the like. Thus, the features/metrics may be used to direct actions related to those aspects; Par. [0096]).
Therefore, claim 16 is unpatentable over Steinke, et al.
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 factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Steinke, et al. (US 2022/0266022) in view of Jayakumar, et al. (US 2023/0191131 – cited on IDS).
Regarding claim 7, Steinke teaches the system of claim 6, as indicated hereinabove. Steinke does not explicitly teach the limitation of instant claim 7, that is wherein the one or more machine learning models comprise one or more of the following:
one or more support vector machines (SVMs);
one or more convolutional neural network (CNN) models;
one or more feed forward neural network models;
one or more transformer neural network models; and
one or more decision trees.
Jayakumar, is directed to analogous art, and teaches closed-loop feature optimization of biological signals (Title; Abstract). Jayakumar also teaches the limitation of instant claim 7, that is wherein (Figs. 5 and 7) the one or more machine learning models comprise one or more of the following:
one or more support vector machines (SVMs);
one or more convolutional neural network (CNN) models;
one or more feed forward neural network models;
one or more transformer neural network models; and
one or more decision trees (Par. [0069] – In some example embodiments, machine-learning programs (MLPs), also referred to as machine-learning algorithms or tools, are utilized to perform operations associated with machine learning tasks, such as identifying relationship(s) between detected feature(s) in a sensed biological signal and waveform parameter(s) used to control the neuromodulation. Thus, machine learning may be used to determine the relationships between the extracted features and the simulation therapy referenced in FIGS. 5 and 535.; Par. [0074] – One of ordinary skill in the art will be familiar with several other machine learning algorithms that may be applied with the present disclosure, including linear regression, random forests, decision tree learning, neural networks, deep neural networks, etc.).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have implemented Jayakumar’s teaching of using particular machine learning models, such as decision trees or neural networks, into Steinke’s system, because doing so would be an example of applying a known technique to a known device ready for improvement to yield predictable results. One of ordinary skill in the art would recognize that Steinke teaches using machine learning (see Par. [0103] of Steinke) and would have recognized the listed machine learning options, such as neural networks and decision trees disclosed by Jayakumar (see Par. [0074]), to be well known types of machine learning.
Therefore, claim 7 is unpatentable over Steinke, et al. and Jayakumar, et al.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Dinsmoor, et al. (US2018/0078769)
Johnson, et al. (US2024/0139515)
Steinke, et al. (US2025/0018194)
Brounstein, et al. (US 2025/0135205)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL TAYLOR HOLTZCLAW whose telephone number is (571)272-6626. The examiner can normally be reached Monday-Friday (7:30 a.m.-5:00 p.m. EST).
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, Jennifer McDonald can be reached at (571) 270-3061. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL T. HOLTZCLAW/Primary Examiner, Art Unit 3796
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