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 .
Remarks
Claims 1-20, filed 05/29/2024, are currently pending and are under consideration.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a system and method for updating a patient-specific model. To determine whether a claim satisfies the criteria for subject matter eligibility, the claim is evaluated according to a stepwise process as described in MPEP 2106(III) and 2106.03-2106.04. The instant claims are evaluated according to such analysis.
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Claim 1 is directed to a system, and claim 13 is directed to a method, and thus meet the requirements for step 1.
Step 2A (Prong 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claims 1 and 13 recite a system and method for updating a trained patient-specific model comprising receiving conduction data from a portion of the user’s brain, project the conduction data through the patient-specific model, update a parameter of an electrical signal based on the brain state, and update the trained patient-specific model to include the brain state. The limitation of determining a probability a user will transition between sleep stages, as drafted in claims 1-20, under its broadest reasonable interpretation, covers performance of the limitation in the mind or using pen and paper. For example, updating a model in the context of this claim encompasses a user gathering conduction data, putting that data through a trained model, updating a parameter of an electrical signal based on the brain state, and updating the trained model to include the brain state.
Step 2A (Prong 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
The step of receiving conduction data is considered to be the pre-solution activity of data gathering by no more than routine means. The steps of projecting the conduction data through the patient-specific model, updating a parameter of an electrical signal based on the brain state, and updating the trained patient-specific model to include the brain state are considered to be data analysis steps.
The additional elements of the recording electrode, stimulating electrode, controller, memory, and processor are recited at a high level of generality (i.e., as generic computer components for inputting, processing, and storing data). The electrodes are generic structure for the insignificant, extra-solution activity of data gathering. Specifically, these additional elements are generically recited computing elements that perform the steps of gathering, analyzing, and outputting data.
Accordingly, these additional elements do no integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.04(a)(2)(III)(C).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
The additional elements when considered individually and in combination is 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 additional elements of the recording electrode, stimulating electrode, controller, memory, and processor amounts to no more than generically claimed computer components which enable the above-identified abstract idea to be conducted by performing the basic functions of automating mental tasks. The electrodes are generic structure for the insignificant, extra-solution activity of data gathering. Furthermore, the additional elements do not amount to more than generically linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Therefore, the claims are not patent eligible.
Claims 2-12 and 14-20 depend on claims 1 and 13 and recite the same abstract idea as claims 1 and 13 from which they depend. Further, these claims only contain recitations that further limit the abstract idea (that is, the claims only recite limitations that further limit the mental process). For example, the additional limitations recited in claims 2-12, 14, and 17-20 (i.e. further defining the processing steps) are further data analysis steps. The additional limitations recited in claims 15 and 16 (i.e. defining the neurological condition) is simply describing a condition of the patient. The additional elements individually do not amount to significantly more than the judicial exception explained above (the abstract idea). Looking at the limitations as a whole adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves any technology or includes a particular solution to a computer-based problem or a particular way to achieve a computer-based outcome. Rather, the collective functions of the claimed invention merely provides a conventional computer implementation, i.e. the computer (processor) is simply a tool to perform the claimed invention.
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.
Claims 1, 2, 6-10, and 12-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Genov et al. (US Patent Application Publication 2020/0397383), hereinafter Genov.
Regarding claim 1, Genov discloses a system for closed-loop neuromodulation to treat a condition of a brain (e.g. Abstract; Par. [0089]: closed loop neuromodulation), the system comprising:
at least one recording electrode configured to record conduction data from at least a portion of the brain (e.g. Par. [0119]: electrodes 150);
at least one stimulating electrode configured to apply an electrical signal, generated and configured by a generator, to at least another portion of the brain, wherein the electrical signal comprises at least one parameter (e.g. Par. [0119]: neurostimulator applying electronic signals, “the stimulation controller is a neurostimulator 114 connected to the patient's brain through one or more electrodes for delivering the electronic signal”); and
a controller in electrical communication with the at least one recording electrode and the generator (e.g. Par. [0119]), the controller comprising a non-transitory memory configured to store instructions and a trained patient-specific model and a processor configured to execute the instructions and the trained patient-specific model (e.g. Par. [0119]: memory units 104, machine learning module 108) to:
receive the conduction data at a time (e.g. Par. [0120]: receiving data);
project the conduction data through the trained patient-specific model to determine a brain state at the time (e.g. Par. [0119]: “The system 100 also includes a machine learning module 108 using the signals extracted by the feature extraction module 106 for brain state classification to detect pathological brain state.”);
update the at least one parameter of the electrical signal based on a propensity of the brain state at the time to cause an effect of the condition of the brain (e.g. Par. [0090]: using analysis of brain signals for feedback stimulation to implement a brief stimuli); and
update the trained patient-specific model to include the brain state at the time and an effect of the updated the at least one parameter of the electrical signal on the brain state at the time (e.g. Par. [0093]: model is incrementally trained).
Regarding claim 13, Genov discloses a method for closed-loop neuromodulation to treat a condition of a brain (e.g. Abstract; Par. [0089]: closed loop neuromodulation), the method comprising:
receiving, by a system comprising a processor, conduction data at a time from at least one recording electrode in communication with the processor, wherein the at least one recording electrode records conduction data from at least a portion of the brain (e.g. Pars. [0119]-[0120]: receiving data);
projecting, by the system, the conduction data at the time through a trained patient-specific model to determine a brain state at the time (e.g. Par. [0119]: “The system 100 also includes a machine learning module 108 using the signals extracted by the feature extraction module 106 for brain state classification to detect pathological brain state.”);
updating, by the system, at least one parameter of an electrical signal based on a propensity of the brain state at the time to cause an effect of the condition of the brain, wherein the processor is further in communication with at least a generator that generates the electrical signal and provides the electrical signal to at least one stimulation electrode that applies the electrical signal to at least another portion of the brain (e.g. Par. [0090]: using analysis of brain signals for feedback stimulation to implement a brief stimuli; Par. [0119]: neurostimulator applying electronic signals, “the stimulation controller is a neurostimulator 114 connected to the patient's brain through one or more electrodes for delivering the electronic signal”); and
updating, by the system, the trained patient-specific model to include the brain state at the time and an effect of the application of the updated the at least one parameter of the electrical signal on the brain state at the time (e.g. Par. [0093]: model is incrementally trained).
Regarding claims 2 and 17, Genov further discloses wherein the processor is configured to process the conduction data at the time into brain state data at the time, wherein the brain state data is compatible with the trained patient-specific model before being projected through the trained patient-specific model to determine the brain state at the time (e.g. Par. [0119]: feature extraction module to process the received data).
Regarding claims 6 and 14, Genov further discloses wherein the processor is configured to determine the propensity of the brain state at the time to cause the effect of the condition of the brain based on brain state data at the time being projected through the trained patient-specific model, wherein the trained patient-specific model is trained over brain state data from a previous time period of at least one hour (e.g. Par. [0165]: detecting a patient’s neurological event based on a model created from recorded data; Par. [0087]: data trends determined from a timeline of seconds to years).
Regarding claim 7, Genov further discloses wherein the propensity is determined by comparing the brain state data at the time to identified distinct brain state groupings with known outcomes in the trained patient-specific model (e.g. Par. [0003]: machine learning module to detect a physiological event, the machine learning model trained on labeled time-series data of known occurrences of the physiological event).
Regarding claim 8, Genov further discloses wherein the processor executes the instructions to train the trained patient-specific model using previous conduction data of the patient over a previous time period of at least one hour to form a multi-dimensional latent space with identified brain state groupings having known outcomes (e.g. Par. [0003]: machine learning module to detect a physiological event, the machine learning model trained on labeled time-series data of known occurrences of the physiological event; Pars. [0129]-[0130]: electrodes used to collect data, after at least 24 hrs of data collection, feature extraction is performed and a model is trained on that data).
Regarding claim 9, Genov further discloses wherein the previous conduction data of the patient over the time period comprises at least one channel of time series data, wherein the at least one channel corresponds to the at least one recording electrode (e.g. Par. [0130]).
Regarding claim 10, Genov further discloses wherein the brain state at the time corresponds to a propensity for a future seizure (e.g. Par. [0090]).
Regarding claim 12, Genov further discloses wherein the electrical signal provides a low-energy stimulation to the other portion of the brain (e.g. Par. [0090]: minimal stimulation provided).
Regarding claim 15, Genov further discloses wherein the condition of the brain is a neurological pathology (e.g. Par. [0088]: neurological pathology of epilepsy).
Regarding claim 16, Genov further discloses wherein the neurological pathology is epilepsy (e.g. Par. [0088]: neurological pathology of epilepsy).
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.
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 11 is rejected under 35 U.S.C. 103 as being unpatentable over Genov et al. (US Patent Application Publication 2020/0397383), hereinafter Genov, as applied to claim 10 above, and further in view of Harrer et al. (US Patent Application Publication 2019/0160287), hereinafter Harrer.
Regarding claim 11, Genov fails to specifically disclose wherein the propensity of the brain state at the time corresponds to a likelihood to cause a seizure on a day in the future if the electrical signal is not modulated. Harrer, in a similar field of endeavor, is directed toward seizure detection. Harrer discloses determining that the propensity of the brain state at the time corresponds to a likelihood to cause a seizure on a day in the future if the electrical signal is not modulated (e.g. Par. [0019]: seizure predictor system determines that signals indicate pre-seizure activity and determines a future time at which seizure activity is predicted to occur).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Genov to include the seizure prediction as taught by Harrer because doing so would allow the user to be prepared based on the warning of a potential future seizure.
While there are no prior art rejections for claims 3-5 and 18-20, the claims are not indicated as allowable due to the rejection of the claims under 35 U.S.C. 101, as explained above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Giftakis et al. (US 2012/0277618) is directed towards seizure probability.
Nelson et al. (US 2014/0358024) is directed towards patient state determination based on analysis of brain signals.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHREYA P ANJARIA whose telephone number is (571)272-9083. The examiner can normally be reached M-F: 8:00-5:00 EST.
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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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/SHREYA ANJARIA/Examiner, Art Unit 3796
/Jennifer Pitrak McDonald/Supervisory Patent Examiner, Art Unit 3796
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