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 .
Detailed Action
This action is in response to the papers filed June 1, 2026.
Election/Restrictions
Applicant’s reply filed 06/01/2026 to the Requirement for Restriction/Election mailed 03/30/2026 is acknowledged.
Applicant elected with traverse Invention I, claims 1-15, drawn to a computing system.
The traversal is on the grounds that claim 1 has been amended to recite that the electronic computing device is configured to perform a computational task based on a difference between the input signal and the output signal, and thus the amended product claims do not encompass the alternative uses provided in the restriction requirement. Applicant further argues there is no search burden because the claims, as amended, recite the same functional configurations, and differences in classification does not establish a serious burden. Applicant’s traversal is not found persuasive because the amendment to claim 1 was not present at the time of restriction. In addition, although the computing system may be configured to perform a computational task based on a difference between the input signal and the output signal, such a broadly-recited function in a product claim does not preclude using the claimed product in other, distinct methods of use. For example, the computing system may be configured to perform additional computational tasks beyond the intended use limitation of claim 1. Regarding search burden, Inventions I and II are distinct or unrelated inventions. A search for one distinct or unrelated invention would not be co-extensive with a search for another distinct or unrelated invention. Further, a reference rendering one distinct or unrelated invention as anticipated or obvious over the prior art would not necessarily also render another distinct or unrelated invention as anticipated or obvious over the prior art. Because these inventions are distinct for reasons given above, and because a search of one does not necessarily overlap with that of another, it would be unduly burdensome for the examiner to search and examine all the subject matter being sought in the presently pending claims and thus, restriction for examination purposes as indicated is proper. Thus, the requirement is still deemed proper and is therefore made final.
Claims 16-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected invention, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on 06/01/2026
Claim Amendments
Applicant’s amendment to the claims filed 06/01/2026 is acknowledged.
Claim 1 is amended.
Claims 1-20 are pending.
Claims 16-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected invention.
Claims 1-15 are under examination.
Priority
The instant application 18/367,392 was filed on 09/12/2023. This application claims priority based on U.S. Provisional Patent Applications 63/503,655 filed 05/22/2023; 63/503,406 filed 05/19/2023; and 63/503,400 filed 05/19/2023.
Effective filing dates: At least the following claim limitations are not found to possess sufficient written support in applicant’s priority documents: the electronic computing device is configured to perform a computational task based on a difference between the input signal and the output signal (claim 1), and the input signal and the output signal are provided and detected by one or more bidirectional electrodes at the same or different locations (claim 9). Accordingly, absent evidence to the contrary, the effective filing date of at least claims 1-14 is found to be 09/12/2023, based on the filing date of the instant application 18/367,392.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 01/16/2024, 08/19/2024, 03/19/2025, 01/05/2026 and 04/20/2026 have been considered.
The listing of references in the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, or by applicant in an information disclosure statement (IDS), they have not been considered.
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.
(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 1-9, 14-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2023/0134609 A1 to Chong et al.
Chong discloses a system comprising a computing device with in vitro biological neurons, wherein the system interfaces with a plurality of in vitro biological neurons by generating, by a processing device, a first tensor indicative of a state of a virtual environment; encoding the first tensor into an instruction; generating first signals according to the instruction using a first plurality of electrodes, one or more chemical emitters or one or more light sources; detecting second signals by a second plurality of electrodes, one or more chemical sensors or one or more image sensors, the second signals having been generated by one or more of the plurality of in vitro biological neurons, wherein the second signals represent an action associated with the virtual environment; decoding the second signals into a second tensor; and applying the action to the virtual environment based on the second tensor. See, Abstract. See also, fig. 4.
Chong further discloses the system is usable to perform in vitro training of biological neurons. In the biological neural network, data is encoded as spikes of action potentials of a population of biological neurons. The computing device acts as an encoder/decoder to generate signals that can be interpreted by biological neural networks and to decode output signals generated by the biological neural networks. See, e.g., par. 17, 20-21, 28, 70.
The virtual environment includes processing logic that may receive inputs from the MEA and/or external source and generate outputs, wherein the processing logic is a machine learning model, such as an artificial neural network, deep neural network, convolutional neural network, recurrent neural network (RNN), etc. See, e.g., par. 85.
The biological neurons are integrated with the computing device via a multi-electrode array (MEA). The MEA includes electrodes to provide stimulation of the neurons and sensors for detecting neuron activity. The same grid of electrodes can be used both for excitation of neurons and for measuring electrical activity of neurons responsive to such excitation. See, e.g., par. 22-25, 39, 45, and fig. 1.
The biological neurons may be cortical neuronal cells differentiated from human induced pluripotent stem cells or embryonic stem cells. See, e.g., par. 22, 29, 34-36. The biological neurons may be spread out on a 2D grid such that they do not overlap one another, or the biological neurons may be densely arranged to overlap one another and form a 3D structure, such as a neurosphere, in which multiple neurons may be stacked vertically in addition to being arranged on a 2D grid. See, e.g., par. 38.
The computing device further comprises a processing unit, a memory and a mass storage. See, e.g., par. 178-182, and fig. 9.
For these reasons, claims 1-9, 14-15 are anticipated by the cited prior art.
Claims 1-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2021/0334657 A1 to Jordan et al.
Jordan discloses a biological neural network (BNN) comprising a neural cell culture, an input stimulation unit (SU), and an output readout unit (RU) that may be controlled through its various life cycles to provide data processing functionality. The pre-processing and post-processing of the BNN interface signal may further facilitate the training and reinforcement learning by the BNN. See, Abstract.
In particular, Jordan discloses a system comprising an in vitro BNN core unit, an input SU adapted to apply an input signal into a first set of neural cells, and an output RU adapted to capture am output signal from a second set of neural cells. The system further comprises a controller comprising a pre-processing unit and/or post-processing unit to transform the input data signal into a stimulation signal and/or the readout signal into an output data signal using a machine learning algorithm, such as an artificial neural network or a reservoir computing method. See, e.g., par. 14-17, 38, 95, 100, 107, 124, and fig. 1, 8, 11. See also, par, 123 and fig. 10b, wherein pre-processing block P 1000 may facilitate the learning processing, and the post-processing block O 1010 may represent a topology for reservoir computing. Reservoir computing is an approach to recurrent neural network (RNN) design and training, and received input signals are mapped into a different computational space.
The neural cell culture is integrated into one or more multi-electrode arrays (MEA). See, e.g., par. 38-39, 50-51. The MEA is further describe as providing both the electrical stimulation (input signal) and measurement (output signal). See, e.g., par. 9.
The neural cells may be assembled through cell culture or organogenesis-like approaches and arranged in 2D or in 3D. The BNN may employ a neurosphere, neurovolume or monolayer system, or the BNN may be grown into a brain organoid See, e.g., par. 38, 54. Jordan further recognizes that the BNN may be generated out of embryonic stem cells or differentiated human induced pluripotent stem cells. See, e.g., par. 7.
The computer system comprises at least one processing unit, memory unit and mass storage unit. See, e.g., par. 89.
Regarding the requirement for a spiking neural network (SNN), in the biological neural networks (BNN), such as found in Jordan, data is encoded as spikes of action potentials of a population of biological neurons.
For these reasons, claims 1-15 are anticipated by the cited prior art.
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.
Claims 10-13 are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0134609 A1 to Chong et al., as applied to claims 1-9, 14-15 above; in further view of Lukoševičius et al. (2009) “Reservoir computing approaches to recurrent neural network training” Computer science review, 3(3), 127-149.
Chong discloses the computing system includes processing logic that may receive inputs from the MEA and/or external source and generate outputs. The computing system is configured such that the neuronal cell culture functions as a machine learning model, such as an artificial neural network or recurrent neural network (RNN). See, e.g., par. 85.
Lukoševičius is relevant prior art for disclosing an approach to recurrent neural network (RNN) design and training, referred to as reservoir computing (RC), which avoids the shortcomings of traditional gradient-descent RNN training, facilitates the practical application of RNNs, and outperforms classical fully trained RNNs in many tasks. See, e.g., Abstract, pg. 127-128, fig. 1.
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Therefore, prior to the effective filing date of the instantly claimed invention, it would have been prima facie obvious to one of ordinary skill in the art to modify the computing system of Chong by configuring the biological neural network to function as a reservoir in reservoir computing, in view of Lukoševičius, with a reasonable expectation of success because Chong discloses that the computing system may be configured as a recurrent neural network (RNN), and reservoir computing (RC) avoids the shortcomings of traditional gradient-descent RNN training, facilitates the practical application of RNNs, and outperforms classical fully trained RNNs in many tasks.
For these reasons, claims 10-13 would have been prima facie obvious over the cited references.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES J GRABER whose telephone number is (571)270-3988. The examiner can normally be reached Monday-Thursday: 9:00 am - 4:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James D Schultz can be reached at (571)272-0763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAMES JOSEPH GRABER/Examiner, Art Unit 1631
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