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 . This action is responsive to the application filed on 05/08/2023. Claims 1-20 are presented in the case. Claims 1, 10 and 19 are independent claims.
Priority
Applicant's claim for the benefit of a Chinese Patent Application No. 202310403703.8, filed April 14, 2023 is acknowledged.
Information Disclosure Statement
The information disclosure statement submitted on 05/08/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-9 are directed to a method, claims 10-18 are directed to a device and claims 19-20 are directed to a product. Therefore, the claims are eligible under Step 1 for being directed to a process, a machine and a manufacture respectively.
Independent claims 1, 10 and 19:
Step 2A Prong 1:
Claims recite:
setting one or more of a software environment and a hardware environment based on the environment configuration in response to a confirmation on the neuromorphic use case and the environment configuration - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and setting a software environment and a hardware environment data based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper;
generating a software interface associated with the software environment and/or a hardware interface associated with the hardware environment - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating a software interface data based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper;
generating a use case interface in the neuromorphic computation based on the installed neuromorphic use case - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating a software interface data based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
receiving a request for generating a use case interface in the neuromorphic computation - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g));
retrieving, based on key information in the request, a neuromorphic use case and an environment configuration corresponding to the request from a neuromorphic use case repository - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g));
installing the neuromorphic use case based on the software interface and/or the hardware interface - the steps recited at a high level of generality, and amounts to mere data outputing which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
An electronic device, comprising: at least one processor; and a memory coupled to the at least one processor and having instructions stored therein, wherein the instructions, when executed by the at least one processor, cause the electronic device to perform actions; A computer program product, the computer program product being tangibly stored on a non-transitory computer-readable medium and comprising machine-executable instructions, wherein the machine-executable instructions, when executed by a machine, cause the machine to perform steps - These limitations amount to components of a general purpose computer that applies a judicial exception, by use of conventional computer functions (see MPEP § 2106.05(b)).
receiving a request for generating a use case interface in the neuromorphic computation - which is a well-understood, routine, conventional activity similar to receiving or transmitting data over a network described in MPEP 2106.05(d)(II).
retrieving, based on key information in the request, a neuromorphic use case and an environment configuration corresponding to the request from a neuromorphic use case repository - which is a well-understood, routine, conventional activity similar to receiving or transmitting data over a network described in MPEP 2106.05(d)(II).
installing the neuromorphic use case based on the software interface and/or the hardware interface - the steps recited at a high level of generality, and amounts to mere data outputing which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 2, 11 and 20:
Step 2A Prong 1:
Claims recite:
wherein one or more of the software environment and the hardware environment is set by a neuromorphic software-hardware simulation platform based on the environment configuration - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and setting the software environment and the hardware environment based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
the neuromorphic software-hardware simulation platform comprises a neuromorphic software platform, a neuromorphic software-hardware connection layer, and a neuromorphic hardware pool - the step recited at a high level of generality, and amounts to selecting a particular data source or type of data to be manipulated, which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
the neuromorphic software-hardware simulation platform comprises a neuromorphic software platform, a neuromorphic software-hardware connection layer, and a neuromorphic hardware pool - viewed individually or in combination, describes selecting a particular data source or type of data to be manipulated similar to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display described in MPEP § 2106.05(g).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 3 and 12:
Step 2A Prong 1: The claim recites the abstract ideas of claims 2 and 11.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
wherein the neuromorphic software platform comprises a plurality of neuromorphic software simulators for executing the neuromorphic use case in dedicated computing resources - the step recited at a high level of generality, and amounts to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
wherein the neuromorphic software platform comprises a plurality of neuromorphic software simulators for executing the neuromorphic use case in dedicated computing resources - the step recited at a high level of generality, and amounts to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 4 and 13:
Step 2A Prong 1: The claim recites the abstract ideas of claims 2 and 11.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
wherein the neuromorphic hardware pool comprises a plurality of pieces of neuromorphic hardware for executing the neuromorphic use case in the neuromorphic hardware - These limitations amount to components of a general purpose computer that applies a judicial exception, by use of conventional computer functions (see MPEP § 2106.05(b)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
wherein the neuromorphic software platform comprises a plurality of neuromorphic software simulators for executing the neuromorphic use case in dedicated computing resources - the step recited at a high level of generality, and amounts to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 5 and 14:
Step 2A Prong 1: The claim recites the abstract ideas of claims 2 and 11.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
wherein the neuromorphic software-hardware connection layer establishes a connection between the neuromorphic software platform and the neuromorphic hardware pool for executing the neuromorphic use case in the neuromorphic hardware pool via the neuromorphic software platform - the step recited at a high level of generality, and amounts to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
wherein the neuromorphic software-hardware connection layer establishes a connection between the neuromorphic software platform and the neuromorphic hardware pool for executing the neuromorphic use case in the neuromorphic hardware pool via the neuromorphic software platform - the step recited at a high level of generality, and amounts to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 6 and 15:
Step 2A Prong 1:
Claims recite:
combining one or more of a plurality of neuromorphic software simulators of the neuromorphic software platform with one or more of a plurality of pieces of neuromorphic hardware of the neuromorphic hardware pool to obtain one or more neuromorphic software-hardware simulation schemes - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating one or more neuromorphic software-hardware simulation schemes based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
obtaining, by comparing the respective neuromorphic software-hardware simulation scheme performance scores for the same neuromorphic use case, a neuromorphic software-hardware simulation scheme suitable for the neuromorphic use case - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and selecting a neuromorphic software-hardware simulation scheme suitable for the neuromorphic use case based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
running the neuromorphic use case in the one or more neuromorphic software-hardware simulation schemes to obtain neuromorphic software-hardware simulation scheme performance scores for the neuromorphic use case - the step recited at a high level of generality, and amounts to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
running the neuromorphic use case in the one or more neuromorphic software-hardware simulation schemes to obtain neuromorphic software-hardware simulation scheme performance scores for the neuromorphic use case - the step recited at a high level of generality, and amounts to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 7 and 16:
Step 2A Prong 1: The claim recites the abstract ideas of claims 1 and 10.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
building the neuromorphic use case repository by inputting existing neuromorphic use cases and environment configurations to the neuromorphic use case repository - the steps recited at a high level of generality, and amounts to mere data storing which is well known which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
building the neuromorphic use case repository by inputting existing neuromorphic use cases and environment configurations to the neuromorphic use case repository - which is a well-understood, routine, conventional activity similar to Storing and retrieving information in memory described in MPEP 2106.05(d)(II).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 8 and 17:
Step 2A Prong 1:
Claims recite:
selecting the neuromorphic use case and the environment configuration from a plurality of neuromorphic use cases and a plurality of environment configurations - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and selecting the neuromorphic use case and the environment configuration based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so the claims do not provide a practical application and is not considered to be significantly more. As such, the claims are ineligible.
Dependent claims 9 and 18:
Step 2A Prong 1: The claim recites the abstract ideas of claims 1 and 10.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
providing a service for the neuromorphic computation by running the use case interface - the step recited at a high level of generality, and amounts to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
providing a service for the neuromorphic computation by running the use case interface - the step recited at a high level of generality, and amounts to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
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 1, 7-9, 10, 16-18 and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by KIM et al. (hereinafter KIM), US 20210004666 A1.
Regarding independent claim 1, KIM teaches a method for generating a use case interface in neuromorphic computation ([0011] provided a neural network generation method for neuromorphic computing, including selecting at least one part of a brain corresponding to a neural network function requested to be generated by an application; determining whether an existing neural network corresponding to the at least one part of the brain is present in a neural network database; when it is determined that no existing neural network is present in the neural network database, generating new neural network configuration information corresponding to the part of the brain based on a brain information database; generating a new neural network by mapping the new neural network to neuromorphic hardware based on the new neural network configuration information), comprising:
receiving a request for generating a use case interface in the neuromorphic computation ([0061] Referring to FIG. 2, the neural network generation system 100 for neuromorphic computing includes a neural network configuration apparatus 120 and a neural network generation apparatus 130. The neural network configuration apparatus 120 may receive a neuromorphic neural network generation request 210 from an application of a neuromorphic system and generate new neural network configuration information);
retrieving, based on key information in the request, a neuromorphic use case and an environment configuration corresponding to the request from a neuromorphic use case repository ([0062] The neural network configuration apparatus 120 may include a brain configuration unit 230, a neural network DB determination unit 240, a neural network configuration unit 250, and a brain information DB 220. The brain configuration unit 230 may select at least one part of the brain corresponding to the neural network function requested to be generated by the application. The neural network DB determination unit 240 may determine whether an existing neural network corresponding to the at least one part of the brain is present in a neural network database (DB) 280; [0063] When the neural network generation request 210 is received, the brain configuration unit 230 may search for a neural network function desired to be used by the application, and may select at least one part of the brain corresponding to the neural network function. Here, the part of the brain may also be selected with reference to the information stored in the brain information DB 220; [0064] The neural network DB determination unit 240 may determine whether an existing neural network corresponding to the at least one part of the brain is present in the neural network DB 280. The neural network DB 280 is a database (DB) in which neural networks previously generated by the neural network generation apparatus 130 are stored. If it is determined that the existing neural network that is capable of performing the function required by the application is present, there is no need to configure and generate a new neural network. Thus, the neural network DB determination unit 240 first checks the neural network DB and needs to generate a new neural network only when no existing neural network is present. Therefore, the neural network DB determination unit 240 may send a signal instructing a new neural network to be configured to the neural network configuration unit 250 when no existing neural network is present, and may send a signal instructing an existing neural network to be loaded to the neural network DB 280 when there is the existing neural network);
setting one or more of a software environment and a hardware environment based on the environment configuration in response to a confirmation on the neuromorphic use case and the environment configuration ([0066] The brain information DB 220 may include brain subpart connection information, which is the information about connections between brain subparts which constitute each part of the brain, brain subpart configuration information, which is the information about the configuration of the brain subparts, neuron structure information, and neuron connection information. Therefore, the neural network configuration unit 250 may perform the operations of configuring the brain subparts based on the brain subpart connection information, establishing neuron sets which form the brain subparts based on the brain subpart configuration information, and configuring neurons which form the neuron sets based on the neuron structure information and the neuron connection information; [0068] That is, in order to configure a new neural network, the neural network configuration unit 250 loads information about subparts and neurons of the brain, which constitute the part of the brain, from the brain information DB. The subparts may be configured using the brain subpart connection information stored in the brain information DB, neuron sets may be assigned using the subpart configuration information, and respective neurons may be individually and specifically set using the neuron structure information and the neuron connection information; [0070] As a result, the neural network configuration unit 250 may collect information about neuron sets and neurons constituting each part of the brain from the brain information DB, and may then generate the new neural network configuration information; [0071] The new neural network configuration information generated by the neural network configuration unit 250 is transferred to the neural network generation apparatus 130. The neural network generation apparatus 130 may generate a new neural network by mapping the new neural network to the neuromorphic hardware based on the new neural network configuration information);
generating a software interface associated with the software environment and/or a hardware interface associated with the hardware environment ([0071] The new neural network configuration information generated by the neural network configuration unit 250 is transferred to the neural network generation apparatus 130. The neural network generation apparatus 130 may generate a new neural network by mapping the new neural network to the neuromorphic hardware based on the new neural network configuration information);
installing the neuromorphic use case based on the software interface and/or the hardware interface ([0072] The neural network generation apparatus 130 may include a ratio adjustment unit 260, a hardware mapping unit 270, and the neural network DB 280. The hardware mapping unit 270 may create a neural network graph corresponding to the new neural network configuration information, and may create a resource graph corresponding to hardware resource information. Further, the hardware mapping unit 270 may map the neural network graph to the resource graph using a graph-mapping algorithm. In this way, the hardware mapping unit 270 may generate a new neural network in the neuromorphic hardware. Also, the ratio adjustment unit 260 may further perform the operations of comparing the resource graph with the neural network graph and proportionately adjusting the neural network graph in conformity with the resource graph; [0073] That is, the generated neural network may be mapped to the neuromorphic hardware in accordance with the resource information of the neuromorphic hardware. The mapping algorithm used here may be an existing graph-mapping algorithm, and is intended to map the neural network graph to the resource graph; [0076] Further, when it is determined by the neural network DB determination unit 240 that the existing neural network is present in the neural network DB 280, the neural network DB determination unit 240 may send a request to load the existing neural network to the neural network DB 280, and the neural network DB 280 may load the existing neural network into the hardware mapping unit 270 in response to the request); and
generating a use case interface in the neuromorphic computation based on the installed neuromorphic use case ([0077] By means of this method, the hardware mapping unit 270 may output the neural network 290 generated by mapping the existing neural network or the new neural network, which corresponds to the neural network function requested to be generated by the application, to the neuromorphic hardware).
Regarding dependent claim 7, KIM teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. KIM further teaches comprising:
building the neuromorphic use case repository by inputting existing neuromorphic use cases and environment configurations to the neuromorphic use case repository ([0064] The neural network DB 280 is a database (DB) in which neural networks previously generated by the neural network generation apparatus 130 are stored; [0065] When the signal instructing a new neural network to be configured is received from the neural network DB determination unit 240, the neural network configuration unit 250 may generate the new neural network configuration information corresponding to the selected at least one part of the brain based on the brain information DB 220; [0075] The new neural network generated by the hardware mapping unit 270 may be stored in the neural network DB).
Regarding dependent claim 8, KIM teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. KIM further teaches wherein said confirmation on the neuromorphic use case and the environment configuration further comprises:
selecting the neuromorphic use case and the environment configuration from a plurality of neuromorphic use cases and a plurality of environment configurations ([0064] The neural network DB determination unit 240 may determine whether an existing neural network corresponding to the at least one part of the brain is present in the neural network DB 280. The neural network DB 280 is a database (DB) in which neural networks previously generated by the neural network generation apparatus 130 are stored. If it is determined that the existing neural network that is capable of performing the function required by the application is present, there is no need to configure and generate a new neural network … Therefore, the neural network DB determination unit 240 … may send a signal instructing an existing neural network to be loaded to the neural network DB 280 when there is the existing neural network).
Regarding dependent claim 9, KIM teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. KIM further teaches further comprising:
providing a service for the neuromorphic computation by running the use case interface ([0077] By means of this method, the hardware mapping unit 270 may output the neural network 290 generated by mapping the existing neural network or the new neural network, which corresponds to the neural network function requested to be generated by the application, to the neuromorphic hardware).
Regarding independent claim 10, it is a device claim that corresponding to the method of claim 1. Therefore, it is rejected for the same reason as claim 1 above. KIM further teaches an electronic device (Fig. 6, 600; [0103]-[0104]), comprising:
at least one processor (Fig. 6, 610; [0105]); and
a memory coupled to the at least one processor and having instructions stored therein, wherein the instructions, when executed by the at least one processor, cause the electronic device to perform actions (Fig. 6, 630; [0105]).
Regarding dependent claim 16, it is a device claim that corresponding to the method of claim 7. Therefore, it is rejected for the same reason as claim 7 above.
Regarding dependent claim 17, it is a device claim that corresponding to the method of claim 8. Therefore, it is rejected for the same reason as claim 8 above.
Regarding dependent claim 18, it is a device claim that corresponding to the method of claim 9. Therefore, it is rejected for the same reason as claim 9 above.
Regarding independent claim 19, it is a product claim that corresponding to the method of claim 1. Therefore, it is rejected for the same reason as claim 1 above. KIM further teaches a computer program product, the computer program product being tangibly stored on a non-transitory computer-readable medium and comprising machine-executable instructions, wherein the machine-executable instructions, when executed by a machine, cause the machine to perform steps ([0104]-[0105]).
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 2-6, 11-15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over KIM as applied in claims 1, 10 and 19, in view of Izhikevich et al. (hereinafter Izhikevich), US 20130073484 A1.
Regarding dependent claim 2, KIM teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. KIM does not explicitly disclose wherein one or more of the software environment and the hardware environment is set by a neuromorphic software-hardware simulation platform based on the environment configuration, and the neuromorphic software-hardware simulation platform comprises a neuromorphic software platform, a neuromorphic software-hardware connection layer, and a neuromorphic hardware pool.
However, in the same field of endeavor, Izhikevich teaches wherein one or more of the software environment and the hardware environment is set by a neuromorphic software-hardware simulation platform based on the environment configuration ([0043] The neural simulator development environment of FIG. 1 allows a user to define an arbitrary neural system model and to execute the model on an arbitrary computational platform (the engine). Neural simulator development 100 may comprise a number of software tools (transparent blocks in FIG. 1) that interact with each other via data structures configured in certain formats, and a computational engine 104, which can be embodied in a single computer, a computer cluster, GPU, or a specialized hardware), and the neuromorphic software-hardware simulation platform comprises a neuromorphic software platform, a neuromorphic software-hardware connection layer, and a neuromorphic hardware pool (Fig. 2; [0046] The Elementary Network Description (END) (i.e. a neuromorphic software-hardware connection layer) representation acts as an intermediary bottleneck (i.e., a link) between simulator tools and hardware platform implementations as illustrated in FIG. 2; [0049] The END description may also operate as a platform-independent link between a high-level description and the platform-specific implementation of the neural model, as illustrated in FIG. 2. In FIG. 2, blocks 210 (Neural simulators 1-3) denote various network development tools (such as, NEURON, GENESIS, NEST), while blocks 220 (Hardware platform 1-3) denote different hardware implementations (e.g., CPU, multiprocessor computers (workstations, desktop, server, mainframe, ASICs, FPGA, etc) that are used to execute the respective neural simulator models).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of a neural simulator development environment allowing a user to define an arbitrary neural system model and to execute the model on an arbitrary computational platform as suggested in Izhikevich into KIM’s system because both of these systems are addressing configuring a neuromorphic system. This modification would have been motivated by the desire to have parallel hardware architectures and corresponding languages that are optimized for parallel execution and simulation of neuronal models (Izhikevich, [0009]).
Regarding dependent claim 3, the combination of KIM and Izhikevich teaches all the limitations as set forth in the rejection of claim 2 that is incorporated. Izhikevich further teaches wherein the neuromorphic software platform comprises a plurality of neuromorphic software simulators for executing the neuromorphic use case in dedicated computing resources ([0049] In FIG. 2, blocks 210 (Neural simulators 1-3) denote various network development tools (such as, NEURON, GENESIS, NEST), while blocks 220 (Hardware platform 1-3) denote different hardware implementations (e.g., CPU, multiprocessor computers (workstations, desktop, server, mainframe, ASICs, FPGA, etc) that are used to execute the respective neural simulator models).
Regarding dependent claim 4, the combination of KIM and Izhikevich teaches all the limitations as set forth in the rejection of claim 2 that is incorporated. Izhikevich further teaches wherein the neuromorphic hardware pool comprises a plurality of pieces of neuromorphic hardware for executing the neuromorphic use case in the neuromorphic hardware ([0049] In FIG. 2, blocks 210 (Neural simulators 1-3) denote various network development tools (such as, NEURON, GENESIS, NEST), while blocks 220 (Hardware platform 1-3) denote different hardware implementations (e.g., CPU, multiprocessor computers (workstations, desktop, server, mainframe, ASICs, FPGA, etc) that are used to execute the respective neural simulator models).
Regarding dependent claim 5, the combination of KIM and Izhikevich teaches all the limitations as set forth in the rejection of claim 2 that is incorporated. Izhikevich further teaches wherein the neuromorphic software-hardware connection layer establishes a connection between the neuromorphic software platform and the neuromorphic hardware pool for executing the neuromorphic use case in the neuromorphic hardware pool via the neuromorphic software platform ([0046] The Elementary Network Description (END) representation acts as an intermediary bottleneck (i.e., a link) between simulator tools and hardware platform implementations as illustrated in FIG. 2. The END representation provides an abstraction layer that isolates developing environment from the underlying hardware).
Regarding dependent claim 6, the combination of KIM and Izhikevich teaches all the limitations as set forth in the rejection of claim 2 that is incorporated. Izhikevich further teaches further comprising:
combining one or more of a plurality of neuromorphic software simulators of the neuromorphic software platform with one or more of a plurality of pieces of neuromorphic hardware of the neuromorphic hardware pool to obtain one or more neuromorphic software-hardware simulation schemes ([0046] The END approach may operate to partition implementation of neural models (such as the model of FIG. 1) into two steps. At the first step, neuroscientists create neural models of varying complexity using high-level description language and END representation. At the second step, developers (programmers and hardware engineers) modify and adapt underlying implementation blocks to adapt and optimize model operation for a particular hardware/software platforms, etc.);
running the neuromorphic use case in the one or more neuromorphic software-hardware simulation schemes to obtain neuromorphic software-hardware simulation scheme performance scores for the neuromorphic use case ([0057] When implementing large-scale models of complex real-life systems such as, for example, a mammalian visual system, certain data structures described by the END format may consume the majority (in one example up to 99%) of the network model resources (memory or CPU, or both). Implementation of these data structures, typically referred to as "canonical structures", greatly benefits from the use of specialized hardware, such as an ASIC or FGPA optimized to simulate such canonical structures. Similarly, in some implementations where certain rules and methods consume majority of CPU processing resources (e.g., take the most time to execute), development of specialized hardware accelerators provides a substantial increased in processing of canonical methods. Different hardware implementations can hard-wire different methods, leading to a diversity of hardware platforms); and
obtaining, by comparing the respective neuromorphic software-hardware simulation scheme performance scores for the same neuromorphic use case, a neuromorphic software-hardware simulation scheme suitable for the neuromorphic use case ([0121] In order to achieve execution efficiency during model simulations, neuromorphic hardware implementing the computational engine typically has the following features: (i) fast highly specialized processing of neuronal dynamics and basic synaptic events, such as synaptic release; and (ii) a general purpose processor (e.g., an ARM core) for performing of computational background processes, such as slow synaptic update, turnover, rewiring, short-term plasticity, etc. Such configuration enables fast execution of the basic synaptic processing (that less likely requires frequent modifications by the user) for the majority of synapses while allowing for implementation of proprietary boutique processing for a smaller fraction of synapses).
Regarding dependent claim 11, it is a device claim that corresponding to the method of claim 2. Therefore, it is rejected for the same reason as claim 2 above.
Regarding dependent claim 12, it is a device claim that corresponding to the method of claim 3. Therefore, it is rejected for the same reason as claim 3 above.
Regarding dependent claim 13, it is a device claim that corresponding to the method of claim 4. Therefore, it is rejected for the same reason as claim 4 above.
Regarding dependent claim 14, it is a device claim that corresponding to the method of claim 5. Therefore, it is rejected for the same reason as claim 5 above.
Regarding dependent claim 15, it is a device claim that corresponding to the method of claim 6. Therefore, it is rejected for the same reason as claim 6 above.
Regarding dependent claim 20, it is a product claim that corresponding to the method of claim 2. Therefore, it is rejected for the same reason as claim 2 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
Okazawa et al. (US 11270191 B2) discloses a spiking neural network device is provided that includes a spiking neural network circuit, plural axons connected with the spiking neural network circuit, and plural Poisson spike generators respectively provided for the plural axons.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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/AMY P HOANG/Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143