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 .
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 5/12/2025 has/have been acknowledged and is/are being considered by the Examiner.
Drawings
The Applicant is reminded to carefully review the drawing figures and the accompanying specification to ensure that all reference numerals present in the drawing figures are defined within the specification.
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 5 and 20 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 5 recites the limitation "the estimated response is determined using knowledge of an expected topology of a search, parameter or other space corresponding to the plurality of available stimulation sets, and the expected topology includes slopes and orientations, peaks, valleys, cliffs or plateaus in the search space" in lines 1-4. The metes and bounds of the claim are unclear is it is unclear what is meant by “topology” in the context of the claim, what is being referred to as “an expected topology of a search” or what is meant by knowledge in the context of the claim. Clarification is requested.
Claim 20 recites the limitation "the processing system is configured to determine the estimated response using knowledge of an expected topology of a search, parameter or other space corresponding to the plurality of available stimulation sets, and the expected topology includes slopes and orientations, peaks, valleys, cliffs or plateaus in the search space" in lines 1-4. The metes and bounds of the claim are unclear is it is unclear what is meant by “topology” in the context of the claim, what is being referred to as “an expected topology of a search” or what is meant by knowledge in the context of the claim. Clarification is requested.
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)(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 1-4 and 6-19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Sendi et al. (U.S. 2026/0233009), herein Sendi. Regarding claim 1, Sendi discloses a method, comprising: using a neurostimulator to use stimulation parameters to deliver electrical energy to tissue in a patient (“neuromodulation therapies are disclosed, where neuromodulation is defined as a neurosurgical treatment that modulates brain neural functioning by delivering an electrical signal using predefined stimulation parameters to a specific deep anatomical structure of the central nervous system”, paragraph [0024]); and using processing system to provide a stimulation effects map that maps stimulation effects for different stimulation parameters (“An objective of the disclosed approach is to find a best regression model (or map) between one or more stimulation parameters and a feature associated with a physical or neurological characteristic, also referred to herein as a biomarker”, paragraph [0026]) by: testing stimulation parameter sets from a plurality of available stimulation parameter sets by controlling the neurostimulator for each of the tested stimulation parameter sets to deliver the electrical energy using the corresponding stimulation parameter set, wherein the plurality of available stimulation parameter sets includes the tested stimulation parameter sets and a plurality of untested stimulation parameter sets (“In the first step (1), a set, m.sub.0, of modulation parameters may be randomly selected, i.e., x, and measure the response of the respective system to each modulation parameter of m.sub.0.”, paragraph [0036]); acquiring clinical effect data indicative of a patient response to the electrical energy delivered to the tissue using at least a first subset of the tested stimulation parameter sets (“body states” and “body features” described as a biomarker of corporal states in paragraph [0029]); acquiring sensed data indicative of a sensed response to the electrical energy delivered to the tissue using at least a second subset of the tested stimulation parameter sets (“electrophysiological features” described as a biomarker of corporal states in paragraph [0029]); and evaluating parameter sets from the plurality of untested stimulation parameter sets to provide evaluated parameter sets, including for each of the evaluated parameter sets, determine an estimated response by estimating at least one of the patient response or the sensed response to the electrical energy using the acquired clinical effect data and the acquired sensed data, wherein the stimulation effects map includes the acquired clinical effect data, the acquired sensed data, and the estimated responses for the evaluated parameter sets (“a model may be trained from the available data and use that to predict the neural response for untested stimulation parameters”, paragraph [0033]).
Regarding claim 2, Sendi discloses using the processing system to: choose, based on at least one or more of the estimated responses, a stimulation parameter set from the plurality of available stimulation parameter sets as a chosen stimulation parameter set to be tested; and control the neurostimulator to deliver electrical energy using the chosen stimulation parameter set (see paragraph [0034]).
Regarding claim 3, Sendi discloses that the chosen parameter set is selected from one of the evaluated parameter sets, the method further comprising acquiring a tested response, including at least one of clinical effect data or sense data, when the electrical energy is delivered using the chosen parameter set, comparing the acquired tested response to the corresponding one of the estimated responses to provide comparison data, and using the comparison data to update a model used to determine the estimated response (see paragraph [0034]).
Regarding claim 4, Sendi discloses that the estimated response is determined by interpolating or extrapolating, including by line or surface fittings of the tested stimulation sets (“A model may be fit between the modulation parameters to the target biomarker. Depending on the non-linearity of the effect of stimulation, linear or nonlinear regression models may be utilized.”, paragraph [0041]).
Regarding claim 6, Sendi discloses using machine learning for estimating at least one of the patient response or the sensed response to the electrical energy using the acquired clinical effect data and the acquire sensed data (“a machine learning approach is disclosed”, paragraph [0025]).
Regarding claim 7, Sendi discloses that the machine learning analyzes data from a current patient (“Upon receiving inputs relating to a patient and one or more biomarkers indicating a condition affecting the patient, a link between a treatment parameter and a response is identified.”, paragraph [0025]), data across multiple patients other than the current patient, or data across multiple patients including the current patient to estimate the at least one of the patient response or the sensed response.
Regarding claim 8, Sendi discloses at least one of: sensing an electrical response from the patient to the electrical energy delivered to the tissue, wherein the sensed data is indicative of the sensed electrical response; or sensing a physical characteristic for the patient, wherein the sensed data is indicative of the physical characteristic for the patient (“a corporal response may be any response to modulation of any body part, organ, or region, or the body as a whole”, paragraph [0027], where the identification of a “response” is considered to satisfy the broadest reasonable interpretation for sensing a response as claimed).
Regarding claim 9, Sendi discloses receiving a user input indicative of the patient response to the electrical energy delivered to the tissue, wherein the user input is indicative of at least one of: one or more side effects to the electrical energy delivered to the tissue; or whether and to what extent the electrical energy delivered to the tissue is therapeutically effective (“ the main inputs into the model or framework are the features or biomarkers of corporal states (e.g., body states, body features, electrophysiological features of different brain states, etc.) and their associated labels”, paragraph [0029]).
Regarding claim 10, Sendi discloses that the each of the plurality of available stimulation parameter sets includes an electrode configuration and a stimulation waveform configuration (“Modulation methods may include pharmacological and non-pharmacological modulation methods In one example, neuromodulation therapies are disclosed, where neuromodulation is defined as a neurosurgical treatment that modulates brain neural functioning by delivering an electrical signal using predefined stimulation parameters to a specific deep anatomical structure of the central nervous system.”, paragraph [0024]).
Regarding claim 11, Sendi discloses that both the clinical effect data and the sensed data are associated with at least two stimulation parameters in the plurality of available stimulation parameter sets (a regression model requires at least two data points and, therefore, there would be at least two stimulation parameters).
Regarding claim 12, Sendi discloses that both the clinical effect data and the sensed data are associated with a stimulation location and a stimulation amplitude (see paragraph [0026]).
Regarding claim 13, Sendi discloses that both the clinical effect data and the sensed data are further associated with at least one of: a stimulation frequency, a stimulation pulse width, a stimulation location and a stimulation amplitude (see paragraph [0026]).
Regarding claim 14, Sendi discloses that the sensed data includes features of a sensed signal, the method further comprising determining a difference between the estimated sensed response and the sensed signal, and using the determined difference to choose the stimulation parameter set as the chosen stimulation parameter set to be tested (“The method 100 may also comprise determining 106 a functional link between the biomarker and at least one modulation parameter using, for instance, an active learning framework. After finding the biomarker associated with neurological and neuropsychiatric disorders, the next step is to identify how different stimulation parameters may alter the biomarker. In other words, a regression model may be built between the inputs (i.e., stimulation parameters) and the brain outputs (i.e., target biomarkers) to understand the brain response to the stimulation. To this end, the method may include identifying a functional map between stimulation parameters and brain response. Identifying such a functional map (or regression model) between stimulation parameters and brain response aids in identifying the optimal neuromodulation control strategies. “, paragraph [0033]).
Regarding claim 15, Sendi discloses that the sensed data includes features of a sensed signal, the method further comprising determining a difference between the sensed signal and the estimated sensed response, determining the estimated patient response based on the determined difference and displaying the estimated patient response on a user interface (“As described herein, a novel algorithmic approach based on the active learning for optimal data collection and modeling the neurophysiological effects of neuromodulation is disclosed. Active learning, referred to as experimental design in statistics, is a subfield of machine learning and statistics and a smart solution for designing an experiment in which human decision-making is less than optimal for the task. The main rationale underpinning active learning is that data collection is costly, so these query points should be selected in a way such that it optimizes some notion of accuracy for a model being identified. More specifically, active learning is a paradigm in which machine learning models can direct the learning process by providing dynamic suggestions/queries for the “next-best experiment.”, paragraph [0034]).
Regarding claim 16, Sendi discloses a non-transitory machine-readable medium including instructions, which when executed by a machine, cause the machine to perform a method, comprising: using a neurostimulator to use stimulation parameters to deliver electrical energy to tissue in a patient (“neuromodulation therapies are disclosed, where neuromodulation is defined as a neurosurgical treatment that modulates brain neural functioning by delivering an electrical signal using predefined stimulation parameters to a specific deep anatomical structure of the central nervous system”, paragraph [0024]); and using processing system to provide a stimulation effects map that maps stimulation effects for different stimulation parameters (“An objective of the disclosed approach is to find a best regression model (or map) between one or more stimulation parameters and a feature associated with a physical or neurological characteristic, also referred to herein as a biomarker”, paragraph [0026]) by: testing stimulation parameter sets from a plurality of available stimulation parameter sets by controlling the neurostimulator for each of the tested stimulation parameter sets to deliver the electrical energy using the corresponding stimulation parameter set, wherein the plurality of available stimulation parameter sets includes the tested stimulation parameter sets and a plurality of untested stimulation parameter sets (“In the first step (1), a set, m.sub.0, of modulation parameters may be randomly selected, i.e., x, and measure the response of the respective system to each modulation parameter of m.sub.0.”, paragraph [0036]); acquiring clinical effect data indicative of a patient response to the electrical energy delivered to the tissue using at least a first subset of the tested stimulation parameter sets (“body states” and “body features” described as a biomarker of corporal states in paragraph [0029]); acquiring sensed data indicative of a sensed response to the electrical energy delivered to the tissue using at least a second subset of the tested stimulation parameter sets (“electrophysiological features” described as a biomarker of corporal states in paragraph [0029]); and evaluating parameter sets from the plurality of untested stimulation parameter sets to provide evaluated parameter sets, including for each of the evaluated parameter sets, determine an estimated response by estimating at least one of the patient response or the sensed response to the electrical energy using the acquired clinical effect data and the acquired sensed data, wherein the stimulation effects map includes the acquired clinical effect data, the acquired sensed data, and the estimated responses for the evaluated parameter sets (“a model may be trained from the available data and use that to predict the neural response for untested stimulation parameters”, paragraph [0033]).
Regarding claim 17, Sendi discloses a system, comprising: a neurostimulator configured to use stimulation parameters to deliver electrical energy to tissue in a patient (“neuromodulation therapies are disclosed, where neuromodulation is defined as a neurosurgical treatment that modulates brain neural functioning by delivering an electrical signal using predefined stimulation parameters to a specific deep anatomical structure of the central nervous system”, paragraph [0024]); and a processing system configured to provide a stimulation effects map that maps stimulation effects for different stimulation parameters (“An objective of the disclosed approach is to find a best regression model (or map) between one or more stimulation parameters and a feature associated with a physical or neurological characteristic, also referred to herein as a biomarker”, paragraph [0026]) by: testing stimulation parameter sets from a plurality of available stimulation parameter sets by controlling the neurostimulator for each of the tested stimulation parameter sets to deliver the electrical energy using the corresponding stimulation parameter set, wherein the plurality of available stimulation parameter sets includes the tested stimulation parameter sets and a plurality of untested stimulation parameter sets (“In the first step (1), a set, m.sub.0, of modulation parameters may be randomly selected, i.e., x, and measure the response of the respective system to each modulation parameter of m.sub.0.”, paragraph [0036]); acquiring clinical effect data indicative of a patient response to the electrical energy delivered to the tissue using at least a first subset of the tested stimulation parameter sets (“body states” and “body features” described as a biomarker of corporal states in paragraph [0029]); acquiring sensed data indicative of a sensed response to the electrical energy delivered to the tissue using at least a second subset of the tested stimulation parameter sets (“electrophysiological features” described as a biomarker of corporal states in paragraph [0029]); and evaluating parameter sets from the plurality of untested stimulation parameter sets to provide evaluated parameter sets, including for each of the evaluated parameter sets, determining an estimated response by estimating at least one of the patient response or the sensed response to the electrical energy using the acquired clinical effect data and the acquired sensed data, wherein the stimulation effects map includes the acquired clinical effect data, the acquired sensed data, and the estimated responses for the evaluated parameter sets (“a model may be trained from the available data and use that to predict the neural response for untested stimulation parameters”, paragraph [0033]).
Regarding claim 18, Sendi discloses that the processing system is configured to: based on the stimulation effects map including at least one or more of the estimated responses, choose a stimulation parameter set from the plurality of available stimulation parameter sets as a chosen stimulation parameter set to be tested; and control the neurostimulator to deliver electrical energy using the chosen stimulation parameter set (see paragraph [0034]).
Regarding claim 19, Sendi discloses that the processing system is configured to determine the estimated response by interpolating or extrapolating, including by line or surface fittings of the tested stimulation sets (“A model may be fit between the modulation parameters to the target biomarker. Depending on the non-linearity of the effect of stimulation, linear or nonlinear regression models may be utilized.”, paragraph [0041]).
Note: Examiner notes that no art has been applied to claim 5 and 20; however, the claims as currently presented are not deemed allowable and Applicant is required to clarify in compliance with 35 USC 112 so as to facilitate a clear understanding of the claimed invention and the protection sought.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See attached Notice of References Cited.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TAMMIE K MARLEN whose telephone number is (571)272-1986. The examiner can normally be reached Monday through Friday from 8 am until 4 pm.
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, Benjamin Klein can be reached at 571-270-5213. 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.
/TAMMIE K MARLEN/Primary Examiner, Art Unit 3796
Prosecution Insights