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 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to “a system” comprising only ‘a plurality of reference environments’ and ‘an Application Programming Interface’. According to paragraph 30 of the Specification, a reference environment is simulated. Therefore it is directed to software. Application Programming Interface is also directed to software. The system of claims 1-7 is thusly directed to software per se. Software per se is considered non-statutory subject matter.
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 (i.e., changing from AIA to pre-AIA ) 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)(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.
Claim(s) 1-20 with an earliest effective filing date of 5/19/23 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Chong et al. (U.S. Publication No. 2023/0134609 published on 5/4/23 as cited on IDS).
With respect to claim 1, the Chong reference teaches a system comprising:
a plurality of reference environments each configured to provide a reinforcement learning task (trained to perform a task by virtual environment [paragraphs 71, 80, 118, and Figure 1]); and
an Application Programming Interface (API) configured to provide inputs to and receive outputs from a neuronal cell culture (the MEA interface uses an API to send and receive data [paragraphs 14, 24, 87, 96, and Figure 1]), wherein the inputs are configured to provide the neuronal cell culture information about the reference environments and a reward (data is provided to the cell culture including reward stimulus [paragraphs 24, 71, and 101]), and the outputs are configured to indicate an action taken by the neuronal cell culture as an agent (the actions of the neurons can be applied to the external world [paragraph 108 and Figure 8B).
With respect to claim 2, the Chong reference teaches all the limitations of claim 1 as described above. In addition, the Chong reference teaches that the reference environments are simulated environments on an electronic computing device (virtual environment [Figure 1]).
With respect to claim 3, the Chong reference teaches all the limitations of claim 1 as described above. Additionally, the Chong reference teaches that the reinforcement learning task is a goal-oriented behavioral task (the neural culture is trained to perform tasks [paragraph 71]).
With respect to claim 4, the Chong reference teaches all the limitations of claim 1 as described above. In addition, the Chong reference teaches that the API specifies a format of the inputs and a format of the outputs that is used by all of the plurality of reference environments (the MEA interface translates the inputs and outputs to the virtual environment [paragraphs 24, 68, 69, and 70]).
With respect to claim 5, the Chong reference teaches all the limitations of claim 4 as described above. Additionally, the Chong reference teaches that the API specifies a standard technique for providing signals to and detecting signals from input devices and sensors (the MEA interface translates the inputs and outputs to the virtual environment [paragraphs 24, 68, 69, and 70]).
With respect to claim 6, the Chong reference teaches all the limitations of claim 1 as described above. In addition, the Chong reference teaches that the reward is electrical stimulation, light stimulation, or chemical stimulation applied to the neuronal cell culture (the reward stimuli is electrical, optical, or chemical [paragraph 97]).
With respect to claim 7, the Chong reference teaches all the limitations of claim 1 as described above. Additionally, the Chong reference teaches that the neuronal cell culture and an interface configured to convey the inputs and outputs between an electronic computing device and the neuronal cell culture (the MEA interface uses an API to send and receive data [paragraphs 24 & 87]).
With respect to claim 8, the Chong reference teaches a system comprising:
a neuronal cell culture: an interface configured to communicatively couple the neuronal cell culture to an electronic computing device (paragraphs 87, 96, and Figure 1);
the electronic computing device comprising: a processing unit; a memory (paragraph 33 and Figure 1);
a first reference environment, implemented by the processing unit, that is configured to provide a first reinforcement learning task to the neuronal cell culture; and a second reference environment, implemented by the processing unit, that is configured to provide a second reinforcement learning task to the neuronal cell culture (one or more virtual environments [paragraph 34 and Figure 1] the neural culture is trained to perform tasks by virtual environments [paragraphs 71, 80, and 118]).
With respect to claim 9, the Chong reference teaches all the limitations of claim 8 as described above. In addition, the Chong reference teaches that the neuronal cell culture is a two-dimensional (2D) cell culture or a three-dimensional (3D) cell culture comprising differentiated embryonic stem cells or induced pluripotent stem cells (the MEA includes a 2D or 3D grid of excitation sites [paragraph 25]).
With respect to claim 10, the Chong reference teaches all the limitations of claim 8 as described above. Additionally, the Chong reference teaches that the interface comprises an input device configured to stimulate neurons in the neuronal cell culture and an output device configured to detect activation potentials of neurons in the neuronal cell culture (see paragraphs 56, 71, and claim 1).
With respect to claim 11, the Chong reference teaches all the limitations of claim 8 as described above. In addition, the Chong reference teaches that the electronic computing device further comprises an API that specifies a standard technique for providing inputs to and receiving outputs from the neuronal cell culture via the interface, wherein the API is the same for the first reference environment and the second reference environment (the MEA interface translates the inputs and outputs to the virtual environment [paragraphs 24, 68, 69, and 70]).
With respect to claim 12¸ the Chong reference teaches all the limitations of claim 8 as described above. Additionally, the Chong reference teaches that the electronic computing device further comprises a comparison module configured to present a comparison of results obtained by the neuronal cell culture on the first reinforcement learning task and on the second reinforcement learning task (the configuration with the highest score is selected [paragraph 135]).
With respect to claim 13, the Chong reference teaches all the limitations of claim 8 as described above. In addition, the Chong reference teaches that the electronic computing device further comprises a third reference environment, implemented by the processing unit, that is configured to provide a third reinforcement learning task to the neuronal cell culture (see paragraphs 71, 80, 118, and 135).
With respect to claim 14, the Chong reference teaches a method comprising:
training a neuronal cell culture to perform a first reinforcement learning task (neural culture is trained to perform tasks [paragraph 71]) by communicating with the neuronal cell culture using a standard protocol for providing inputs and receiving outputs via an interface connected to an electronic computing device (the MEA interface translates the input/output [paragraphs 24, 68-70, and Figure 1]);
recording a first performance of the neuronal cell culture on the first reinforcement learning task; training the neuronal cell culture to perform a second reinforcement learning task by communicating with the neuronal cell culture using the standard protocol for providing inputs and receiving outputs via the interface connected to the electronic computing device; and recording a second performance of the neuronal cell culture on the second reinforcement learning task (configurations with the highest score are selected [paragraph 135]).
With respect to claim 15, the Chong reference teaches all the limitations of claim 14 as described above. Additionally, the Chong reference teaches that the neuronal cell culture is a two-dimensional (2D) cell culture or a three-dimensional (3D) cell culture comprising differentiated embryonic stem cells or induced pluripotent stem cells (the MEA includes a 2D or 3D grid of excitation sites [paragraph 25]).
With respect to claim 16, the Chong reference teaches all the limitations of claim 14 as described above. In addition, the Chong reference teaches that the first performance or the second performance comprises success or failure at completing a reinforcement learning task, a level of competence at the reinforcement learning task (a score is based on how well a culture performs a task [paragraph 135]), or a speed of learning the reinforcement learning task.
With respect to claim 17, the Chong reference teaches all the limitations of claim 14 as described above. Additionally, the Chong reference teaches that a reward used to train the neuronal cell culture to perform the first reinforcement learning task is the same as the reward used to train the neuronal cell culture to perform the second reinforcement learning task (rewards are used to train the neural cultures [paragraphs 71 and 101]).
With respect to claim 18, the Chong reference teaches all the limitations of claim 17 as described above. In addition, the Chong reference teaches that the reward is electrical stimulation, light stimulation, or chemical stimulation applied to the neuronal cell culture (the reward stimuli is electrical, optical, or chemical [paragraph 97]).
With respect to claim 19, the Chong reference teaches all the limitations of claim 14 as described above. Additionally, the Chong reference teaches generating a comparison of the first performance of the neuronal cell culture on the first reinforcement learning task to the second performance of the neuronal cell culture on the second reinforcement learning task (the configuration with the highest score is selected [paragraph 135]).
With respect to claim 20, the Chong reference teaches all the limitations of claim 14 as described above. In addition, the Chong reference teaches training the neuronal cell culture to perform a third reinforcement learning task by communicating with the neuronal cell culture using the standard protocol for providing inputs and receiving outputs via the interface connected to the electronic computing device; and recording a third performance of the neuronal cell culture on the third reinforcement learning task (see paragraphs 71, 80, 118, and 135).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ortner et al. U.S. Publication No. 2025/0005348
Systems and techniques that facilitate neuronal activity modulation of artificial neural networks are provided. In various embodiments, an artificial neural network can comprise a set of base neuron populations that collectively generate, during an inferencing phase or a training phase of the artificial neural network, an inferencing task result based on a data candidate. In various aspects, the artificial neural network can comprise a control neuron population that is independent of the set of base neuron populations. In various instances, the control neuron population can modulate, during the inferencing phase or the training phase, neuronal activity of at least one base neuron population of the set of base neuron populations. In various cases, the control neuron population can modulate the neuronal activity of the at least one base neuron population by scaling one or more operands internally produced by the at least one base neuron population.
Akopyan et al. U.S. Publication No. 2016/0321537
Embodiments of the invention relate to a neural network circuit comprising a memory block for maintaining neuronal data for multiple neurons, a scheduler for maintaining incoming firing events targeting the neurons, and a computational logic unit for updating the neuronal data for the neurons by processing the firing events. The network circuit further comprises at least one permutation logic unit enabling data exchange between the computational logic unit and at least one of the memory block and the scheduler. The network circuit further comprises a controller for controlling the computational logic unit, the memory block, the scheduler, and each permutation logic unit.
Alvarez-Icaza Rivera et al. U.S. Publication No. 2015/0324684
Embodiments of the invention provide a neurosynaptic system comprising a delay unit for receiving and buffering axonal inputs, and a neural computation unit for generating neuronal outputs by performing a set of computations based on at least one axonal input received by the delay unit. The system further comprises a permutation unit for receiving external inputs to the system, and transmitting external outputs from the system. The permutation unit maps each external input received as either an axonal input to the delay unit or an external output from the system. The permutation unit maps each neuronal output generated by the neural computation unit as either an axonal input to the delay unit or an external output from the system. The neural computation unit comprises multiple electronic neurons, multiple electronic axons, and a plurality of electronic synapse devices interconnecting the neurons with the axons.
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/KRIS E MACKES/Primary Examiner, Art Unit 2153
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