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 § 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 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites the step of converting the first biosignal corresponding to a low-frequency signal into a second biosignal corresponding to a high-frequency signal on the basis of a first neural network model.
The limitation of converting the first biosignal corresponding to a low-frequency signal into a second biosignal corresponding to a high-frequency signal on the basis of a first neural network model, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The limitation also covers mathematical concepts as a neural network is a model of equations. That is, other than reciting “a processor”, the claims are direct to concepts relating to organizing information in a way that can be performed mentally or analogous to human mental work and nothing in the claim element precludes the steps from practically being performed in the mind. For example, but for the processor, “converting” in the context of this claim encompasses the user manually calculating a signal transform or interpolating data points. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of obtaining a first biosignal. This is mere data gathering and amounts to insignificant extra-solutional activity, specifically pre-solutional activity. Additionally, the processor and neural network are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Similarly the dependent claims do not include additional elements that amount to significantly more. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept and well-understood, routine and conventional activity is not sufficient to amount to significantly more than the abstract idea itself. The claim is not patent eligible.
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.
Claims 1-16 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. The claims recite using a first and second neural network to convert the first signal into the second signal. The specification does support several architectures of neural networks but it also appears that a specific type of neural network with specific transformer layers are used not just any generic neural network. As the claim language is now the scope includes any neural network but it does not appear Applicants were in possession of such a device at the time of filing. The signals are converted using a specific neural network structure. It is similarly unclear where the specific support is for the neural network converting one type of signal to another like a motion signal to EMG.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 2 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. It is unclear if the “a neural network” in line 5 is the same or different than the first neural network model recited in claim 1.
Claims 2, 3, 10 and 11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The claims recite that the signals are converted to signals with improved sampling frequency. “improved” is a relative term of degree. It is not clear if this means the signals are upsampled or something else.
Claims 4-5 and 12-13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. It is unclear what the different types of signals are and the components that are then part of the first and second signal. It is unclear if the claim is stating that the first signal is a different type than the second or that the first and second both include components of different types of signals. Additionally, the claim states that the signals contain different types obtained from different parts of the human body which is unclear. The specification, at ¶78, gives an example of converting a noninvasive EEG signal to an invasive ECoG but these are not from different parts of the body really.
Claims 7 and 15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. It is unclear how the accelerometer signals are transformed to EMG signals.
Claims 8 and 16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. It is unclear what is meant by a predetermined ratio of first training data in each mini-batch of the training data. It is unclear what variables are in the ratio here or what the mini-batch is. It is also not clear what the one spike is.
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, 4, 5, 8, 9, 12, 13 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goodwin et al. “A practical approach to storage and retrieval of high-frequency physiological signals”.
Regarding claims 1 and 9, Goodman discloses a method of restoring a high-frequency biosignal, comprising:
loading a first biosignal by a processor ([pg. 3] physiological measurements are collected and sent to the processing device); and
converting, by the processor, the first biosignal corresponding to a low-frequency signal into a second biosignal corresponding to a high-frequency signal on the basis of a first neural network model ([pg. 11] the data is compressed in a low frequency format and then retrieved as a high-frequency signal). Goodman does not specifically disclose that the machine learning is neural networks, however; Goodman does disclose the use of its calculations with deep learning which is multilayered neural networks. Therefore, it would have been obvious to one of ordinary skill in the art prior to the time of filing to use neural networks or deep learning as Goodman already discloses the possibility of its use and it allows for the retrieval of high-frequency signals on architecture that cannot store the complex signals ([pg. 11]).
Regarding claim 4 and 12, Goodman discloses the first biosignal and the second biosignal include frequency band components of different types of biosignals ([pg. 9] some of the signals are composite containing different types of signals from different parts of the body).
Regarding claims 5 and 13, Goodman discloses the first biosignal and the second biosignal include frequency band components of different types of biosignals obtained from different parts of a human body ([pg. 9] some of the signals are composite containing different types of signals from different parts of the body).
Regarding claims 8 and 16, Goodman discloses the first neural network model is a machine learning-based learning model trained on the basis of training data in which input signals corresponding to low-frequency signals are labeled with ground truth (GT) signals corresponding to high-frequency signals, and first input signals corresponding to low-frequency signals are labeled on first GT signals corresponding to high- frequency signals in a predetermined ratio of first training data in each mini-batch of the training data, and each of the first GT signals includes at least one spike ([pg. 10] the machine learning is fed training data with true detection labels).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Liu et al. US 20180160917.
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/MICHAEL A CATINA/Examiner, Art Unit 3791 /TSE W CHEN/Supervisory Patent Examiner, Art Unit 3791