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 without traverse of Invention I pertaining to claims 35-49 in the reply filed on 29 October 2025 is acknowledged.
Claims 50-54 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to nonelected Invention II, there being no allowable generic or linking claim. Election was made without traverse in the reply.
Claim Objections
Claim 35 is objected to because of the following informalities: line 3 recites “first sympathetic nerve activity signal (SNA) signal”, wherein the double recitation of the term signal is considered redundant. Appropriate correction is required.
Claim 45 is objected to because of the following informalities: line 5 recites “first sympathetic nerve activity signal (SNA) signal”, wherein the double recitation of the term signal is considered redundant. 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) 35, 39-42, and 45 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Armitage et al. (WO 2020/240200 – disclosed by Applicant).
In regard to claim 35, Armitage et al. is directed to a method for modifying a therapeutic device setting (i.e., a closed loop cardiac control system 1), the method comprising:
receiving a first sympathetic nerve activity signal (SNA) signal, the first SNA signal being generated by an SNA sensor (para 62, the closed loop cardiac control system 1 receives neural data signals from the neural transducers 4 through the embedded electrical connectors 7. The received neural data signals are stored in the data store 33 and processed by the processing module 32 to provide processed neural data signals, which are then used to determine current cardiac function of the subject; para 59 and 69 teach that the neural signals relate to physiological activity of sympathetic cardiac nerves);
receiving a physiological endpoint (para 62, the processing module 32 then processes the determined current cardiac function of the subject and a predetermined desired cardiac function (read as the claimed physiological endpoint) obtained from the data store 33; para 64, the predetermined desired cardiac function may comprise a defined predetermined physiological response which may be one or more of a reduction in mean blood pressure or reduction of a component of blood pressure);
providing the first SNA signal and the physiological endpoint to a machine learning model trained to output a subset of the first SNA signal based on the physiological endpoint (para 62, the received neural data signals are stored in the data store 33 and processed by the processing module 32 to provide processed neural data signals, which are then used to determine current cardiac function of the subject. The processing module 32 then processes the determined current cardiac function of the subject and a predetermined desired cardiac function (i.e., physiological endpoint) obtained from the data store 33, such as a desired cardiac function bodily setpoint, to determine one or more output signals (i.e., the output signal here is considered to comprise the subset of the first SNA signal based on the predetermined cardiac function) which will affect the current cardiac function of the subject in a desired manner; para 68, the processing module 32 of controller 3 uses one or models, such as machine learning (ML) models, to determine the current cardiac function of the subject from the received neural data signals);
receiving the subset of the first SNA signal output from the machine learning model (para 62, the determined output signals are then sent by the output communications module 35 and received by the neural stimulators 5);
determining a first current physiological state based on the subset of the first SNA signal (para 62 and 64, the step is considered to be apparent from the output signals provided from processing module 33, wherein the output signal contains information regarding the current cardiac function compared to the predetermined cardiac function, wherein the outcome of the comparison is considered to be indicative of a physiological/cardiac state);
determining the therapeutic device setting based on the first current physiological state and the physiological endpoint (para 62 and 64, the output signal is used to determine therapeutic stimulation provided by neural stimulators 5 to either modify one or more cardiac function parameters of the subject towards the corresponding predetermined values or ranges of values as defined by the predetermined desired cardiac function, or to modify cardiac function of the subject to produce the desired physiological response); and
causing a therapeutic device to be modified based on the therapeutic device setting (para 67, when the neural stimulators 5 receive the output signals from the controller 3 through the embedded electrical connectors 8 the neural stimulators 5 provide neural stimulation to the at least one cardiac parasympathetic nerve of the Vagus nerve 62 based on the received output signals).
In regard to claims 39 and 40, Armitage et al. teach that the subset of the first SNA signal excludes portions of the first SNA signal collected during a blanking period wherein the blanking period corresponds to a duration of time during which the therapeutic device outputs an electrical impulse (para 138, the controller 3 may stop the recording of neural data by the affected ones of the neural transducers 4 for the duration of the neural stimulation signals, in order to prevent cross talk between the neural stimulators 5 and the neural transducers 4 reducing the quality of the received neural data. In some examples the affected neural data may be replaced by blanket zeros during the stimulation. In some examples the neural transducers 4 may be switched off or deactivated for the duration of the neural stimulation signals).
In regard to claims 41 and 42, Armitage et al. teach that the physiological endpoint is a target blood pressure, wherein the target blood pressure is one of a systolic blood pressure, diastolic blood pressure, or mean arterial pressure.
(para 62, the processing module 32 then processes the determined current cardiac function of the subject and a predetermined desired cardiac function (read as the claimed physiological endpoint) obtained from the data store 33; para 64, the predetermined desired cardiac function may comprise a defined predetermined physiological response which may be one or more of a reduction in mean blood pressure or reduction of a component of blood pressure
In regard to claim 45, Armitage et al. is directed to a system comprising (i.e., a closed loop cardiac control system 1): a memory 33 configured to store processor-readable instructions (para 61; data store 33; para 197 and 204 which discuss the data store and it ability as computer storage media for storage of computer readable instructions); and a processor 32 operatively connected to the memory, and configured to execute the instructions to perform operations that include (para 61, controller 3 comprises data store 33 and processing module 32, wherein module 32 comprises one or more processors; also see para 203 which discusses the controller and processor hardware structure): receiving a first sympathetic nerve activity signal (SNA) signal, the first SNA signal being generated by an SNA sensor (para 62, the closed loop cardiac control system 1 receives neural data signals from the neural transducers 4 through the embedded electrical connectors 7. The received neural data signals are stored in the data store 33 and processed by the processing module 32 to provide processed neural data signals, which are then used to determine current cardiac function of the subject; para 59 and 69 teach that the neural signals relate to physiological activity of sympathetic cardiac nerves); receiving a physiological endpoint (para 62, the processing module 32 then processes the determined current cardiac function of the subject and a predetermined desired cardiac function (read as the claimed physiological endpoint) obtained from the data store 33; para 64, the predetermined desired cardiac function may comprise a defined predetermined physiological response which may be one or more of a reduction in mean blood pressure or reduction of a component of blood pressure); providing the first SNA signal and the physiological endpoint to a machine learning model trained to output a subset of the first SNA signal based on the physiological endpoint (para 62, the received neural data signals are stored in the data store 33 and processed by the processing module 32 to provide processed neural data signals, which are then used to determine current cardiac function of the subject. The processing module 32 then processes the determined current cardiac function of the subject and a predetermined desired cardiac function (i.e., physiological endpoint) obtained from the data store 33, such as a desired cardiac function bodily setpoint, to determine one or more output signals (i.e., the output signal here is considered to comprise the subset of the first SNA signal based on the predetermined cardiac function) which will affect the current cardiac function of the subject in a desired manner; para 68, the processing module 32 of controller 3 uses one or models, such as machine learning (ML) models, to determine the current cardiac function of the subject from the received neural data signals); receiving the subset of the first SNA signal output from the machine learning model (para 62, the determined output signals are then sent by the output communications module 35 and received by the neural stimulators 5); determining a first current physiological state based on the subset of the first SNA signal (para 62 and 64, the step is considered to be apparent from the output signals provided from processing module 33, wherein the output signal contains information regarding the current cardiac function compared to the predetermined cardiac function, wherein the outcome of the comparison is considered to be indicative of a physiological/cardiac state; determining a therapeutic device setting based on the first current physiological state and the physiological endpoint (para 62 and 64, the output signal is used to determine therapeutic stimulation provided by neural stimulators 5 to either modify one or more cardiac function parameters of the subject towards the corresponding predetermined values or ranges of values as defined by the predetermined desired cardiac function, or to modify cardiac function of the subject to produce the desired physiological response); and causing a therapeutic device to be modified based on the therapeutic device setting (para 67, when the neural stimulators 5 receive the output signals from the controller 3 through the embedded electrical connectors 8 the neural stimulators 5 provide neural stimulation to the at least one cardiac parasympathetic nerve of the Vagus nerve 62 based on the received output signals).
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) 36-38 and 46-48 is/are rejected under 35 U.S.C. 103 as being unpatentable over Armitage et al. (WO 2020/240200 – disclosed by Applicant).
In regard to claims 36 and 46 (using claim 36 as exemplary), Armitage et al. is considered to describe the invention as claimed, however does not explicitly teach receiving a second SNA signal, the second SNA signal being generated by the SNA sensor; providing the second SNA signal and the physiological endpoint to the machine learning model; receiving a subset of the second SNA signal output by the machine learning model based on the second SNA signal and the physiological endpoint; and determining a second current physiological state based on the subset of the second SNA signal. Armitage et al. does teach that controller 3 is arranged to produce output signals substantially continuously based on the received neural data (para 145). Continuous production of output signals necessarily requires receiving successive neural signals, which reads on receiving a “second SNA signal” as claimed. Further, the Armitage et al. teaches that the controller 3 may provide output in response to identification of a predetermined event, wherein the predetermined event may be a signal such as blood pressure or heart rate (para 64) which is considered to comprise the physiological endpoint which is provided together with the neural data to determine the output signals. The collection of the second SNA signal, processing of a second subset of the second SNA signal, and determining a second current physiological state based on the second signals as claimed here is considered to be implied by the teachings in Armitage et al. which perform this analysis in real time, and on a continuous basis (para 37, 138, and 145). Operation in a continuous, real-time basis is considered to require successive measurements to repeatedly update the current state in order optimize the closed loop control and deliver therapy appropriate for the most recent state. Therefore, the limitations of this claim are considered to have been obvious to one of ordinary skill in the art since the teachings of Armitage et al. imply continuous, real-time updating of data in order to optimize closed loop control of therapeutic output.
In regard to claims 37 and 47 (using claim 36 as exemplary), Armitage et al. is considered to describe the invention as claimed, however does not explicitly teach training the machine learning model based on the first current physiological state and the second current physiological state. As discussed above, Armitage et al. teach continuous, real-time operation based on received neural data and use of predetermined events over time (para 145). Armitage et al. suggests use of historically determined patient states (e.g., the recited first physiological states) in order to train and update the model (para 133). Such continuous and iterative processing necessarily involves adapting controller behavior based on successive identified physiological conditions. This iterative adaptation is considered to read on the training or updating of the model based on the most currently determined physiological state. Training the machine learning model using the most recently, real-time determined physiological state, along with historical states is considered to have been obvious to one of ordinary skill in the art in order to improve the accuracy and responsiveness of the system to therapeutic needs.
In regard to claims 38 and 48 (using claim 36 as exemplary), Armitage et al. is considered to describe the invention as claimed, however does not explicitly teach determining an updated therapeutic device setting based on the second current physiological state and the physiological endpoint; and causing the therapeutic device to be modified based on the updated therapeutic device setting. Armitage et al. teaches that controller 3 produces output signal in response to identified neural events and predetermined cardiac events. These output signals are considered to necessarily correspond to control signals that modify operation of the therapeutic device based on the determined physiological condition. Because the controller 3 generates output signals responsive to the determined events (para 62-67) and does so on a continuous basis, in real time, Armitage et al. is considered to necessarily determining an updated therapeutic setting causing modification of the therapeutic device. Therefore, it is considered to have been obvious to one of ordinary skill in the art to update therapeutic settings based on recent data assessments (e.g., the second current physiological state) as routine in closed-loop therapy systems for delivering appropriate therapy.
Claim(s) 43 is/are rejected under 35 U.S.C. 103 as being unpatentable over Armitage et al. (WO 2020/240200 – disclosed by Applicant) in view of JP 2020521128 (English translation provided by USPTO).
In regard to claim 43, Armitage et al. is considered to substantially describe the invention as claimed, however does not teach that the SNA sensor is associated with a wearable device. Armitage et al. is implanted, however may receive information from external devices or sensors for use in closed loop therapy modification (para 63 and 154-156). The Japanese reference teaches a system that obtains data regarding a patient’s autonomic tone, particularly sympathetic nervous system activity to asses health of patient (page 3 of translation, 2nd full paragraph starting “data regarding the patient’s autonomic tone…”). The reference explicitly teaches that sympathetic nervous system activity may be collected using a wearable device and/or an implantable device (page 16, paragraph starting “the method may further…”). Therefore, modification of Armitage et al. to obtained sympathetic neural activity from a wearable device is considered to have been obvious to one of ordinary skill in the art since it is explicitly taught by the Japanese patent. The modification would comprise the application of a known technique to a known device to yield a predictable result.
Claim(s) 44 and 49 is/are rejected under 35 U.S.C. 103 as being unpatentable over Armitage et al. (WO 2020/240200 – disclosed by Applicant) in view of Libbus et al. (US Publication no. 2006/0106428).
In regard to claims 44 and 49 (using claim 44 as exemplary claim), Armitage et al. is considered to substantially describe the invention as claimed, however does not teach wherein receiving the first SNA signal further comprises: receiving a sensed SNA signal; amplifying the sensed SNA signal to generate an amplified sensed SNA signal; and processing the amplified sensed SNA signal using a band pass filter to generate the first SNA signal. Libbus et al. is directed to a device that performs cardiac rhythm management therapy by amplify, filter, record and analyze the target nerve activity, and use the resulting information to accurately and appropriately deliver CRM therapy such as cardiac resynchronization therapy (CRT). The target nerve activity is sympathetic nerve activity (SNA) signals, wherein SNA signals are received, amplified, and bandpass filtered to process neural traffic associated with SNA (para 30). It is therefore considered to have been obvious to one of ordinary skill in the art at the time of the invention to modify Armitage et al. to amplify and bandpass filter the neural signals obtained by neural transducers 4 since Libbus et al. demonstrate that this technique was explicitly known in the art at the time for processing sympathetic neural activity for use in closed loop cardiac therapy systems.
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 23 January 2026