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 .
Status of Claims
Claims 1-20 are presented for examination in this application. The application filing date on 09/13/2023. Claims 1 and 13 are independent.
Examiner notes
(A). Drawings submitted on 09/13/2023 comply with the provisions of 37 CFR 1.121(d)
(B). IDS submitted on 09/19/2024 have been fully considered by the Examiner.
(C). Limitations have been provided with the Bold fonts in order to distinguish from the cited part of the reference (Italic).
(D). Examiner has cited particular columns, line numbers, references, or figures in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses to fully consider the reference in entirety, as potentially teaching all or part of the claimed invention. See MPEP § 2141.02 VI and 2123.
The examiner requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111 (c).
Priority
Applicant’s claim for the benefit of two prior-filed applications (63/406,196 and 63/417,456) under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged. However, examiner notes that application 63/406,196 does not deal with any estimate/cost computing. The application 63/417,456 deal with cost analysis. Accordingly, the continuation of application’s priority date (10/19/2022) is being considered by the examiner.
Claim Objections
Claims 1-12 and 17 are objected to because of the following informalities:
Claim 1, lines 6-7 “the one more resource costs” lacks proper antecedent basis.
Claim 9, line 3, “the selected configuration” lacks proper antecedent basis.
Claim 17, line 1, “claim 3” should have been --claim 13--. For the following rejection claim 17 will be treated as depending on claim 13.
These claims 2-8 and 10-12, are dependent claims of objected claims; therefor they inherit the same issue.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“training module” and “estimation module” in claim 1;
“optimization module” in claim 6;
“configuration module” in claim 10;
“runtime module” in claim 11;
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
INSUFFICIENT CORRESPONDING STUCTURE
As to claims 1-12, various limitations of these claims invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph as noted above. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed functions of limitations a-e identified above. The claims are therefore indefinite.
Note that for system for estimating resource costs for computing tasks limitations, the corresponding structure includes an algorithm for performing the entire claimed functions. See M.P.E.P § 2181(II)(B). And the specification here discloses no algorithms for performing the entire claimed functions. It does little more than repeat the claim language. For the purposes of examination, the limitations will simply be interpreted in accordance with the broadest reasonable interpretation in light of the specification.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
As to claims 2-5 and 7-9, they are dependent on claim 1, do not cure the deficiencies of that claim and are rejected for the same reasons.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-12 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As to claims 1-12, these claims invoke interpretation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 without sufficient corresponding structure in the specification as set forth above. Such claims also lack written description. See M.P.E.P. § 2163.063(V).
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 non-statutory subject matter.
Step 1: Claims 1-12 are directed to system and fall within the statutory category of machines; Claims 13-20 are directed to method and fall within the statutory category of processes. Therefore, “Are the claims to a process, machine, manufacture or composition of matter?” Yes.
In order to evaluate the Step 2A inquiry “Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?” we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon or an abstract idea and further whether the claim recites additional elements that integrate the judicial exception into a practical application.
As to claim 13: Under Step 2A, Prong 1, the claim recites multiple limitations that recite an abstract idea. The limitations “ estimating resource costs for computing tasks for a reconfigurable dataflow; obtaining resource costs for a computing template for each configuration of a set of template configurations, the computing template corresponding to a computing task;” , “training a neural network using the one or more resource costs as training targets to produce a trained neural network;” and “using the trained neural network to estimate the resources costs for an uncompiled configuration for the computing template”, mental process since “…a reconfigurable … defining” is a concept that can be reasonably performed in the human mind (with the aid of pen and paper), judgement and/or opinion. The limitation The BRI of these limitations requires performing an arithmetic calculation (estimate), Neurons network (mathematical models) specifically “calculation” which requires “calculating a resource cost.” Therefore, since the BRI of the claim requires a mathematical calculation, the limitation is directed to a mathematical concept which is a judicial exception that is not patent eligible.
Under Step 2A, Prong 2, the additional elements “thereby produce estimated resource costs for the uncompiled configuration for the computing template.” are not indicative of integration into a practical application. The limitations “computing system includes” merely recites generic computer system or device to carry out or apply the judicial exception. MPEP 2106.05(f).
Under step 2B, the additional elements do not amount to significantly more than the abstract idea. As stated above, the claimed invention merely recites generic computer system for carrying out or applying the abstract idea. Furthermore, the courts have recognized that mere data obtain, such as those defined in the claim, are well-understood, routine, and convention computer functions which cannot serve as an inventive concept according to MPEP 21.06.05(d).
For the above reasons, the claims of this application are not patentable under 35 USC 101.
As to claim 1: Under Step 2A, Prong 1, the claim recites multiple limitations that recite an abstract idea. The limitations “obtain resource costs for a computing template for each configuration of a set of template configurations, the computing template corresponding to a computing task;” , “the training module configured to train a neural network using the one or more resource costs as training targets to produce a trained neural network” and “an estimation module configured to use the trained neural network to estimate the resources costs for an uncompiled configuration for the computing template”, mental process since “…a reconfigurable … defining” is a concept that can be reasonably performed in the human mind (with the aid of pen and paper), judgement and/or opinion. The limitation The BRI of these limitations requires performing an arithmetic calculation (estimate), Neurons network (mathematical models) specifically “calculation” which requires “calculating a resource cost.” Therefore, since the BRI of the claim requires a mathematical calculation, the limitation is directed to a mathematical concept which is a judicial exception that is not patent eligible.
Under Step 2A, Prong 2, the additional elements “thereby produce estimated resource costs for the uncompiled configuration for the computing template.” are not indicative of integration into a practical application. The limitations “computing system includes” merely recites generic computer system or device to carry out or apply the judicial exception. MPEP 2106.05(f).
Under step 2B, the additional elements do not amount to significantly more than the abstract idea. As stated above, the claimed invention merely recites generic computer system for carrying out or applying the abstract idea. Furthermore, the courts have recognized that mere data obtain, such as those defined in the claim, are well-understood, routine, and convention computer functions which cannot serve as an inventive concept according to MPEP 21.06.05(d).
For the above reasons, the claims of this application are not patentable under 35 USC 101.
Claims 2-3 and 15 are not patent eligible for the same reasons given for claim 1, “wherein: the training module is configured to …” and “wherein each configuration comprises …” ; are functions that can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, opinion, thus it is reasonable to identify these limitation as reciting a mental process.
Claims 4, 7, 16 and 19 are not patent eligible for the same reasons given for claim 1, “… comprises one or more of an input size, a filter size, a stride, and a base grid size.” and “wherein the plurality of proposed configurations comprise a plurality of base grid sizes.” are functions that can be reasonably carried out in the human mind with the aid of pen and paper, through observation, evaluation, judgment, opinion, thus it is reasonable to identify these limitation as reciting a mental process.
Claims 5, 10, 11-12, 14 and 17 are not patent eligible for the same reasons given for claim 1, “… wherein the resources costs comprise a memory unit count, a compute unit ...” , “… configured to generate dataflow configuration information that enables the reconfigurable dataflow computing system …” , “… the runtime module configured to launch execution of the computing template …” and “...wherein the resources costs comprise a memory unit count, a compute unit count, and a compute latency. ” the additional elements are merely recite instructions to implement an abstract idea on a generic computer, or merely uses a generic computer or computer components as a tool to perform the abstract idea, thus is not a practical application under Prong 2. See MPEP 2106.05(f).
Claims 6, 8-9, 8 and 20 are not patent eligible for the same reasons given for claim 1, “… module configured to determine the estimated resource costs ...” , “… wherein the selected configuration is selected according …” , “ and “… configured to comprise: an allocation module configured to allocate resources ...” are functions that can be reasonably carried out in the human mind with the aid of pen and paper. Therefore, since the BRI of the claim requires a mathematical calculation. Additionally, the limitations “determining, selecting, based on the estimation and allocation, a compute resource of the available compute resources to perform at least one calculation of the plurality of calculations;” is a concept that can be reasonably performed in the human mind (with the aid of pen and paper), which include observation, evaluation, judgement and/or opinion, through observation, evaluation, judgment, opinion, thus it is reasonable to identify these limitation as reciting a mental process.
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 of this title, 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.
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 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.
Claims 1, 3, 6, 8-9, 13, 15, 18 and 20 are rejected under 35 U.S.C. 103 as being obvious over Jiang et al. (CN-114528070-A, hereinafter Jiang) in view of Gupta et al. (US 20230409387 A1, hereinafter Gupta).
As to claim 1, Jiang discloses a system for estimating resource costs for computing tasks for a reconfigurable dataflow computing system includes:
a training module configured to (abstract, generating complete machine learning model code according to the template; abstract modular template, based on hierarchical characteristics of the convolutional neural network module, providing template of standard abstract convolutional neural network module … The invention can fully exert [i.e. obtain] the performance of the computing device, reduce the cost of the model designer training model, … ), the computing template corresponding to a computing task (page 9, The method according to the convolutional neural network module relationship between each module, designing a segmented machine learning frame and a set of template of abstract convolutional neural network module module, providing the abstract according to the module needed by the calculation resource [i.e. computing task], …);
the training module configured to train a neural network using the one or more (abstract, The invention can fully exert [i.e. obtain] the performance of the computing device, reduce the cost of the model designer training model, …. Further, page 2, providing a convolutional neural network module training method and system based on containerized); and
an estimation module configured to use the trained neural network (page 6, The calculation amount determines the calculation resource needed by the module, such as CPU, GPU and so on. based on the convolutional neural network module relationship between each module is obvious and each module parameter quantity and calculating quantity difference is large convolutional neural network module each module does not accord with the hardware requirement of the characteristic such that a large amount of storage resource and calculating resource is wasted, increasing the cost of the model training) for an uncompiled configuration for the computing template (page 8, using the method design model, there is no need to compile the related model code, only need in the declaring type template according to the type of the template) and thereby produce estimated (page 6, The calculation amount determines the calculation resource needed by the module, such as CPU, GPU and so on. based on the convolutional neural network module relationship between each module is obvious and each module parameter quantity and calculating quantity difference is large convolutional neural network module each module does not accord with the hardware requirement of the characteristic such that a large amount of storage resource and calculating resource is wasted, increasing the cost of the model training).
Jiang does not explicitly disclose the following limitations but,
Gupta disclose obtain resource costs for a computing (par. 0023-0024, computing tasks may be referred to as being compute-bound, in which the time needed to complete the computing task is determined mainly by the speed of the processor (or multiple processing cores in the case of a computing task … which the time needed to complete the computing task is determined mainly by the amount of memory required to hold the working data, … where the end cost to the user is calculated based on the amount of time the computing resource);
using the one or more resource costs (par. 0023, … The various types of computer hardware are typically offered to users at different prices (e.g., different hourly price rates), where more powerful computer systems are typically more expensive than less powerful computer systems, and where the end cost to the user is calculated based on the amount of time the computing resource)
produce estimated resource costs (see pars. 0023-0024).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Jiang to include obtain / producing resource costs for a computing, as disclosed by Gupta, for the purpose to determine / obtain the amount of memory / resource required to hold the working data. (see paragraph 0023 of Gupta).
As to claim 3, Jiang discloses the system wherein each configuration comprises a set of configuration parameters (page 3, the convolution layer template is mainly used for configuring parameters of the convolutional neural network
module layer, the convolution layer template mainly comprises the following parameters).
As to claim 6, Jiang does not explicitly disclose the following limitation but,
Gupta discloses the system further configured to comprise:
an optimization module configured to determine the estimated resource costs for a plurality of proposed configurations and select a selected configuration for the computing template (par. 0012, FIG. 2A is a flowchart of a method for generating a proposed computer hardware configuration according to one example of the present technology. Further, par. 0024, … training deep neural networks. The various types of computer hardware are typically offered to users at different prices (e.g., different hourly price rates), where more powerful computer systems are typically more expensive than less powerful computer systems, and where the end cost to the user is calculated based on the amount of time the computing resource … . Further, 0038, FIG. 1, the computing task management interface 130 may present the proposed computer hardware configuration to the user 102 via the client application 120, and may configure an instance of a computing resource in accordance with a selection of a computer hardware configuration made by the user 102. In some examples, the user makes a selection that is consistent with the proposed computer hardware configuration, but in other cases the user may make a selection that is different from the proposed computer hardware configuration. In other examples, the computing task management interface 130 automatically selects the proposed computer hardware configuration generated by the self-tuning computer hardware configuration proposal engine 115 without requesting authorization or confirmation from the user 102, in which case the proposed computer hardware configuration may be referred to herein as an updated computer hardware configuration. Further, par. 0032, the user 102 will typically be asked to specify the type of computer system that will be provisioned to execute the specified computing task, illustrated in FIG. 1 in the simplified form [i.e. template] of selecting one hardware configuration from among hardware configuration)).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Jiang to include an optimization module configured to determine the estimated resource costs for a plurality of proposed configurations and select a selected configuration for the computing template, as disclosed by Gupta, for the purpose to automatically allocate an instance of a computing resource that is configured according to the proposed computer hardware configuration. (see paragraph 0048 of Gupta).
As to claim 8, Gupta discloses the system wherein the selected configuration is selected according to one or more optimization criteria (par. 0045, … a computer hardware configuration of a computer system, specifies computing hardware that will execute a task, such as a number of CPUs, amount of memory, an amount of storage bandwidth, a number of GPUs, or the like. As one concrete example, the proposed computer hardware configuration may be selection between a “general” compute type and a “memory-optimized” compute type, as well as a number of cores (e.g., selected from a collection of possible numbers of cores, such as: 8 cores, 16 cores, 32 cores, 48 cores, 80 cores, 144 cores, or 272 cores). …).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Jiang to include the system wherein the selected configuration is selected according to one or more optimization criteria, as disclosed by Gupta, for the purpose to automatically allocate an instance of a computing resource that is configured according to the proposed computer hardware configuration. (see paragraph 0048 of Gupta).
As to claim 9, Gupta discloses the system further configured to comprise:
an allocation module configured to allocate resources according to the estimated resource costs for the selected configuration for the computing template to produce allocated resources (par. 0024, … the end cost to the user is calculated [i.e. estimated] based on the amount of time the computing resource is used … . Further, par. 0025, … configured upon request to implement the different types of computer system configurations. For example, a physical server may have 256 processor cores and 2048 GB of installed memory, and these computing resources may be allocated and dedicated to particular virtual machines in accordance with their configuration types. For example, a virtual machine providing a low performance computer system configuration may be allocated one processor core and 4 GB of memory, while a compute-optimized computer system … . Further, par. 0032, the user 102 will typically be asked to specify the type of computer system that will be provisioned to execute the specified computing task, illustrated in FIG. 1 in the simplified form [i.e. template] of selecting one hardware configuration from among hardware configuration).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Jiang to include an allocation module configured to allocate resources according to the estimated resource costs for the selected configuration for the computing template to produce allocated resources, as disclosed by Gupta, for the purpose to automatically allocate an instance of a computing resource that is configured according to the proposed computer hardware configuration. (see paragraph 0048 of Gupta).
As to claim 13, Jiang discloses a computer-implemented method for
obtaining (abstract, generating complete machine learning model code according to the template; abstract modular template, based on hierarchical characteristics of the convolutional neural network module, providing template of standard abstract convolutional neural network module … The invention can fully exert [i.e. obtain] the performance of the computing device, reduce the cost of the model designer training model, … ), the computing template corresponding to a computing task (page 9, The method according to the convolutional neural network module relationship between each module, designing a segmented machine learning frame and a set of template of abstract convolutional neural network module module, providing the abstract according to the module needed by the calculation resource [i.e. computing task], …);
training a neural network using the one or more (abstract, The invention can fully exert [i.e. obtain] the performance of the computing device, reduce the cost of the model designer training model, …. Further, page 2, providing a convolutional neural network module training method and system based on containerized); and
using the trained neural network to estimate the resources costs for an uncompiled configuration for the computing template (page 8, using the method design model, there is no need to compile the related model code, only need in the declaring type template according to the type of the template) and thereby produce estimated (page 6, The calculation amount determines the calculation resource needed by the module, such as CPU, GPU and so on. based on the convolutional neural network module relationship between each module is obvious and each module parameter quantity and calculating quantity difference is large convolutional neural network module each module does not accord with the hardware requirement of the characteristic such that a large amount of storage resource and calculating resource is wasted, increasing the cost of the model training).
Gupta disclose method for estimating resource costs and obtaining resource costs (par. 0023-0024, computing tasks may be referred to as being compute-bound, in which the time needed to complete the computing task is determined mainly by the speed of the processor (or multiple processing cores in the case of a computing task … which the time needed to complete the computing task is determined mainly by the amount of memory required to hold the working data, … where the end cost to the user is calculated based on the amount of time the computing resource);
using the one or more resource costs (par. 0023, … The various types of computer hardware are typically offered to users at different prices (e.g., different hourly price rates), where more powerful computer systems are typically more expensive than less powerful computer systems, and where the end cost to the user is calculated based on the amount of time the computing resource)
produce estimated resource costs (see pars. 0023-0024).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Jiang to include obtain / producing resource costs for a computing, as disclosed by Gupta, for the purpose to determine / obtain the amount of memory / resource required to hold the working data. (see paragraph 0023 of Gupta).
As to claim 15, it is the method claim, having similar limitations of claim 3. Thus, claim 15 is also rejected under the same rationale as cited in the rejection of claim 3.
As to claim 18, Gupta discloses the computer-implemented method further including:
determining the estimated resource costs for a plurality of proposed configurations and selecting a selected configuration for the computing template (par. 0012, FIG. 2A is a flowchart of a method for generating a proposed computer hardware configuration according to one example of the present technology. Further, par. 0024, … training deep neural networks. The various types of computer hardware are typically offered to users at different prices (e.g., different hourly price rates), where more powerful computer systems are typically more expensive than less powerful computer systems, and where the end cost to the user is calculated based on the amount of time the computing resource … . Further, 0038, FIG. 1, the computing task management interface 130 may present the proposed computer hardware configuration to the user 102 via the client application 120, and may configure an instance of a computing resource in accordance with a selection of a computer hardware configuration made by the user 102. In some examples, the user makes a selection that is consistent with the proposed computer hardware configuration, but in other cases the user may make a selection that is different from the proposed computer hardware configuration. In other examples, the computing task management interface 130 automatically selects the proposed computer hardware configuration generated by the self-tuning computer hardware configuration proposal engine 115 without requesting authorization or confirmation from the user 102, in which case the proposed computer hardware configuration may be referred to herein as an updated computer hardware configuration. Further, par. 0032, the user 102 will typically be asked to specify the type of computer system that will be provisioned to execute the specified computing task, illustrated in FIG. 1 in the simplified form [i.e. template] of selecting one hardware configuration from among hardware configuration)).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Jiang to include an optimization module configured to determine the estimated resource costs for a plurality of proposed configurations and select a selected configuration for the computing template, as disclosed by Gupta, for the purpose to automatically allocate an instance of a computing resource that is configured according to the proposed computer hardware configuration. (see paragraph 0048 of Gupta).
As to claim 20, it is the method claim, having similar limitations of claim 8. Thus, claim 20 is also rejected under the same rationale as cited in the rejection of claim 8.
Claims 2 and 14 are rejected under 35 U.S.C. 103 as being obvious over Jiang et al. and Gupta et al. as applied in the claims 1 and 13 in above and in view of Branson et al. (US 20150161289 A1, hereinafter Branson).
As to claim 2, Jiang discloses the system wherein:
the training module is configured to (page 10, calculating resource is wasted, increasing the cost of the model training. By convolutional neural network module each module and forming a template method, it can solve the problem that the computer hardware resource not fully used in the model training process. The invention claims a method based on convolutional neural network module each module and made into template, providing a set of standard abstract convolutional neural network module template of each module. The template configures the parameters of each module in a declaring definition manner).
Gupta disclose method of resource costs (par. 0023-0024, computing tasks may be referred to as being compute-bound, in which the time needed to complete the computing task is determined mainly by the speed of the processor (or multiple processing cores in the case of a computing task … which the time needed to complete the computing task is determined mainly by the amount of memory required to hold the working data, … where the end cost to the user is calculated based on the amount of time the computing resource);
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Jiang to include the method of resource cost, as disclosed by Gupta, for the purpose to automatically allocate an instance of a computing resource that is configured according to the proposed computer hardware configuration. (see paragraph 0048 of Gupta).
Jiang as modified by Gupta does not explicitly disclose the following limitations, but,
Branson discloses the module is configured to initiate compilation (par. 0004, a system for initializing a streaming application for execution on one or more compute nodes. In various embodiments, the system may include a compiler configured to receive a source code that includes an operator graph that includes a plurality of processing elements, each processing element having one or more stream operators. In addition, the compiler may also be configured to parse, from the source code, a metadata tag describing a customization of at least one of the one or more stream operators having a windowing processing operation. Furthermore, the compiler may also be configured to compile the source code of the streaming application having the windowing processing operation based on the metadata tag. Further, 0041, The memory 425 may store a compiler 136. The compiler 136 compiles modules, which include source code or statements, into the object code, which includes machine instructions that execute on a processor. …).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Jiang to include the module is configured to initiate compilation, that can be used for selection of communication links, as disclosed by Branso, for the purpose to perform peephole optimizations, local optimizations, loop optimizations, inter-procedural or whole-program optimizations, machine code optimizations, or any other optimizations that reduce the amount of time required to execute the object code, to reduce the amount of memory required to execute the object code, or both (see paragraph 0041 of Branson).
As to claim 14, it is the method claim, having similar limitations of claim 2. Thus, claim 14 is also rejected under the same rationale as cited in the rejection of claim 2.
Claims 4-5, 10-12 and 16-17 are rejected under 35 U.S.C. 103 as being obvious over Jiang et al. and Gupta et al. as applied to claims 1, 3, 9, 13, 15, and 18 in the above and further in view of Farabet et al. (US 20120303932 A1, hereinafter Farabet).
As to claim 4, Jiang discloses the system wherein the set of configurations parameters comprises one or more of an input size, a filter size, a stride, (page 4, stride: step length, representing the moving step length in the convolution process, default is 1; the movement of the general convolution kernel on the input image is from left to right, from up to down [i.e. input size]… Stride: pooling window moving step length, default value is kernel-size; Padding: the layer number of O is supplemented to each side of the input; Dilation: a parameter of the element step amplitude in the control window; return - if it is True, returning the sequence number for outputting the maximum value; ceil mode: if it is True, calculating the output signal is too small will be integer, replacing the default downward whole operation [i.e. filter size]).
Jiang as modified by Gupta does not explicitly disclose the following claim limitations, but,
Farabet discloses the system comprises a base grid size (Fig. 1, par. 0019, … The dataflow processor 100 can be configured into various data grids. A dataflow grid or grid is a particular configuration of a dataflow processor 100 …).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Jiang to include the system comprises a base grid size, as disclosed by Farabet, for the purpose to implement of the dataflow grid, what is the sequence of grid configurations that yields the shortest computation time (see paragraph 0045 of Farabet).
As to claim 5, Jiang discloses the system wherein the resources costs comprise a memory unit count, a compute unit count (abstract, … The invention can fully exert the performance of the computing device, reduce the cost of the model designer training model, improve the repeated utilization rate of the cloud service provider device, simplify the design process of the convolutional neural network module. Further, page 9, calculating resource scale (memory [i.e. memory unit]),
Farabet discloses the system wherein a compute latency unit count (par. 0023, The dataflow processor 100 [i.e. latency unit] can be reconfigured at runtime. The time to reconfigure the dataflow processor 100 is in the order of the latency of the dataflow processor 100 …).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Jiang to include the system wherein a compute latency unit count, as disclosed by Farabet, for the purpose to compared to the time needed to perform one such operation (see paragraph 0023 of Farabet).
As to claim 10, Gupta discloses the system further configured to comprise:
a configuration module configured to conduct the computing template according to the allocated resources (par. 0024, … the end cost to the user is calculated [i.e. estimated] based on the amount of time the computing resource is used … . Further, par. 0025, … configured upon request to implement the different types of computer system configurations. For example, a physical server may have 256 processor cores and 2048 GB of installed memory, and these computing resources may be allocated and dedicated to particular virtual machines in accordance with their configuration types. For example, a virtual machine providing a low performance computer system configuration may be allocated one processor core and 4 GB of memory, while a compute-optimized computer system … . Further, par. 0032, the user 102 will typically be asked to specify the type of computer system that will be provisioned to execute the specified computing task, illustrated in FIG. 1 in the simplified form [i.e. template] of selecting one hardware configuration from among hardware configuration).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Jiang to include a configuration module configured to conduct the computing template according to the allocated resources, as disclosed by Gupta, for the purpose to automatically allocate an instance of a computing resource that is configured according to the proposed computer hardware configuration. (see paragraph 0048 of Gupta).
Jiang as modified by Gupta does not explicitly disclose the following limitations but,
Farabet a configuration module configured to generate dataflow configuration information (par. 0019, ] FIG. 1 illustrates a dataflow architecture in accordance with an illustrative embodiment. The dataflow architecture can process homogeneous streams of data in parallel. A dataflow processor 100 includes numerous processing tiles 110, a controller or control unit 120, and a memory access module 130 … ) that enables the reconfigurable dataflow computing system t(par. 0023, The dataflow processor 100 can be reconfigured at runtime. The time to reconfigure the dataflow processor 100 is in the order of the latency of the dataflow processor 100. This allows the dataflow processor to be reconfigured between two kinds of operations, where the time to reconfigure the dataflow processor is negligible compared to the time needed to perform one such operation).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Jiang to include configuration module configured to generate dataflow configuration information that enables the reconfigurable dataflow computing system, as disclosed by Farabet, for the purpose to reconfigure the dataflow processor 100 is in the order of the latency of the dataflow processor 100 (see paragraph 0023 of Farabet).
As to claim 11, Farabet discloses the system further configured to comprise:
a runtime module configured to configure the reconfigurable dataflow computing system using the dataflow configuration information (par. 0025, The runtime configuration bus 160 allows the dataflow processor 100 to be configured at runtime. In one embodiment, each module in the design, such as the processing tiles and the memory access module, has a set of configurable parameters, routes or settings (depicted as squares on FIG. 1), and possesses a unique address on the network. Groups of similar modules can also share a broadcast address, which dramatically speeds up reconfiguration of elements that need to perform similar tasks. As a non-limiting example, the controller 120 can broadcast a configuration packet to a group of processing tiles, that cause the group. … ).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Jiang to include a runtime module configured to configure the reconfigurable dataflow computing system using the dataflow configuration information, as disclosed by Farabet, for the purpose to reconfigure the dataflow processor 100 is in the order of the latency of the dataflow processor 100 (see paragraph 0023 of Farabet).
As to claim 12, Jiang disclose the system wherein: (page 4, an order for defining the name of the template and the execution step of the template; Further, page 13, a step of defining the name of the template and the execution step sequence of the template).
Farabet discloses the runtime module configured to launch execution of the computing(par. 0021, The dataflow processor 100 also includes a runtime configuration bus 160 that can reconfigure many aspects of the processing tiles 110 and the memory access module 130 at runtime. Configurable aspects include, but are not limited to, connections, operators, and memory access module modes. Configurable elements are depicted as squares in FIG. 1. The runtime configuration bus 160 is operably connected to the memory access module 130 and to each of the processing tiles 110. The controller 120 uses the runtime configuration bus 160 to reconfigure the processing tiles 110).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Jiang to include the runtime module configured to launch execution of the computing template with the reconfigurable dataflow computing system according to the dataflow configuration information, as disclosed by Farabet, for the purpose to reconfigure the dataflow processor 100 is in the order of the latency of the dataflow processor 100 (see paragraph 0023 of Farabet).
As to claim 16, it is the method claim, having similar limitations of claim 4. Thus, claim 16 is also rejected under the same rationale as cited in the rejection of claim 4.
As to claim 17, it is the method claim, having similar limitations of claim 5. Thus, claim 17 is also rejected under the same rationale as cited in the rejection of claim 5.
Claims 7 and 19 are rejected under 35 U.S.C. 103 as being obvious over Jiang et al. and Gupta et al. as applied to claims 6 and 18 in the above and further in view of Ghazvinan et al. (US 20230186476 A1, hereinafter Ghazvinan) .
As to claim 7, Jiang as modified by Gupta does not explicitly disclose the following limitations but,
Ghazvinan discloses the system wherein the plurality of proposed configurations comprise a plurality of base grid sizes (par. 0058, The object detector 106 may include a deep neural network system configured to generate object proposals. An object proposal may include a volume in the 3D space of the point cloud that includes a set of points that has a high probability of representing a certain object. ... FIG. 2B the 3D grid may define nodes 207 of a certain density in the 3D space of the point cloud. The nodes of the 3D grid may define centers for object proposals in the 3D space. This way, the deep neural network system of the object detector may define an object proposal network configured to determine object proposals, e.g. 3D bounding boxes, located on the nodes of the 3D grid, wherein the 3D grid comprises a plurality of nodes which spans the 3D space of the point cloud.).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Jiang to include the system wherein the plurality of proposed configurations comprise a plurality of base grid sizes, as disclosed by Ghazvinan, for the purpose to includes a set of points that has a high probability of representing a certain object. (see paragraph 0058 of Gupta).
As to claim 19, it is the method claim, having similar limitations of claim 7. Thus, claim 19 is also rejected under the same rationale as cited in the rejection of claim 7.
Conclusion
16. Prior arts made of record are considered pertinent to applicant's disclosure. See MPEP § 707.05 (C) For Examples:
I. Lau et al. (US 20230225290 A1) discloses: “management layer 80 may provide the functions described below. Resource provisioning 81 provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing 82 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal 83 provides access to the cloud computing environment for consumers and system administrators. Service level management 84 provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment 85 provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.” (please see [0050]).
II. Lai et al. (US 20230076967 A1) discloses: “Furthermore, adjustments of the hyperparameters require a large amount of computing resources, and the conventional diagnosis models for evaluating machine health are not capable of simultaneously evaluating computing costs. Therefore, under the condition of limited resources, there is difficulty in performing cost assessments before establishing the diagnosis model.” (please see [0006]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Mohammad Kabir whose telephone number is (571)270-13411. The examiner can normally be reached on M-F, 8:00 am - 5:00 pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sam Sough can be reached on (571) 272-6799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Mohammad Kabir/
Examiner, Art Unit 2192
/S. Sough/SPE, Art Unit 2192
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