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 .
Response to Arguments
Applicant’s amendment and arguments filed 6/15/2026, with respect to the rejections set forth in the Non-Final Rejection dated 3/24/2026 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Barsa (4,570,640) in view of Huang et al. “Kernel Based Algorithms for Mining Huge Data Sets”.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 80 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The Examiner was unable to find support in the originally filed disclosure for controlling a thermal stimulus to remain below a pain threshold while eliciting a neurological response as presently claimed.
Claims 71, 76, 77, 79-81, 83, 84, 87, 88, and 90-99 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. As note din MPEP §2161.01, “Similarly, original claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed.”
Regarding Claims 71 and 84, Applicant is claiming a machine learning algorithm that is trained to use “one or more extracted features from the cortical ERP” to “determine at least one of (a) a depth of the anaesthesia and (b) a spatial distribution of the anaesthesia across the plurality of stimulation sites; the spatial distribution comprising a dermatomal map of an anaesthesia effect”. However, Applicant’s specification only repeats the desired functional outcome of a machine learning algorithm that process ERP signals (original claim 71). The originally filed disclosure does not describe any process on how cortical ERPs are particularly used by the machine learning algorithm or any details regarding what features form cortical EROPs are even used to train the machine learning algorithm. The only paragraph in the disclosure that mentions using cortical ERPs to determine anesthesia depth is par. [0320] of the publication of the present application (PGPUB 2025/0009292), which states:
“According to some exemplary embodiments, in order to determine an anesthesia depth, a stimulation, for example an electric field, is delivered with a frequency of about 5 Hz, at block 104. In some embodiments, if an ERP signal, for example a D-SSEP signal is received in the brain, for example by recording a D-SSEP signal from cortical or sub-cortical locations at block 106, then a system monitoring anesthesia detects that a c-fiber transmitting thermal sensation is not anesthetized, and therefore optionally a depth of anesthesia is not sufficient.”
This section does not mention the use of a trained machine learning algorithm on cortical ERP signals as claimed and the disclosure does not set forth any details regarding what features of a cortical ERP signal correlate to determinations of anesthesia depth and/or spatial distribution of the anesthesia as claimed. Applicant has claimed and described an intended, functional result without the requisite details necessary to show how the result is particularly obtained.
Regarding Claim 83, Applicant is claiming a machine learning algorithm that outputs a pharmacodynamic profile of one or more anesthetic compounds used for said anesthesia and a time trend of the depth index and/or dermatomal map. However, Applicant’s specification only repeats the desired functional outcome of a machine learning algorithm that outputs the pharmacodynamic profile and time trend without setting forth any steps regarding how the algorithm is particularly trained/ pre-trained to obtain and produce said results. Additionally, the machine learning algorithm was previously defined as taking cortical potential responses an equating them to anesthesia depth or spatial distribution (see Claim 71). There is no mention of further training or incorporating anesthesia drug dosages, time trends, etc. The algorithm as claimed in Claim 83 does not have adequate written description support.
Likewise, regarding Claims 96 and 97, Applicant claims using machine learning to predict an anesthesia effect at a future point in time but dos not set forth any details regarding how the algorithm is particularly trained with cortical potential data (see Claims 71 and 84) to predict future effects of anesthesia as claimed. Applicant has only claimed an intended result without setting forth the requisite detail indicating how the result is achieved.
Claims 76, 77, 79-81, 87, 88,90-95, 98 and 99 are rejected due to their dependence on the rejected claims above.
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 71, 76, 77, 79-81, 83, 84, 87, 88, 90-92 and 94-99 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1
The claims are drawn to a method of determining an effect of anesthesia (claims 71, 76, 77, 79-81, 83, 94, 96 and 98) and a system for monitoring the effect of anesthesia (claims 84, 87, 88, 90-92, 94, 95, 97 and 99).
Step 2A, Prong 1
The claims recite the steps of determining at least one of a depth of anesthesia and a spatial distribution of anesthesia across a plurality of stimulation sites and generating an output based on the determined depth or a dermatomal map indicative of the spatial distribution. This limitation, given its broadest reasonable interpretation, amounts to a clinician looking at a set of data; mentally assessing the information and providing some sort of indication of a depth of anesthesia (such as a mild effect, heavy effect, etc.). The claim therefore recites a mental process abstract idea.
Step 2A, Prong 2
The claims do not include any additional elements that amount to integration of the abstract idea into a practical application.
Claims 71 and 84 include the additional elements of a stimulator that stimulates a plurality of stimulation sites while a patient is under anesthesia; measuring cortical event-related potentials from at least one electrode; extracting amplitude, latency or waveform morphology from the potentials; a machine learning algorithm; and control circuitry for implementing the algorithm. The stimulation provided by the stimulator merely sets up the environment in which data is gathered for the mental process and therefore only amounts to insignificant extra-solution activity (see example C in MPEP §2106.04(d)(2)). The electrode for gathering electrical potential signals and extracting amplitude, latency or morphology from the signals amounts to the insignificant extra-solution activity of mere data gathering. The machine learning algorithm and implementation via control circuitry is so generically claimed that it amounts to generic computer implementation of the abstract idea. Insignificant extra-solution activity and generic computer implementation do not amount to integration of the abstract idea into a practical application.
Step 2B
The claims do not include any additional elements that amount, alone or in combination, to significantly more than the abstract idea itself.
Claims 71 and 84 include the additional elements of a stimulator that stimulates a plurality of stimulation sites while a patient is under anesthesia; measuring cortical event-related potentials from at least one electrode; extracting amplitude, latency or waveform morphology from the potentials; a machine learning algorithm; and control circuitry for implementing the algorithm. The stimulation provided by the stimulator merely sets up the environment in which data is gathered for the mental process and therefore only amounts to insignificant extra-solution activity (see example C in MPEP §2106.04(d)(2)). The electrode for gathering electrical potential signals and extracting amplitude, latency or morphology from the signals amounts to the insignificant extra-solution activity of mere data gathering. The machine learning algorithm and implementation via control circuitry is so generically claimed that it amounts to generic computer implementation of the abstract idea. Insignificant extra-solution activity and generic computer implementation do not amount, alone or in combination, to significantly more than the abstract idea itself.
Claims 76, 77, 79, 80, 87, 88 and 90-92 only further define the initial stimulus which is insignificant, extra-solution activity.
Claim 81 recites an alert which amounts to insignificant, post-solution activity.
Claim 83 recites data output which amounts to insignificant, post-solution activity.
Claims 94-97 relate to the machine learning algorithm which is merely computer implementation of a process that mimics human thinking.
Claims 98 and 99 only relate to the insignificant extra-solution activity of data gathering.
The Examiner notes claim 93 is not rejected under 35 USC 101 as it contains additional elements that amount to integration of the abstract idea into a practical application. Namely, the claim controls the infusion rate of a pump using the determination of anesthesia depth and/or spatial distribution of the anesthesia across different dermatomes. By amending the independent claims to include the limitations of Claim 93, the rejection would be overcome.
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.
Claims 71, 76, 77, 79-81, 83, 84, 87, 88 and 90-99 are rejected under 35 U.S.C. 103 as being unpatentable over Barsa (4,570,640) in view of Huang et al. “Kernel Based Algorithms for Mining Huge Data Sets”.
Regarding Claims 71, 76, 77, 83, 87, 88 and 93-97, Barsa discloses a system having a stimulator for providing electrical stimulation to a plurality of sites (such as the back of a user that spans the S5-T2 region) with electrodes 55 (Fig. 10) while the body is under anesthesia (Abstract). The stimulation does include stimuli below a pain sensation threshold (the “A” level of stimuli results in no sensation and is ramped up from there to an “E” level which is a painful sensation, see cal. 20, line 64-col. 21, line 48). The Examiner notes the claim sues the open-ended transitional phrase “comprising” which does not limit the claim to only the steps claimed and instead only require the prior art to include the limitations claimed to anticipate. It does not preclude or exclude the delivery of other types of signals. Since Barsa discloses delivery of stimulation that does not case a sensation, Barsa meets the requirement of the claim to deliver stimuli below a pain sensation threshold. Barsa also discloses measuring cortical potentials (particularly magnitudes/amplitudes of the potentials, such as via EEG electrodes on the head, that constitute neural responses to the stimulation (col. 20, lines 25-30;col. 21, lines 53-60). A depth, degree and/or efficacy of anesthesia is then determined from readings such as the EEG readings and is used to control a pump for increasing an infusion rate of an anesthetic (col. 24, lines 19-41). Barsa does not disclose automating the decision making process with a machine learning algorithm.
However, Huang, concerned with the common problem of processing data from sensors for predicative purposes discloses utilizing machine learning algorithms pre-trained on feature measurements associated with different classes (binary +1 or -1 class for example which correspond to a positively identified condition and an absence of such a condition). This provides the benefit of quickly and efficiently quantify, in fine detail, complex processes that result in large amounts of data (pp 1-2). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device in the Barsa reference to include automating the decision making process with a pre-trained machine learning algorithm, as taught and suggested by Huang, for the purpose of quickly and efficiently quantify, in fine detail, complex processes that result in large amounts of data.
In regard to Claims 79 and 90, Barsa discloses wherein said stimulating comprises delivering an electric field to said body part using a stimulating electrode, wherein said delivered electric field has an intensity value in a range between 0.5-40 mA and/or a frequency value in a range between 1-4000 Hz. (see at least col. 18:21-35 of Barsa).
Regarding Claim 80, Barsa discloses wherein said stimulating comprises delivering a temperature stimuli. (see at least col. 10:20 of Barsa)
In regard to Claim 81, Barsa discloses outputting a signal (which could be construed as an alert signal) when inadequate anesthesia is determined (col. 24, lines 19-36).
Regarding Claims 91 and 92, Barsa discloses utilizing a plurality of electrodes 55 along the spine of a user to target each dermatome. Barsa discloses the types and shapes of electrodes and their dimensions are dictated by the use contemplated. (col. 14, lines 5-30). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to axially space the electrodes to account for each dermatome as claimed, since it has been held that where the general conditions of a claim are disclosed in the prior art, discovering the optimum or workable ranges involves only routine skill in the art. In re Aller, 105 USPQ 233.
In regard to Claims 98 and 99, Barsa discloses obtaining sensor data in response to stimulation but does not set forth the exact time window of detection. While the disclosure of Barsa implies simultaneous stimulation and response assessment (col. 16, lines 55-65; col. 20, line 15-col. 22, line 50) the exact time window is not disclosed. It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to detect a response within 300 ms of the applied stimuli, since it has been held that where the general conditions of a claim are disclosed in the prior art, discovering the optimum or workable ranges involves only routine skill in the art. In re Aller, 105 USPQ 233.
Conclusion
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/ALLEN PORTER/Primary Examiner, Art Unit 3796
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