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 .
Election/Restrictions
Applicant’s election without traverse of Species B in the reply filed on November 17, 2025 is acknowledged.
Claims 1-14 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected species, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 11/17/2025.
Priority
Claims 15-20 are deemed to have an effective filing date of February 18, 2022.
Specification
The disclosure is objected to because of the following informalities: Paragraph [0056], lines 2-3, of the originally-filed specification recites “target firing response generate by Zilany model” which is awkward. The Examiner believes “generate” should be --generated--.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 15-17 and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US Patent Application Publication No. 2014/0355861 to Nirenberg et al. (hereinafter referred to as “Nirenberg”).
Referring to claims 15-16, Nirenberg discloses a method for neural implant processing (e.g., abstract, title, and paragraph [0014] where the neural implant is a retinal vision prosthesis), the method comprising: receiving, at a receiver of a neural implant, an input activation pattern (e.g., paragraphs [0014]-[0015], [0021], [0082]: retina prosthesis receives raw image data/stimuli); processing, by a front-end processing algorithm, the input activation pattern to produce a target population firing pattern for one or more neurons (e.g., paragraphs [0092]-[0094]: raw images are processed to determine information indicative of the retinal cell response to images (encoded) and the encoded information is output as a matrix of firing rates so that a firing rate retinal image is generated); and transforming, by a back-end processing algorithm, the target population firing pattern to a simulation pattern that induces a response with naturalistic timing (e.g., paragraphs [0014]: encoded information is transformed into signals via an interface where the signals activate a plurality of retinal cells/neurons with a high resolution transducer, and the activation of the retinal cells induces a response that is substantially similar to the time dependent responses of retinal ganglion cells from a normal retina to the same stimuli where the interface and transducer are considered a back-end processing algorithm).
With respect to claim 17, Nirenberg discloses the method of claim 15, wherein the front-end processing algorithm comprises a trained neural network (e.g., paragraphs [0130]-[0133]: encoder module 404 generates retinal images based on the raw training images using neural networks).
As to claim 19, Nirenberg discloses the method of claim 17, wherein the trained neural network is trained using clinical data, a phenomenological model, or both (e.g., paragraph [0016]: encoders use input/output models for retinal cells which were generated using data obtained from studies of the actual input/output response to a variety of stimuli).
With respect to claim 20, Nirenberg discloses the method of claim 17, wherein the trained neural network comprises one or more convolution layers for retinal prosthesis analysis (e.g., paragraph [0139] and Fig. 7, CNN layer 704).
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.
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 18 is rejected under 35 U.S.C. 103 as being unpatentable over Nirenberg in view of US Patent Application Publication No. 2022/0249844 to Alfsmann et al. (EFD 07/10/2020 and hereinafter referred to as “Alfsmann”).
Nirenberg discloses the method of claim 17, but does not expressly disclose that the trained neural network including CNNs is a trained recurrent neural network. However, Alfsmann, in a related art: system and methods for training a machine learning model for use by a processing unit in a medical implant, teaches that neural networks used in the medical arts includes convolutional neural networks (CNN) using recurrent networks (e.g., paragraph [0059] of Alfsmann). Accordingly, one of ordinary skill in the art would have recognized the benefits of employing a trained recurrent neural network in a medical implant in view of the teachings of Alfsmann. Consequently, one of ordinary skill in the art would have modified the method of Nirenberg so that its trained neural network is a trained recurrent neural network in view of the teachings of Alfsmann that such was a known engineering protocol in the medical implant art, and because the combination would have yielded a predictable result.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent Application Publication No. 2009/0030486 to Klefenz is directed to a method, device and computer program for generating a control signal for a cochlear implant, based on an audio signal where a front-end processing algorithm filters out activity events in an activity pattern (e.g., paragraphs [0072]-[0078]) and a back-end processing algorithm transforms the target firing pattern to a stimulation pattern (e.g., paragraph [0019]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CATHERINE M VOORHEES whose telephone number is (571)270-3846. The examiner can normally be reached Monday-Friday 8:30 AM to 4:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Unsu Jung can be reached at 571 272-8506. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CATHERINE M VOORHEES/Primary Examiner, Art Unit 3792
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