Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/24/2026 has been entered. Claims 1-20 are pending, with Claims 10-20 being withdrawn from consideration.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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Is the Claim to a Process, Machine, Manufacture or Composition of Matter?
Claim 1 recites a process for compensating for disruptions at a human-machine interface. Thus, the claims are to a method, which is one of the statutory categories of invention.
Step 2A: Prong One: Does the Claim Recite an Abstract Idea?
Independent claim 1 recites:
A process for compensating for disruptions at a human-machine interface, the process comprising:
monitoring signal quality;
using adaptive machine learning decoders to determine deviations in the monitored signal quality, wherein the adaptive machine learning decoders employ recurring parameter updates when determining deviations in the monitored signal quality [the examiner finds that the foregoing underlined element recites mathematical concepts, and/or a mental process because they can be performed in the human mind]; and
mitigating for the determined deviations.
Step 2A: Prong Two: Does the Claim Recite Additional Elements That Integrate The Abstract Idea Into a Practical Application?
The elements that are not underlined above are the additional elements (i.e., “monitoring signal quality”, “using adaptive machine learning decoders” to determine the deviations, and “mitigating for the determined deviations”).
The examiner submits that each of the following additional elements does no more than generally link the use of the abstract idea to a particular technological environment or field of use because they are merely an incidental or token addition to the claim that does not alter or affect how the process steps of the abstract idea are performed. The monitoring step recites mere gathering of data for use in the abstract idea, and the mitigating step merely recites an insignificant application of a result of the abstract idea. The adaptive machine learning decoders merely recite use of generic computer hardware to perform the mathematical calculations of the determining step.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For example, there is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
Step 2B: Does the Claim Recite Additional Elements That Amount to Significantly More Than the Abstract Idea?
The examiner submits that the additional elements identified in Step 2A do not amount to significantly more than the abstract idea for the same reasons discussed above with respect to the conclusion that the additional elements do not integrate the abstract idea into a practical application. The additional elements identified in Step 2A are not unconventional or otherwise more than what is well-understood, routine, conventional activity in the field; and simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP § 2106.05(d).
Dependent Claims 2-9 are also not patent eligible. Dependent Claim 2-4 and 6-7 merely recite further details of the gathering of data for use in the abstract idea. Dependent Claim 5 merely recite further details of the mathematical concepts and/or mental process. Dependent Claims 8-9 merely recite further details of the insignificant application of a result of the abstract idea.
Claim Rejections - 35 USC § 103
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) 1, 2, 6, and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Laszlo et al (U.S. Pub. No. 2019/0196586, hereinafter “Laszlo”) in view of Zhang et al (U.S. Pub. No. 2018/0177619, hereinafter “Zhang”).
Regarding Claim 1, Laszlo teaches a process for compensating for disruptions at a human-machine interface (paragraph [0055]), the process comprising: monitoring signal quality (paragraph [0055], evaluating each of the plurality of signals to determine a quality of each of the plurality of signals); determining deviations in the monitored signal quality (paragraph [0055], selecting one or more of the signals based on their quality); and mitigating for the determined deviations (paragraph [0055], outputting an EEG signal corresponding to the selected signals).
Laszlo does not specifically teach using adaptive machine learning decoders to determine the deviations, wherein the adaptive machine learning decoders employ recurring parameter updates when determining the deviations in the monitored signal quality. However, Laszlo does teach determining deviations in signal quality (paragraph [0055], selecting one or more of the signals based on their quality). Further, Zhang teaches using adaptive machine learning decoders (paragraph [0055], predictive models equated to machine learning) to determine the deviations, wherein the adaptive machine learning decoders employ recurring parameter updates when determining the deviations in the monitored signal quality (paragraph [0058], updating decoding parameters based on changes in neural activity and decodable variables, detecting loss of a channel for a variable is equated to determining a deviation; see also paragraphs [0065]-[0066] and [0182]. It is further noted that, because the decoder of Zhang changes over time, this may be equated to using adaptive machine learning decoders, plural, as is recited in the claim). It would have been obvious to one skilled in the art before the effective filing date of the invention to use the adaptive decoders taught in Zhang in the signal monitoring system of Laszlo, in order to account for channel loss (see Zhang, paragraph [0058]) and to recalibrate the decoder over time, due to, e.g., inaccuracies in the initial calibration, or degradation or movement of the implant (see Zhang, paragraph [0066]).
Regarding Claim 2, Laszlo in view of Zhang teaches everything that is claimed above with respect to Claim 1. Laszlo further teaches wherein monitoring signal quality comprises monitoring signal quality in real-time (paragraph [0055], real-time).
Regarding Claim 6, Laszlo in view of Zhang teaches everything that is claimed above with respect to Claim 1. Laszlo further teaches wherein monitoring signal quality comprises monitoring signal quality while a user of the human-machine interface is at rest (paragraph [0184], system may be used in user’s everyday life, which would include periods in which the user is at rest; see also paragraph [0142], without any physical action from the user).
Regarding Claim 7, Laszlo in view of Zhang teaches everything that is claimed above with respect to Claim 1. Laszlo further teaches wherein monitoring signal quality comprises monitoring signal quality while a user of the human-machine interface is performing a motor task (paragraph [0184], system may be used in user’s everyday life, which would include motor tasks).
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Laszlo in view of Zhang and Litt et al (U.S. Pat. No. 6658287, hereinafter “Litt”).
Regarding Claim 3, Laszlo in view of Zhang teaches everything that is claimed above with respect to Claim 2. Laszlo does not specifically teach wherein monitoring signal quality comprises using statistical process control (SPC). However, Litt teaches use of SPC for monitoring of brain activity features in column 21, lines 44-54. It would have been obvious to one skilled in the art before the effective filing date of the invention to include the SPC of Litt in the system of Lazlo, in order to identify persistent deviation of a parameter outside of its control limits (see Litt, column 21, lines 52-54).
Claim(s) 4 is rejected under 35 U.S.C. 103 as being unpatentable over Laszlo in view of Zhang, Litt, and Caplan (WO-2008057365-A2).
Regarding Claim 4, Laszlo in view of Zhang and Litt teaches everything that is claimed above with respect to Claim 3. Laszlo further teaches wherein monitoring signal quality comprises: monitoring impedance (paragraph [0008]); monitoring channel correlations (paragraph [0163]); monitoring microelectrode array signal values (paragraph [0169], electrode array); monitoring identified units (paragraphs [0162]-[0163], each signal corresponds to a unit, i.e., a population of neurons, see paragraph [0003]); comparing the monitored impedance with a baseline impedance (paragraph [0008], threshold value); comparing the monitored channel correlations with a normal range of channel correlations (paragraph [0163]); comparing the monitored microelectrode array signal values with expected monitored microelectrode array signal values (paragraph [0127], signal values are compared to threshold); comparing the monitored identified units with expected identified units (paragraphs [0162]-[0163]); and monitoring signal-to-noise ratio (SNR) (paragraph [0046]).
Laszlo does not teach monitoring firing rate; and comparing the monitored firing rate with an expected firing rate. However, Caplan teaches, in paragraph [073], a signal analyzer that removes activity associated with neurons below a minimum threshold (equated to the claimed expected firing rate). It would have been obvious to one skilled in the art before the effective filing date of the invention to include the firing rate comparison of Caplan in the system of Laszlo, because neurons with lower firing rates may convey less useful information (see Caplan, paragraph [0073]).
Claim(s) 5 is rejected under 35 U.S.C. 103 as being unpatentable over Laszlo in view of Zhang, Litt, and Caplan.
Regarding Claim 5, Laszlo in view of Zhang, Litt and Caplan teaches everything that is claimed above with respect to Claim 4. Laszlo further teaches wherein determining deviations comprises: determining whether or not the monitored impedance deviates from a baseline impedance (paragraph [0008], threshold value); determining whether or not there are abnormal microelectrode array signal values (paragraph [0127], signal values are compared to threshold); determining whether or not there are abnormal identified units (paragraphs [0162]-[0163]); determining whether or not there is an abnormal channel correlations (paragraph [0163]); and determining whether or not there is an unexpected change in SNR (paragraph [0046]).
Laszlo does not specifically teach determining whether or not there is an abnormal firing rate. However, Caplan teaches, in paragraph [073], a signal analyzer that removes activity associated with neurons below a minimum threshold (equated to the claimed abnormal firing rate). It would have been obvious to one skilled in the art before the effective filing date of the invention to include the firing rate comparison of Caplan in the system of Laszlo, because neurons with lower firing rates may convey less useful information (see Caplan, paragraph [0073]).
Laszlo does not specifically teach using adaptive machine learning decoders to determine the deviations. However, Laszlo does teach determining deviations in signal quality (paragraph [0055], selecting one or more of the signals based on their quality). Further, Zhang teaches using adaptive machine learning decoders (paragraph [0055], predictive models equated to machine learning) to determine the deviations (paragraph [0058], updating decoding parameters based on changes in neural activity and decodable variables, detecting loss of a channel for a variable is equated to determining a deviation; see also paragraphs [0065]-[0066] and [0182]. It is further noted that, because the decoder of Zhang changes over time, this may be equated to using adaptive machine learning decoders, plural, as is recited in the claim). It would have been obvious to one skilled in the art before the effective filing date of the invention to use the adaptive decoders taught in Zhang in the signal monitoring system of Laszlo, in order to account for channel loss (see Zhang, paragraph [0058]) and to recalibrate the decoder over time, due to, e.g., inaccuracies in the initial calibration, or degradation or movement of the implant (see Zhang, paragraph [0066]).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Laszlo in view of Zhang and Chen et al (U.S. Pub. No. 2013/0338519, hereinafter “Chen”).
Regarding Claim 8, Laszlo in view of Zhang teaches everything that is claimed above with respect to Claim 1. Laszlo further teaches wherein mitigating for the determined deviations further comprises masking channels experiencing the determined deviations (paragraph [0169], ML algorithm identified signals that exceed threshold quality; signals that do not exceed threshold quality are excluded from output and are equated to masked channels). Laszlo does not specifically teach updating, automatically, a model to reassign weights of channels not masked. However, Laszlo does teach, in paragraph [0169], that weighted average of identified signals is used to compile output signal (i.e., weights are applied to channels that are not masked). Further, Chen teaches in paragraph [0089] updating the weights of input signals based on signal quality measurements in a signal quality measurement system. It would have been obvious to one skilled in the art before the effective filing date of the invention to include the updated weights of Chen in the system of Laszlo, in order to ensure that a patient parameter that is being monitored is of the highest quality and to improve diagnoses (see Chen, paragraph [0089]).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Laszlo in view of Zhang and Chakravarthy et al (U.S. Pub. No. 2020/0029911, hereinafter “Chakravarthy”).
Regarding Claim 9, Laszlo in view of Zhang teaches everything that is claimed above with respect to Claim 1. Laszlo does not specifically teach mitigating for the determined deviations further comprises issuing a warning for a user to stop use of the human-machine interface. However, Laszlo does teach a display that displays information to a user (paragraph [0148]). Further, Chakravarthy teaches in paragraphs [0027] and [0037] alerting a user to replace at least a portion (i.e., stop using the current portion) of a human-machine interface based on signal quality. It would have been obvious to one skilled in the art before the effective filing date of the invention to provide the alert of Chakravarthy via the display of Laszlo, in order to provide a way of communicating signal quality issues to a patient, and allow different types of signal quality issues to be communicated to the user (see Chakravarthy, paragraph [0037]).
Prior Art of Record
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure.
Even-Chen et al (U.S. Pub. No. 2017/0042440) teaches updating decoder parameters during operation of a brain-machine interface (see Abstract, and paragraphs [0025], [0060], and [0062]).
Response to Arguments
Applicant's arguments filed 4/13/2026 regarding the 101 rejections have been fully considered but they are not persuasive. On page 2 of the Remarks, Applicant reiterates the arguments from the previous Response dated 11/26/2025. The Examiner again submits that the claimed “adaptive machine learning decoders” are merely using generic computer hardware to detect the deviations; such decoders are broadly claimed and known in the art (see the updated 103 rejections above). Applicant further cites example 38, which states that because a claim does not recite a mathematical relationship, formula, or calculation, the claim does not recite a mental process because the steps are not practically performed in the human mind. However, the Examiner does not consider example 38 to be relevant to the language of Claim 1, because Claim 1 merely recites determining deviations in received data, which is easily performed in the human mind, by merely making a note of any deviations from a baseline, or any abnormal or unexpected values (see dependent Claim 5). Further, Claim 1 does not limit the claimed model to a neural network. It is further noted that determining deviations in received data may be performed using only simple observations or comparisons that a human can perform, and that a simple version of the claimed decoder could be implemented by a human using pen and paper.
Applicant goes on to argue on pages 2-3 that “adaptive machine learning decoders”, where “the adaptive machine learning decoders employ recurring parameter updates when determining the deviations in the monitored signal quality” integrates the claim into a practical application by improving performance for compensating for disruptions at a human-machine interface. The Examiner disagrees, because the claimed adaptive machine learning decoders are known in the art (see the updated 103 rejections above). Further, the parameter updates are very broadly claimed, and could easily be performed by a human (e.g., by changing a baseline value that is used to determine whether a signal is a deviation).
On page 3-4, Applicant goes on to argue that the claims are an improvement to a technological field. Again, the Examiner disagrees, because the claimed adaptive machine learning decoders are known in the art (see the updated 103 rejections above).
Applicant’s arguments, filed 4/13/2026 with respect to 35 USC § 103 have been considered and are persuasive regarding the newly added limitations. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of the Zhang reference as per the updated rejection above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CYNTHIA L DAVIS whose telephone number is (571)272-1599. The examiner can normally be reached Monday-Friday, 7am to 3pm.
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/CYNTHIA L DAVIS/Examiner, Art Unit 2857
/JORDAN L JACKSON/Primary Examiner, Art Unit 2857
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