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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 06/05/2024 and 07/17/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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)(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.
Claim(s) 17-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Schwemmer et al (pub # 20200282223).
Consider claim 17. Schwemmer et al teaches A method comprising:
providing, by at least one processor, a plurality of calibrated decoders corresponding to a plurality of neural activities, (paragraph 0028, Neural decoders are generally calibrated initially and updated periodically for each user and set of functions, which can require a considerable time commitment from the user. Calibration of the decoder typically requires the user to imagine performing several cued, or scripted, actions in a controlled sequence, so that the decoder can learn the mapping between patterns of neural activity and intended action from data where the intended action is explicitly known).
each of the plurality of calibrated decoders defined by a unique set of parameter values; (paragraph 0058, one set of approaches are aimed at using data collected during practical use in order to continuously adapt the parameters of the decoder).
and applying, by the at least one processor, a mapping function on an incoming neural activity excluded from the plurality of neural activities, to determine a decoder for the incoming neural activity, the decoder having a set of parameter values different from those of the plurality of calibrated decoders, (paragraph 0028, so that the decoder can learn the mapping between patterns of neural activity and intended action from data where the intended action is explicitly known, a process known as supervised learning.).
wherein the incoming neural activity corresponds to an action intended by a subject. (paragraph 0027, The decoder is responsible for inferring the user's intended action from a set of possible functions using the underlying neural activity.).
Consider claim 18. Schwemmer et al further teaches The method of claim 17, wherein the mapping function is configured to provide interpolation between parameter values of a defined parameter for at least two calibrated decoders. (paragraph 0036, The bar plots of FIG. 3a show the marginal accuracy histograms for each of the two decoders.).
Consider claim 19. Schwemmer et al further teaches The method of claim 17, comprising: applying, by the at least one processor, the mapping function on another incoming neural activity that is included in the plurality of neural activities, to identify a corresponding calibrated decoder for the another incoming neural activity, from the plurality of calibrated decoders. (paragraph 0028, so that the decoder can learn the mapping between patterns of neural activity and intended action from data where the intended action is explicitly known, a process known as supervised learning.).
Consider claim 20. Schwemmer et al further teaches The method of claim 17, comprising: applying, by the at least one processor, the determined decoder to translate the incoming neural activity into a control command for the action intended by the subject. (paragraph 0027, the algorithm that translates neural activity into control signals for assistive devices—is a key factor for various desired BCI characteristics. The decoder is responsible for inferring the user's intended action from a set of possible functions using the underlying neural activity.).
Allowable Subject Matter
Claims 1-16 are allowed.
The following is a statement of reasons for the indication of allowable subject matter:
Consider independent claim 1. Schwemmer et al (pub # 20200282223) teaches A method comprising:
determining, by at least one processor, a neural activity of a subject corresponding to an intended movement; (paragraph 0027, the algorithm that translates neural activity into control signals for assistive devices—is a key factor for various desired BCI characteristics. The decoder is responsible for inferring the user's intended action from a set of possible functions using the underlying neural activity.).
However neither Schwemmer et al nor any other prior art teaches or renders obvious, in combination with the rest of the limitations of the claim, applying, by the at least one processor in absence of using any kinematics information corresponding to the neural activity, a statistical test on the neural activity applied to each of a plurality of filter models, to identify a subset of the plurality of filter models that each passes the statistical test, each of the plurality of filter models defined by a unique set of parameter values; and determining, by the at least one processor using parameter values of the identified subset, a first set of parameter values to define a calibrated decoder for the neural activity.
Consider independent claim 9. Schwemmer et al (pub # 20200282223) teaches A system comprising:
at least one processor configured to:
determine a neural activity of a subject corresponding to an intended movement, (paragraph 0027, the algorithm that translates neural activity into control signals for assistive devices—is a key factor for various desired BCI characteristics. The decoder is responsible for inferring the user's intended action from a set of possible functions using the underlying neural activity.).
However neither Schwemmer et al nor any other prior art teaches or renders obvious, in combination with the rest of the limitations of the claim, apply, in absence of using any kinematics information corresponding to the neural activity, a statistical test on the neural activity applied to each of a plurality of filter models, to identify a subset of the plurality of filter models that each passes the statistical test, each of the plurality of filter models defined by a unique set of parameter values, and determine, using parameter values of the identified subset, a first set of parameter values to define a calibrated decoder for the neural activity.
Conclusion
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/CHAYCE R BIBBEE/Examiner, Art Unit 2624
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