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 a responsive to the application filed on 11/18/2022.
Claims 1-20 are pending.
Claims 1-20 are rejected.
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.
Claims 1, 9, and 17 are respectively drawn to a system, method, and non-transitory computer readable storage medium, hence each falls under one of four categories of statutory subject matter (Step 1). Nonetheless, the claims are directed to a judicially recognized exception of an abstract idea without significantly more.
Claims 1, 9, and 17 recite the following, or analogous, limitations “obtaining an initial backbone network and a candidate set; replacing at least one basic unit in the initial backbone network with at least one placeholder module to obtain a to-be-determined network, wherein the candidate set comprises parameters of a plurality of structures corresponding to the at least one placeholder module; performing sampling based on the candidate set to obtain information about at least one sampling structure; obtaining a network model based on the to-be-determined network and the information about the at least one sampling structure, wherein the information about the at least one sampling structure determines a structure of the at least one placeholder module; and applying, when the network model meets a preset condition, the network model as a target…network”. These limitations, as claimed, under its broadest reasonable interpretation, can be evaluated in a human mind, except for the recitation of generic computer components (using artificial intelligence/machine learning, a computer including one or more microprocessors, and a non-transitory computer readable storage medium) (Step 2A). Other than reciting “target neural network”, “memory”, “a processor”, and “a non-transitory computer-readable storage medium” to perform the exceptions, nothing in the claims preclude the steps from practically being performed in the human mind. For example, a human expert can:
mentally/with the aid of pen and paper obtaining an initial backbone network and a candidate set (e.g. by thinking of/writing out remembering a template calculation and dataset of variables),
mentally/with the aid of pen and paper replacing at least one basic unit in the initial backbone network with at least one placeholder module to obtain a to-be-determined network, wherein the candidate set comprises parameters of a plurality of structures corresponding to the at least one placeholder module (e.g. by thinking of/writing out changing a variable in the calculation to create an updated calculation, and the dataset includes different variable sets corresponding to values),
mentally/with the aid of pen and paper performing sampling based on the candidate set to obtain information about at least one sampling structure (e.g. by thinking of/writing out selecting from the variable dataset to determine possible calculation architectures),
mentally/with the aid of pen and paper obtaining a network model based on the to-be-determined network and the information about the at least one sampling structure, wherein the information about the at least one sampling structure determines a structure of the at least one placeholder module (e.g. by thinking of/writing out an updated calculation architecture from the updated calculation and possible calculation architectures, including placement of the chosen variable),
mentally/with the aid of pen and paper applying, when the network model meets a preset condition, the network model as a target…network (e.g. by thinking of/writing out the updated calculation architecture being greater than a predetermined criteria value, and deploying the updated calculation architecture as the final model for use).
Thus, the claims recite a mental process (Step 2A, Prong 1).
Claims 1, 9, and 17 include additional elements, “target neural network”, “memory”, “a processor”, and “a non-transitory computer-readable storage medium”, however the recitations of these elements are at a high level of generality, and amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (i.e., “memory”, “a processor”, and “a non-transitory computer-readable storage medium”) (see MPEP 2106.05(f)); and generally linking the user of the judicial exception to a particular technological environment or field of use (i.e., “target neural network”) (see MPEP 2106.05(h)). Hence, each of the additional limitations or in combination do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (Step 2A, Prong 2; see MPEP 2106.05(f)). The additional elements in the claim do not amount to significantly more than an abstract idea. Furthermore, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of using “target neural network”, “memory”, “a processor”, and “a non-transitory computer-readable storage medium” to perform the steps of the independent claims amounts to no more than mere adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea; and generally linking the user of the judicial exception to a particular technological environment or field of use; as these cannot provide an inventive concept. (STEP 2B). As such, claims 1, 9, and 17 are not patent eligible.
Dependent claims 2-8, 10-16, and 18-20 are also ineligible for the same reasons given with respect to claims 1, 10, and 17. The dependent claims describe additional mental processes:
mentally/with the aid of pen and paper wherein after obtaining the network model based on the to-be-determined network and the information about the at least one sampling structure, the method further comprises: performing, when the network model does not meet the preset condition, resampling based on the candidate set; updating the information about the at least one sampling based on the resampling to obtain updated information; and updating the network model based on the updated information (claims 2, 10, and 18) (e.g. by mentally/writing out the updated calculation architecture being less than a predetermined criteria value, reselecting from the dataset variables, and updating the calculation architecture)
mentally/with the aid of pen and paper wherein before performing sampling based on the candidate set to obtain information about at least one sampling structure, the method comprises constructing a parameter space based on the candidate set, wherein the parameter space comprises architecture parameters corresponding to the parameters of the plurality of structures, and wherein performing sampling based on the candidate set to obtain information about at least one sampling structure comprises performing sampling on the parameter space to obtain at least one group of sampling parameters corresponding to the at least one sampling structure (claims 3, 11, and 19) (e.g. by mentally/writing out a collection of dataset variables corresponding to values and selecting a subset of relevant architecture variables for updating the calculation architecture)
mentally/with the aid of pen and paper wherein obtaining the network model based on the to-be-determined network and the information about the at least one sampling structure comprises converting the structure of the at least one placeholder module in the to-be-determined network based on the at least one group of sampling parameters in order to obtain the network model (claims 4, 12, and 20) (e.g. by mentally/writing out the updated calculation architecture from the updated calculation and possible calculation architectures, including placement of the chosen variable conformed to the selected variables)
mentally/with the aid of pen and paper wherein before obtaining the network model based on the to-be-determined network and the information about the at least one sampling structure, the method further comprises constructing the plurality of structures based on the candidate set and the to-be-determined network, wherein the plurality of structures forms a structure search space, and wherein obtaining the network model based on the to-be-determined network and the information about the at least one sampling structure comprises searching the network model from the structure search space based on the at least one group of sampling parameters (claims 5 and 13) (e.g. by thinking of/writing out multiple updated architectures for the calculation from the selected variables in the collected dataset)
mentally/with the aid of pen and paper wherein the preset condition comprises one or more of the following: a quantity of times of obtaining the network model exceeds a preset quantity of times, a duration for obtaining the network model exceeds preset duration, or an output result of the network model meets a preset requirement (claims 6 and 14) (e.g. by thinking of/writing out the predetermined criteria value includes the maximum output error from an expected value)
mentally/with the aid of pen and paper wherein the candidate set comprises one or more of the following: a type of an operator, attribute information of an operator, or a connection mode between operators (claims 7 and 15) (e.g. by thinking of/writing out the selected variables include mathematical functions utilized by the calculation)
mentally/with the aid of pen and paper wherein the target…network is for performing at least one of picture recognition, semantic segmentation, or object detection (claims 8 and 16) (e.g. by thinking of/writing out the calculation outputs a label regarding an input image)
Again, the dependent claims continued to cover the performance of the limitation in the mind as inherited from the independent claims (Step 2A, Prong 1). The dependent claims 8 and 16 recitation of “target neural network” is again recited at a high level and amounts to generally linking the user of the judicial exception to a particular technological environment or field of use, and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (Step 2A, Prong 2). The additional element in the claims do not amount to significantly more than an abstract idea. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements to perform the steps of in the dependent claims and perform the steps of the claims amount to no more than generally linking the user of the judicial exception to a particular technological environment or field of use, and this cannot provide an inventive concept. (STEP 2B). As such, dependent claims 2-8, 10-16, and 18-20 additional elements or combination of elements do not amount to significantly more than an abstract idea nor provide any inventive concept, nor impose a meaningful limit to integrate the elements into a practical application or significantly more than the judicial exceptions; therefore, the dependent claims are not deemed patent eligible.
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)(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 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Chen et al, (“DetNAS: Backbone Search for Object Detection”, 2019), hereinafter Chen.
Regarding claims 1, 9, and 17, Chen teaches a method; a neural network construction apparatus, comprising: a memory configured to store program instructions; and a processor coupled to the memory and configured to execute the program instructions to cause the neural network construction apparatus to; and a non-transitory computer-readable storage medium storing a computer program, that when executed by a processor (sections 3.1-3.2 and 4 teach performing the embodiments of the disclosure on one or more “GPU” communicatively coupled to one or more “memory” known to be included in a computer system), cause an apparatus to:
obtaining an initial backbone network and a candidate set (sections 1 and 3.2-3.3 teach a “backbone network, DetNASNet” and performing an object detection in search spaces of blocks);
replacing at least one basic unit in the initial backbone network with at least one placeholder module to obtain a to-be-determined network, wherein the candidate set comprises parameters of a plurality of structures corresponding to the at least one placeholder module (sections 1 and 3.2-3.3 teach block search spaces including corresponding block “numbers”, and for example “[f]or each block to search, there are 4 choices developed from the original ShuffleNetv2 block: changing the kernel size with {3⇥3, 5⇥5, 7⇥7} or replacing the right branch with an Xception block (three repeated separable depthwise 3⇥3 convolutions)”);
performing sampling based on the candidate set to obtain information about at least one sampling structure (sections 3.2-3.3 teach “we need to recompute batch statistics for each single path (child networks) before each evaluation” performing the operational blocks);
obtaining a network model based on the to-be-determined network and the information about the at least one sampling structure, wherein the information about the at least one sampling structure determines a structure of the at least one placeholder module (sections 3.3-3.4 teach “a population of networks P is initialized randomly. Each individual P consists of its architecture P.θ and its fitness P.f. Any architecture against the constraint η would be removed and a substitute would be picked”); and
applying, when the network model meets a preset condition, the network model as a target neural network (sections 3.4 and 4 teach “Among these evaluated networks, we select the top |P| as parents to generate child networks. The next generation networks are generated by mutation and crossover half by half under the constraint η. By repeating this process in iterations, we can find a single path θbest with the best validation accuracy or fitness, fbest”).
Regarding claims 2, 10, and 18, Chen teaches all the claim limitations of claims 1, 9, and 17 above; and further teaches wherein after obtaining the network model based on the to-be-determined network and the information about the at least one sampling structure, the method further comprises: performing, when the network model does not meet the preset condition, resampling based on the candidate set; updating the information about the at least one sampling based on the resampling to obtain updated information; and updating the network model based on the updated information (Chen, section 3.4, page 12, and Algorithm 2 teach generating models and iteratively calculating the fitness of generated models to meet a determined “constraint”).
Regarding claims 3, 11, and 19, Chen teaches all the claim limitations of claims 1, 9, and 17 above; and further teach wherein before performing sampling based on the candidate set to obtain information about at least one sampling structure, the method comprises constructing a parameter space based on the candidate set, wherein the parameter space comprises architecture parameters corresponding to the parameters of the plurality of structures, and wherein performing sampling based on the candidate set to obtain information about at least one sampling structure comprises performing sampling on the parameter space to obtain at least one group of sampling parameters corresponding to the at least one sampling structure (Chen, section 3.4 teaches “At first, a population of networks P is initialized randomly. Each individual P consists of its architecture P.θ and its fitness P.f. Any architecture against the constraint η would be removed and a substitute would be picked”).
Regarding claims 4, 12, and 20, Chen teaches all the claim limitations of claims 3, 11, and 19 above; and further teach wherein obtaining the network model based on the to-be-determined network and the information about the at least one sampling structure comprises converting the structure of the at least one placeholder module in the to-be-determined network based on the at least one group of sampling parameters in order to obtain the network model (Chen, sections 3.3-3.4 and 5.3 teach conforming searched blocks to model settings and performing mutation and crossover of models to obtain “next generation networks”).
Regarding claims 5 and 13, Chen teaches all the claim limitations of claims 3 and 11 above; and further teach wherein before obtaining the network model based on the to-be-determined network and the information about the at least one sampling structure, the method further comprises constructing the plurality of structures based on the candidate set and the to-be-determined network, wherein the plurality of structures forms a structure search space, and wherein obtaining the network model based on the to-be-determined network and the information about the at least one sampling structure comprises searching the network model from the structure search space based on the at least one group of sampling parameters (Chen, sections 3.3-3.4 teach “At first, a population of networks P is initialized randomly. Each individual P consists of its architecture P.θ and its fitness P.f. Any architecture against the constraint η would be removed and a substitute would be picked”; and when picking the substitute block structure from the “search space[s]” to be included in the model).
Regarding claims 6 and 14, Chen teaches all the claim limitations of claims 1 and 9 above; and further teach wherein the preset condition comprises one or more of the following: a quantity of times of obtaining the network model exceeds a preset quantity of times, a duration for obtaining the network model exceeds preset duration, or an output result of the network model meets a preset requirement (Chen, section 4 teaches “the evolution process is repeated for 20 iterations”).
Regarding claims 7 and 15, Chen teaches all the claim limitations of claims 1 and 9 above; and further teach wherein the candidate set comprises one or more of the following: a type of an operator, attribute information of an operator, or a connection mode between operators (Chen, section 3.3 teaches block search spaces including blocks that correspond to specific network functions (operators): “For each block to search, there are 4 choices developed from the original ShuffleNetv2 block: changing the kernel size with {3_3, 5_5, 7_7} or replacing the right branch with an Xception block (three repeated separable depthwise 3_3 convolutions)”).
Regarding claims 8 and 16, Chen teaches all the claim limitations of claims 1 and 9 above; and further teach wherein the target neural network is for performing at least one of picture recognition, semantic segmentation, or object detection (Chen, abstract teaches designing networks for “object detection”).
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hua et al (US Pub 20200257961) teaches neural network construction using a search space and a template network with a set of parameters.
Conclusion
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/C.M./Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123