CTNF 17/960,093 CTNF 89901 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Remarks In response to communications sent October 4, 2022, claim(s) 1-20 are pending in this application; of these claims 1 and 11 are in independent form. Priority The provisional patent application 63/251,934 filed October 4, 2021 is a different document from the full specification filed in application 17/960,093 on October 4, 2022. The examiner has not determined the filing date of each claim because the art applied in this office action was filed before both dates. Drawings 06-22-03 AIA The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference character “ 22 ” has been used to designate both a first instance of the element and a second instance of the element. The same is true of elements 24, 34, 42, 44, 153, 154, 214, 330 , and 452 . Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance. The Examiner suggests using designations such as 22a, 22b, etc. Information Disclosure Statement The Information Disclosure Statement(s) is/are acknowledged and the references contained therein have been considered by the Examiner. This includes the Information Disclosure Statements(s) filed on: May 18, 2023. One citation copy was not legible. Therefore, it is added to an PTO-892 in this office action. The citation is: Yakoub, Abraam M. "Cerebral organoids exhibit mature neurons and astrocytes and recapitulate electrophysiological activity of the human brain." Neural regeneration research 14.5 (2019): 757-761. Claim Analysis - 35 USC § 101 No rejection is made under 35 USC § 101 because most of the claims do not recite a judicial exception. Some of the claims recite a neural network, but the claims merely involve the neural network in order to physically integrate the computing device with electrophysiological device. Inventorship Analysis 07-20-02-aia AIA 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. This is important because: the provisional patent application lists only one inventor, the instant application has a different inventive entity as a set of inventors, and similar non-patent literature has various authors . See, for example: K. Voituik et al. 2021. Light-weight Electrophysiology Hardware and Software Platform for Cloud-Based Neural Recording Experiments. bioRxiv doi: https://doi.org/10.1101/2021.05.18.444685 (Version 1) Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-15-03-aia AIA Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 20250124250 A1 (“Com by ”) . As to claim 1, Comby teaches an electrophysiological recording and stimulation system comprising: an electrophysiological device coupled to neural tissue and configured to measure neural electrophysiological signals (Comby Para [0082]: electrophysiological clamps or electrodes traversing the host of neural cells as part of a 3D biological neural network) ; a computing device coupled to the electrophysiological device and configured to: receive neural electrophysiological signals from the electrophysiological device (Comby Para [0044]: the Readout Unit 130 that sensing output from the biological culture 120) ; and transmit neural stimulating signals to the neural tissue through the electrophysiological device based on the neural electrophysiological signals (Comby Para [0086]: transmit raw inputs to the stimulation unit as stimulation signals) . As to claim 2, Comby teaches the electrophysiological recording and stimulation system according to claim 1, wherein the computing device is further configured to execute software instructions embodying a simulated neural network (Comby Para [0018]: an automation controller using a neural network) . As to claim 3, Comby teaches the electrophysiological recording and stimulation system according to claim 2, wherein the computing device is configured to provide neural communication between the simulated neural network and the neural tissue (Comby Para [0097]: “functional interface may include one or more artificial neural networks as pre-processors and/or post-processors and the parameters may comprise weight values, choice of activation functions, and other parameters to achieve the learning of the signals and/or their classification) . As to claim 4, Comby teaches the electrophysiological recording and stimulation system according to claim 2, wherein the neural network is a reinforcement learning agent (Comby Abstract: “Pre-processing and post-processing of the BNN interface signals may further facilitate the training and reinforcement learning by the BNN”; BNN stands for Biological Neural Network) . As to claim 5, Comby teaches the electrophysiological recording and stimulation system according to claim 2, further comprising: a monitor configured to display a graphical user interface including a graphical representation of the simulated neural network and the neural tissue (Comby Para [0089]-[0090]: a user interface for control software that monitors the output of the biological neural network) . As to claim 6, Comby teaches the electrophysiological recording and stimulation system according to claim 1, wherein the electrophysiological device is a microelectrode array including a plurality of electrophysiological electrodes (Comby Para [0057]: an electrophysiological device that is a microelectrode array) . As to claim 7, Comby teaches the electrophysiological recording and stimulation system according to claim 6, wherein the neural tissue includes at least one organoid (Comby Para [0060]: a brain organoid) . As to claim 8, Comby teaches the electrophysiological recording and stimulation system according to claim 7, further comprising a scaffold disposed on the microelectrode array (Comby Para [0060]-[0061]: a scaffold adapted to the microelectronic components) . As to claim 9, Comby teaches the electrophysiological recording and stimulation system according to claim 8, wherein the scaffold is configured to hold a plurality of organoids in a plurality of wells (Comby Para [0123]-[0124]: Biological Neural Networks may be arranged in a biocomputing stack having a plurality of cultures arranged serially or stacked) . As to claim 10, Comby teaches the electrophysiological recording and stimulation system according to claim 9, wherein each organoid is connected to a plurality of neighboring organoids of the plurality of organoids (Comby Para [0123]: serial arrangement and interconnection within the Biological Computing Stack) . As to claim 11, Comby teaches a method for electrophysiological recording and stimulation, the method comprising: measuring neural electrophysiological signals of neural tissue through an electrophysiological device (Comby Para [0082]: electrophysiological clamps or electrodes traversing the host of neural cells as part of a 3D biological neural network) ; receiving neural electrophysiological signals from the electrophysiological device at a computing device (Comby Para [0044]: the Readout Unit 130 that sensing output from the biological culture 120) ; and transmitting neural stimulating signals to the neural tissue through the electrophysiological device based on instructions from the computing device (Comby Para [0086]: transmit raw inputs to the stimulation unit as stimulation signals) . As to claim 12, Comby teaches the method according to claim 11, further comprising: executing software instructions embodying a simulated neural network (Comby Para [0018]: an automation controller using a neural network) . As to claim 13, Comby teaches the method according to claim 12, further comprising: providing neural communication between the simulated neural network and the at least one organoid through the computing device (Comby Para [0097]: “functional interface may include one or more artificial neural networks as pre-processors and/or post-processors and the parameters may comprise weight values, choice of activation functions, and other parameters to achieve the learning of the signals and/or their classification; Comby Para [0060]: a brain organoid) . As to claim 14, Comby teaches the method according to claim 12, wherein the neural network is a reinforcement learning agent (Comby Abstract: “Pre-processing and post-processing of the BNN interface signals may further facilitate the training and reinforcement learning by the BNN”; BNN stands for Biological Neural Network) . As to claim 15, Comby teaches the method according to claim 12, further comprising: displaying a graphical user interface including a graphical representation of the simulated neural network and the neural tissue (Comby Para [0089]-[0090]: a user interface for control software that monitors the output of the biological neural network) . As to claim 16, Comby teaches the method according to claim 11, wherein the electrophysiological device is a microelectrode array including a plurality of electrophysiological electrodes (Comby Para [0057]: an electrophysiological device that is a microelectrode array) . As to claim 17, Comby teaches the method according to claim 16, wherein the neural tissue includes at least one organoid (Comby Para [0060]: a brain organoid) . As to claim 18, Comby teaches the method according to claim 17, wherein the electrophysiological device includes a scaffold disposed on the microelectrode array (Comby Para [0060]-[0061]: a scaffold adapted to the microelectronic components) . As to claim 19, Comby teaches the method according to claim 18, wherein the scaffold is configured to hold a plurality of organoids in a plurality of wells (Comby Para [0123]- [0124]: Biological Neural Networks may be arranged in a biocomputing stack having a plurality of cultures arranged serially or stacked) . As to claim 20, Comby teaches the method according to claim 19, wherein each organoid is connected to a plurality of neighboring organoids of the plurality of organoids (Comby Para [0123]: serial arrangement and interconnection within the Biological Computing Stack) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Osaki and Ikeuchi. 2021. Complex Activity and Short-term Memories in Reciprocally Connected Cerebral Organoids. bioRxiv doi: https://doi.org/10.1101/2021.02.16.431387 WO-2022154080-A1: similar to Osaki and Ikeuchi; has a provisional patent application filed in United States. K. Voituik et al. 2021. Light-weight Electrophysiology Hardware and Software Platform for Cloud-Based Neural Recording Experiments. bioRxiv doi: https://doi.org/10.1101/2021.05.18.444685 (Version 1) (Applicant’s own work) US 20190197393 A1: biological to electronic neural network US 20210334657 A1: biological neural network with grid US 12600943 B2: Innervated organoid US 20250117658 A1: biological neural network with grid Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jesse P Frumkin whose telephone number is (571)270-1849. The examiner can normally be reached Monday - Friday, 10-5 ET. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Olivia Wise can be reached at (571) 272-2249. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /JESSE P FRUMKIN/ Primary Examiner, Art Unit 1685 June 1, 2026 Application/Control Number: 17/960,093 Page 2 Art Unit: 1685 Application/Control Number: 17/960,093 Page 3 Art Unit: 1685 Application/Control Number: 17/960,093 Page 4 Art Unit: 1685 Application/Control Number: 17/960,093 Page 5 Art Unit: 1685 Application/Control Number: 17/960,093 Page 6 Art Unit: 1685 Application/Control Number: 17/960,093 Page 7 Art Unit: 1685 Application/Control Number: 17/960,093 Page 8 Art Unit: 1685 Application/Control Number: 17/960,093 Page 9 Art Unit: 1685 Application/Control Number: 17/960,093 Page 10 Art Unit: 1685
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