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 .
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.
(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 1-17 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C 102(a)(2) as being anticipated by Martinot (U.S. Patent No. 11752327).
In regards to claim 1, Martinot discloses a system for automated sensing, control, and stimulation to treat sleep apnea in a patient, the system comprising: one or more sensors configured to generate a signal based on at least one physiological characteristic of a patient (Col. 14, lns. 11-16: "In particular, the present disclosure aims to provide transcutaneous electrical stimulation to muscles controlling the movement of the mandible of a subject to adjust their contribution to the sleep respiratory activity and reduce the subject's respiratory effort during or after sleep."), an input processor that is configured to:(a) determine, based on the generated signal, a set of input parameters out of multiple possible sets of input parameters to use in connection with detection of respiratory activity in the patient, and(b) determine, for each corresponding input parameter in the determined set of input parameters, a parameter value for the corresponding input parameter (Col. 55, lns. 41-44: "The relevant features were extracted from the corresponding MM raw signal sequences and were used as input data for the algorithm to determine whether they pertained to wake or sleep states."), a control processor configured to prescribe, based on the parameter values determined by the input processor for each of the input parameters in the set of input parameters, an action, out of a plurality of possible actions, to be taken for the patient (Col. 55, lns. 45-46: "The cheek-derived MM features were then used to best classify the chin-derived sleep/wake labels."), an output processor that is configured to:(1) determine, based on the determined action, one or more stimulation parameters to use out of a plurality of possible stimulation parameters, (2) determine, for each respective one of the one or more stimulation parameters, a value for the respective stimulation parameter to be used for modulation of respiratory activity of the patient (Col. 4, lns. 60-64: "In an embodiment the processing unit is configured to determine from the respiratory activity data a stimulation response and compare said stimulation response with a desired response, the desired response consisting of a decrease in the respiratory effort of a sleeping subjec"), implantable hardware to carry out the sensing, control, and stimulation functions using the parameter values in real time (Col. 28, lns. 33-38: "Additionally, the wearable device as described herein may also be used in combination with other systems or methods. These systems may be therapeutic in nature, such as a breathing apparatus (CPAP, BiPAP, Adaptive Support Ventilation), a device for stimulating specific nerves and/or other muscles, whether transcutaneous or implanted...").
In regards to claim 2, Martinot discloses that the set of sensors includes one or more of nerve action potential sensor, an electrical impedance sensor, an accelerometer, a gyroscope, an oxygen saturation sensor, and a pressure sensor (Col. 27, lns. 45-47: "The sensor may comprise at least one gyroscope configured for recording rotational movements of the subject's mandible.").
In regards to claim 3, Martinot discloses that the input signal processor includes one or more of an artificial neural network (ANN), convolutional neural network, generative adversarial network (GAN), auto-encoder, Bayesian optimizer, maximum likelihood estimator, linear support vector machine, and classification and regression tree (Col. 29, lns. 40-43: "In an embodiment the machine learning model may be selected from the list of extreme gradient boosting, deep neural network, convolutional neural network, random forest.").
In regards to claim 4, Martinot discloses that the input signal processor is trained to extract values from respiration waveforms by adjusting the parameter values of a model that is used to detect respiratory activity (Col. 57, lns. 42-48: "To reduce the computational complexity for the algorithmic analysis, it was envisaged to extract simple features from MM signals (standard deviation, maximum values, minimum values, mean, median, etc.) for each 30 seconds epoch of the signal and apply simple formulas to best classify cheek-derived MM signal into wake and sleep labels as defined by the reference chin-derived MM signal.").
In regards to claim 5, Martinot discloses that the values that are extracted as associated with are one or more of arousal, inhalation, exhalation, apnea, hypopnea, and apnea-hypopnea index (Col. 29, lns. 14-19: "The sleep quality parameters may include, e.g., total sleep time (TST), sleep onset latency (SOL), wake time after sleep onset (WASO), awakening or arousal index, sleep efficiency (SE), ratios of REM, non-REM sleep, REM sleep latency, and other sleep quality metrics.").
In regards to claim 6, Martinot discloses that the control processor includes is a decision algorithm that maintains a minimum respiratory rate or minimum oxygen saturation by driving the output processor (Col. 55, lns. 18-28: "The algorithm analysed the time series data from the cheek device in order to precisely identify sequential 30 seconds epochs of MM raw signals as wake or sleep, based on relevant and non-redundant features. Each 30 seconds epoch was summarized by 11 features extracted from the gyroscope norm: a. Standard deviation b. Minimum values and differences in minimum values in adjacent 30 sec windows c. Maximum values and differences in maximum values in adjacent 30 sec windows...").
In regards to claim 7, Martinot discloses that the output processor includes of one or more of an artificial neural network (ANN), convolutional neural network, generative adversarial network (GAN), auto-encoder, Bayesian optimizer, maximum likelihood estimator, linear support vector machine, and classification and regression tree (Col. 29, lns. 40-43: "In an embodiment the machine learning model may be selected from the list of extreme gradient boosting, deep neural network, convolutional neural network, random forest.").
In regards to claim 8, Martinot discloses that the output processor is trained to generate the values for electrical stimulation waveforms to modulate the physiological system of the patient by adjusting parameter values of the stimulator (Col. 57, lns. 42-48: "To reduce the computational complexity for the algorithmic analysis, it was envisaged to extract simple features from MM signals (standard deviation, maximum values, minimum values, mean, median, etc.) for each 30 seconds epoch of the signal and apply simple formulas to best classify cheek-derived MM signal into wake and sleep labels as defined by the reference chin-derived MM signal.").
In regards to claim 9, Martinot discloses that the plurality of possible stimulation parameters include stimulation amplitude, stimulation pulse width, stimulation frequency, number of pulses in a train, and train timing (Col. 20, lns. 61-66: "Accordingly, selection of the optimal stimulation parameters, including the current intensity (in mA), pulse frequency (in Hz), pulse width (in μs), and/or stimulation duration (e.g. continuous, intermittent, triggered), are important factors to promote efficient muscle response but avoid adverse effects.").
In regards to claim 10, Martinot discloses that the action includes one or more of: 1) stimulate efferents, 2) stimulate afferents; 3) set duration; 4) turn on; and/or 5) turn off (Col. 21, lns. 7-10: "In an embodiment the electrical current is a biphasic electrical current characterized by one or more one stimulation parameters, such as the biphasic current intensity, pulse frequency, pulse width, and/or stimulation duration.").
In regards to claim 11, Martinot discloses that the set of input parameters and the one or more stimulation parameters are determined before the implantable hardware is implanted in the patient and/or without the use of the implantable device (Col. 23, lns. 66-67 - Col. 24, lns. 1-3: "The stimulation intensity parameter may be determined via user input. In an embodiment, the stimulator may be configured for connecting to an input device, such as a smartphone, and receiving subject specific input from said input device.").
In regards to claim 12, Martinot discloses that the set of input parameters and the one or more stimulation parameters are determined before the implantable hardware is implanted in the patient and with the use of the implantable device (Col. 23, lns. 66-67 - Col. 24, lns. 1-3: "The stimulation intensity parameter may be determined via user input. In an embodiment, the stimulator may be configured for connecting to an input device, such as a smartphone, and receiving subject specific input from said input device.").
In regards to claim 13, Martinot discloses that the determined set of input parameters and the determined one or more stimulation parameters are configured to be downloaded to the implantable device (Col. 23, lns. 66-67 - Col. 24, lns. 1-3: "The stimulation intensity parameter may be determined via user input. In an embodiment, the stimulator may be configured for connecting to an input device, such as a smartphone, and receiving subject specific input from said input device.").
In regards to claim 14, Martinot discloses that the implantable device utilizes the determined set of input parameters and the determined one or more stimulation parameters for the generation and delivery of the therapy to the patient (Col. 24, lns. 21-26: "The stimulator may then calculate an optimal stimulation intensity parameter based on the reported discomfort and perception thresholds. Alternatively, the stimulator may also be selectively operable by a user to apply a stimulation intensity according to the user input.").
In regards to claim 15, Martinot discloses determining and updating the determined set of input parameters and the determined one or more stimulation parameters during the use of the implantable device to deliver the therapy while optimizing an objective function (Col. 30, lns. 35-38: "Analysis of the recorded data may be used to provide a feedback loop for the wearable device to improve control of the subject's mandible by adjusting one or more stimulation parameters and advantageously improve the device efficacy.").
In regards to claim 16, Martinot discloses that the determined set of input parameters and the determined one or more stimulation parameters are configured to be communicated with other devices through cloud or local storage systems (Col. 55, lns. 15-18: "The collected MM data were automatically transferred to a cloud-based infrastructure at the end of the night, and data analysis was conducted with a dedicated machine-learning algorithm.").
In regards to claim 17, Martinot discloses that parameters are configured to be received from one or more remote computing systems (Col. 23, lns. 66-67 - Col. 24, lns. 1-3: "The stimulation intensity parameter may be determined via user input. In an embodiment, the stimulator may be configured for connecting to an input device, such as a smartphone, and receiving subject specific input from said input device.").
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRYAN M LEE whose telephone number is (703)756-1789. The examiner can normally be reached 9:00 am - 6:00 pm.
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/B.M.L./Examiner, Art Unit 3796
/CARL H LAYNO/Supervisory Patent Examiner, Art Unit 3796
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