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 § 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 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)(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.
(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.
Claims 2-24 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Greger et al. (WO 2012/116232 A1; hereinafter “Greger”).
Regarding claim 2, Greger discloses a system for recording and processing neuronal signals, the system comprising: an array of electronic probes configured to be implanted into or placed on a brain of a subject, wherein the array of electronic probes is configured to collect one or more neuronal signals from the brain of the subject, and wherein the one or more neuronal signals comprises one or more action potentials (APs) or one or more local field potentials (LFPs) (e.g. ¶¶ 136-137); a neuronal signal decoder configured to extract one or more features of the APs or the LFPs (e.g. ¶¶ 65, 73, etc.); and a speech synthesizer configured to synthesize speech from the one or more extracted features of the APs or the LFPs (e.g. ¶¶ 65, 73, 87 – “…decode a word by receiving neural signals, e.g., local field potentials…”, etc.).
Regarding claim 3, Greger discloses the electronic probes comprise microelectrodes, and the array of electronic probes comprises a microelectrode array (e.g. ¶¶ 15 – “electrodes comprise micro-electrodes”).
Regarding claim 4, Greger discloses the electronic probes comprise electrocorticography (ECoG) probes (e.g. ¶¶ 55 – “electrocorticographic (ECoG) electrodes”)
Regarding claim 5, Greger discloses the one or more features of the APs or LFPs comprise speech-related features (e.g. ¶¶ 65, 73, etc.).
Regarding claim 6, Greger discloses the neuronal signal decoder is further configured to apply a filter to identify the one or more features of the APs or the LFPs (e.g. ¶¶ 83 – “filter for isolating electrical signals within a desired frequency bandwidth”).
Regarding claim 7, Greger discloses the filter comprises a band-pass filter (e.g. ¶¶ 105-107 – “signals were band-pass filtered to preserve frequencies between 0.3 Hz and 7.5 KHz”).
Regarding claim 8, Greger discloses the band-pass filter comprises a first order band-pass filter or a second order band-pass filter (e.g. ¶¶ 83 – where the described filter describes a first order band-pass filter).
Regarding claim 9, Greger discloses the band-pass filter comprises a resonant band-pass filter (e.g. ¶¶ 105-107).
Regarding claim 10, Greger discloses a length of each microelectrode of microelectrode array is between about 1 mm and about 8 cm (e.g. ¶¶ 55).
Regarding claim 11, Greger discloses a length of each microelectrode of the microelectrode array is less than about 1mm (e.g. ¶¶ 55, 129, etc.)
Regarding claim 12, Greger discloses the array of electronic probes is configured to be implanted into deep-tissue regions of the brain of the subject (e.g. ¶¶ 20).
Regarding claim 13, Greger discloses the one or more features of the APs or the LFPs comprise signal information meeting or exceeding a threshold (e.g. ¶¶ 87).
Regarding claim 14, Greger discloses the threshold is a predetermined threshold (e.g. ¶¶ 107-111 – where the predetermine threshold is set based on the spectrogram analysis).
Regarding claim 15, Greger discloses the threshold is a dynamic threshold (e.g. ¶¶ 107-111 – where the examiner considers the threshold to be dynamic based on the adjustment from the amplitude of the noise band and necessary frequencies to be filterered out).
Regarding claim 16, Greger discloses the neuronal signal decoder comprises one or more machine learning models (e.g. ¶¶ 83-87 – where the examiner considers processors decoding speech dynamically falls within a machine learning model).
Regarding claim 17, Greger discloses the one or more machine learning models comprises an autoencoder (e.g. ¶¶ 100-111).
Regarding claim 18, Greger discloses the speech synthesizer comprises one or more machine learning models (e.g. ¶¶ 83-87 – where the examiner considers processors decoding speech dynamically falls within a machine learning model).
Regarding claim 19, Greger discloses the neuronal signal decoder comprises a feature extraction module to perform the extraction of the one or more features of the APs or the LFPs (e.g. ¶¶ 63-65, 148, etc.).
Regarding claim 20, Greger discloses the feature extraction module transmits the extracted features of the APs or the LFPs to the speech synthesizer (e.g. ¶¶ 107 – “Raw neural data were first downsampled from 30,000 samples/sec to 3,000 samples/sec.”).
Regarding claim 21, Greger discloses the neuronal signal decoder further comprises a feature-event coalescence module configured to (i) receive output from the feature extraction module, and (ii) construct a model-based inference of neuronal activity based at least in part on the output from the feature extraction module (e.g. ¶¶ 55-59, 62, etc.).
Regarding claim 22, Greger discloses the one or more features of the APs or the LFPs are extracted from the one or more neuronal signals without requiring prior digitization of the one or more neuronal signals (e.g. ¶¶ 107 – “Raw neural data were first downsampled from 30,000 samples/sec to 3,000 samples/sec.”).
Regarding claim 23, Greger discloses each electrode of the array of electrodes is individually addressable (e.g. ¶¶ 65 – “…shows performance results for individual electrodes over the face motor cortex for different words…”).
Regarding claim 24, Greger discloses the speech synthesizer is located externally to the brain of the subject (e.g. ¶¶ 84-86 – where the speech processor or synthesizer is located in the computer, external to the body).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael D’Abreu whose telephone number is (571) 270-3816. The examiner can normally be reached on 7AM-4PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Hamaoui can be reached at (571) 270-5625. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL J D'ABREU/Primary Examiner, Art Unit 3796