DETAILED ACTION
Claim Status
This is first office action on the merits in response to the application filed on 5/28/2024.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-10 are currently pending and have been examined.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under the Step 1 of the Section 101 analysis, Claims 1-8 are drawn to a method which is within the four statutory categories (i.e., a process), and Claims 9-10 are drawn to a system which is within the four statutory categories (i.e. a machine).
Since the claims are directed toward statutory categories, it must be determined if the claims are directed towards a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea). Based on consideration of all of the relevant factors with respect to the claim as a whole, claims 1-10 are determined to be directed to an abstract idea. The rationale for this determination is explained below:
Regarding Claims 1, 4, and 9-10:
Claims 1, 4, and 9-10 are drawn to an abstract idea without significantly more. The claims recite “determining at least two formats of an Al model; determining runtimes corresponding to the Al model in the at least two formats; combining a preset simulation environment with the at least two runtimes to obtain at least two combinations; running the at least two combinations to obtain corresponding data flow results; and determining one combination from the at least two combinations according to the data flow results.”
Under the Step 2A Prong One, the limitations, as underlined above, are processes that, under its broadest reasonable interpretation, cover Mental Processes such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
For example, but for the “Al model”, “simulation environment”, and “data flow results” language, the underlined limitations in the context of this claim encompass the human activity or mental processes. A person could determine, combine, and run to obtain results data or information such as formats, runtimes, environments, and combinations.
Under the Step 2A Prong Two, this judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – “A method for simulating deployment for an artificial intelligence (Al) model, the method comprising:”, “A method for simulating deployment for an artificial intelligence (Al) model, the method comprising:”, “An apparatus for simulating deployment for an artificial intelligence (AI) model, the apparatus comprising: a converter to..; a runtime manager to..; a computing device manager to..; a combinator to..; a profiler to..; and a deployer to…”, “An electronic device comprising: a processor; and a memory storing instructions; wherein the instructions are executed by the processor to:”, “Al model”, “simulation environment”, and “data flow results”. The additional elements are recited at a high-level of generality (i.e., performing generic functions of an interaction) such that it amounts no more than mere instructions to apply the exception using a generic computer component, merely implementing an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea. Additionally, regarding the specification and claims, there is no improvement in the functioning of a computer or an improvement to other technology or technical field present, there is no applying or using the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition present, there is no implementing the judicial exception with or using the judicial exception in conjunction with a particular machine or manufacture that is integral to the claim present, there is no effecting a transformation or reduction of a particular article to a different state or thing present, and there is no applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment present such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Accordingly, these additional elements, individually 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. The claims are directed to an abstract idea.
Under the Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements in the process amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Regarding Claim 8:
Claims 8 is drawn to an abstract idea without significantly more. The claims recite “determining a plurality of preset formats of an AI model; determining a first runtime corresponding to the AI model in a first preset format in the plurality of preset formats; performing a combination of a preset simulation environment and the first runtime; running the combination to obtain a first data flow result; if the first data flow result meets a preset condition, outputting the combination; if the first data flow result does not meet the preset condition, determining a second runtime corresponding to the AI model in a second preset format in the plurality of preset formats; performing a combination of the preset simulation environment and the second runtime; and running the combination to obtain a second data flow result; if the second data flow result meets the preset condition, outputting the combination; and if the second data flow result does not meet the preset condition, repeating the process until a data flow result meets the preset condition, and outputting a combination corresponding to the data flow result.”
Under the Step 2A Prong One, the limitations, as underlined above, are processes that, under its broadest reasonable interpretation, cover Mental Processes such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
For example, but for the “Al model”, “simulation environment”, and “data flow results” language, the underlined limitations in the context of this claim encompass the human activity or mental processes. A person could determine, perform, and run to obtain results data or information such as formats, runtimes, environments, and combinations.
Under the Step 2A Prong Two, this judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – “A method for simulating deployment for an artificial intelligence (AI) model, the method comprising:”, “Al model”, “simulation environment”, and “data flow results”. The additional elements are recited at a high-level of generality (i.e., performing generic functions of an interaction) such that it amounts no more than mere instructions to apply the exception using a generic computer component, merely implementing an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea. Additionally, regarding the specification and claims, there is no improvement in the functioning of a computer or an improvement to other technology or technical field present, there is no applying or using the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition present, there is no implementing the judicial exception with or using the judicial exception in conjunction with a particular machine or manufacture that is integral to the claim present, there is no effecting a transformation or reduction of a particular article to a different state or thing present, and there is no applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment present such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Accordingly, these additional elements, individually 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. The claims are directed to an abstract idea.
Under the Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements in the process amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Regarding Claims 2-3 and 5-7:
Dependent claims 2-3 and 5-7 include additional limitations, for example, “AI model” (Claim 2); “computing device”, “virtualized”, and “simulation environment” (Claim 3); “data flow” (Claim 5); “data flow” (Claim 6); and “simulation environments”, “central processing unit (CPU)”, and “graphic processing unit (GPU)” (Claim 7), but none of these limitations are deemed significantly more than the abstract idea because, as stated above, they require no more than generic computer structures or signals to be executed, and do not recite any Improvements to the functioning of a computer, or Improvements to any other technology or technical field.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation or implementing the judicial exception on a generic computer.
Therefore, whether taken individually or as an ordered combination, claims 2-3 and 5-7 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claim(s) 1 and 3-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goli (US 20200356415 A1) in view of Farabet (WO 2019191306 A1).
Regarding Claims 1 and 9-10, Goli teaches A method for simulating deployment for an artificial intelligence (Al) model, the method comprising (Goli: Abstract; Paragraph(s) 0003): An apparatus for simulating deployment for an artificial intelligence (AI) model, the apparatus comprising: a converter to..; a runtime manager to..; a computing device manager to..; a combinator to..; a profiler to..; and a deployer to.. (Goli: Abstract; Paragraph(s) 0003, 0013): An electronic device comprising: a processor; and a memory storing instructions; wherein the instructions are executed by the processor to (Goli: Abstract; Paragraph(s) 0003):
determining at least two formats of an Al model (Goli: Paragraph(s) 0013, 0018, 0028, 0038, 0068, 0078, 0084 teach(es) The inference master sends the ML model configuration data (e.g., including the information about the different models that the inference engine should run). For a given one of the inference engines, a selected computational framework may be used, such as Tensorflow® serving developed by Google®);
determining runtimes corresponding to the Al model in the at least two formats (Goli: Abstract; Paragraph(s) 0028, 0038, 0050, 0056, 0068, 0079 teach(es) The independent generation of each ML model application by the ML model application generator, may include choosing respective runtime environment settings and memory usage based on specialized hardware (e.g., GPU, TPU, hardware accelerators, VPU, Movidius, etc.) and other hardware configurations of the edge system. Runtime information for the ML model may be determined based on heuristics and statistics collected for similar ML models, which can be estimated based on size; The inference engine may be configured to select a runtime based on a hardware configuration of the edge system, and execute the ML model on input data to provide inference or prediction data; the inference engine may include multiple executors each configured to execute the ML model according to a different runtime. The inference engine may be configured to optimize the ML model for execution based on a hardware configuration);
combining a preset [simulation] environment with the at least two runtimes to obtain at least two combinations (Goli: Paragraph(s) 0003, 0021, 0033-0034, 0050 teach(es) edge systems may include hardware accelerators that can be leveraged to execute the ML model to provide an inference or prediction);
running the at least two combinations to obtain corresponding data flow results (Goli: Abstract; Paragraph(s) 0021, 0057 teach(es) to cause the MT inference service to select a runtime environment from the ML model application to execute the ML model based on a hardware configuration of the edge system, and execute the ML model using the selected to provide inference results; The inference engine may communicate with the RPC to send and receive data associated with loading, executing, providing results, etc. associated with the ML model); and
determining one combination from the at least two combinations according to the data flow results (Goli: Abstract; Paragraph(s) 0014, 0021, 0034 teach(es) The ML inference service hosted on an edge system may be configured to receive a request for a ML model, and to load the requested ML model in an inference engine. The inference engine may be configured to select a runtime based on a hardware configuration of the edge system, and execute the ML model to provide inference data).
However, Goli does not explicitly teach a preset simulation environment.
Farabet from same or similar field of endeavor teaches a preset simulation environment (Farabet: Abstract; Page 1, line 24 ~ Page 2, line 15; Page 12, lines 5-31 teach(es) The DNNs may then be tested in a simulated environment - in some examples using hardware configured for installation in a vehicle to execute an autonomous driving software stack - to control a virtual vehicle in the simulated environment or to otherwise test, verify, or validate the outputs of the DNNs. Prior to use by the DNNs, virtual sensor data generated by virtual sensors within the simulated environment may be encoded to a format consistent with the format of the physical sensor data generated by the vehicle).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Goli to incorporate the teachings of Farabet for a preset simulation environment.
There is motivation to combine Farabet into Goli because Farabet’s teachings of a preset simulation environment would facilitate training, testing, and verifying autonomous machines (Farabet: Abstract; Page 1, line 24 ~ Page 2, line 15; Page 12, lines 5-31).
Regarding Claim 4, Goli teaches A method for simulating deployment for an artificial intelligence (Al) model, the method comprising (Goli: Abstract; Paragraph(s) 0003):
determining at least one format of an Al model (Goli: Paragraph(s) 0013, 0018, 0028, 0038, 0068, 0078, 0084 teach(es) The inference master sends the ML model configuration data (e.g., including the information about the different models that the inference engine should run). For a given one of the inference engines, a selected computational framework may be used, such as Tensorflow® serving developed by Google®);
determining a runtime corresponding to the Al model in the at least one format (Goli: Abstract; Paragraph(s) 0028, 0038, 0050, 0056, 0068, 0079 teach(es) The independent generation of each ML model application by the ML model application generator, may include choosing respective runtime environment settings and memory usage based on specialized hardware (e.g., GPU, TPU, hardware accelerators, VPU, Movidius, etc.) and other hardware configurations of the edge system. Runtime information for the ML model may be determined based on heuristics and statistics collected for similar ML models, which can be estimated based on size; The inference engine may be configured to select a runtime based on a hardware configuration of the edge system, and execute the ML model on input data to provide inference or prediction data; the inference engine may include multiple executors each configured to execute the ML model according to a different runtime. The inference engine may be configured to optimize the ML model for execution based on a hardware configuration);
combining at least two [simulation] environments with the at least one runtime to obtain at least two combinations (Goli: Paragraph(s) 0003, 0021, 0033-0034, 0050 teach(es) edge systems may include hardware accelerators that can be leveraged to execute the ML model to provide an inference or prediction);
running the at least two combinations to obtain corresponding data flow results (Goli: Paragraph(s) Abstract; Paragraph(s) 0021, 0057 teach(es) to cause the MT inference service to select a runtime environment from the ML model application to execute the ML model based on a hardware configuration of the edge system, and execute the ML model using the selected to provide inference results; The inference engine may communicate with the RPC to send and receive data associated with loading, executing, providing results, etc. associated with the ML model); and
determining one combination from the at least two combinations according to the data flow results (Goli: Abstract; Paragraph(s) 0014, 0021, 0034 teach(es) The ML inference service hosted on an edge system may be configured to receive a request for a ML model, and to load the requested ML model in an inference engine. The inference engine may be configured to select a runtime based on a hardware configuration of the edge system, and execute the ML model to provide inference data).
However, Goli does not explicitly teach simulation environments.
Farabet from same or similar field of endeavor teaches simulation environments (Farabet: Abstract; Page 1, line 24 ~ Page 2, line 15; Page 12, lines 5-31 teach(es) The DNNs may then be tested in a simulated environment - in some examples using hardware configured for installation in a vehicle to execute an autonomous driving software stack - to control a virtual vehicle in the simulated environment or to otherwise test, verify, or validate the outputs of the DNNs. Prior to use by the DNNs, virtual sensor data generated by virtual sensors within the simulated environment may be encoded to a format consistent with the format of the physical sensor data generated by the vehicle).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Goli to incorporate the teachings of Farabet for simulation environments.
There is motivation to combine Farabet into Goli because Farabet’s teachings of simulation environments would facilitate training, testing, and verifying autonomous machines (Farabet: Abstract; Page 1, line 24 ~ Page 2, line 15; Page 12, lines 5-31).
Regarding Claim 8, Goli teaches A method for simulating deployment for an artificial intelligence (AI) model, the method comprising (Goli: Abstract; Paragraph(s) 0003):
determining a plurality of preset formats of an AI model (Goli: Paragraph(s) 0013, 0018, 0028, 0038, 0068, 0078, 0084 teach(es) The inference master sends the ML model configuration data (e.g., including the information about the different models that the inference engine should run). For a given one of the inference engines, a selected computational framework may be used, such as Tensorflow® serving developed by Google®);
determining a first runtime corresponding to the AI model in a first preset format in the plurality of preset formats (Goli: Abstract; Paragraph(s) 0028, 0038, 0050, 0056, 0068, 0079 teach(es) The independent generation of each ML model application by the ML model application generator, may include choosing respective runtime environment settings and memory usage based on specialized hardware (e.g., GPU, TPU, hardware accelerators, VPU, Movidius, etc.) and other hardware configurations of the edge system. Runtime information for the ML model may be determined based on heuristics and statistics collected for similar ML models, which can be estimated based on size; The inference engine may be configured to select a runtime based on a hardware configuration of the edge system, and execute the ML model on input data to provide inference or prediction data; the inference engine may include multiple executors each configured to execute the ML model according to a different runtime. The inference engine may be configured to optimize the ML model for execution based on a hardware configuration);
performing a combination of a preset [simulation] environment and the first runtime (Goli: Paragraph(s) 0003, 0021, 0033-0034, 0050 teach(es) edge systems may include hardware accelerators that can be leveraged to execute the ML model to provide an inference or prediction);
running the combination to obtain a first data flow result; if the first data flow result meets a preset condition, outputting the combination (Goli: Abstract; Paragraph(s) 0021, 0057 teach(es) to cause the MT inference service to select a runtime environment from the ML model application to execute the ML model based on a hardware configuration of the edge system, and execute the ML model using the selected to provide inference results; The inference engine may communicate with the RPC to send and receive data associated with loading, executing, providing results, etc. associated with the ML model);
if the first data flow result does not meet the preset condition, determining a second runtime corresponding to the AI model in a second preset format in the plurality of preset formats (Goli: Abstract; Paragraph(s) 0028, 0038, 0050, 0056, 0068, 0079 teach(es) The independent generation of each ML model application by the ML model application generator, may include choosing respective runtime environment settings and memory usage based on specialized hardware (e.g., GPU, TPU, hardware accelerators, VPU, Movidius, etc.) and other hardware configurations of the edge system. Runtime information for the ML model may be determined based on heuristics and statistics collected for similar ML models, which can be estimated based on size; The inference engine may be configured to select a runtime based on a hardware configuration of the edge system, and execute the ML model on input data to provide inference or prediction data; the inference engine may include multiple executors each configured to execute the ML model according to a different runtime. The inference engine may be configured to optimize the ML model for execution based on a hardware configuration);
performing a combination of the preset [simulation] environment and the second runtime (Goli: Paragraph(s) 0003, 0021, 0033-0034, 0050 teach(es) edge systems may include hardware accelerators that can be leveraged to execute the ML model to provide an inference or prediction); and
running the combination to obtain a second data flow result; if the second data flow result meets the preset condition, outputting the combination; and
if the second data flow result does not meet the preset condition, repeating the process until a data flow result meets the preset condition, and outputting a combination corresponding to the data flow result (Goli: Abstract; Paragraph(s) 0021, 0057 teach(es) to cause the MT inference service to select a runtime environment from the ML model application to execute the ML model based on a hardware configuration of the edge system, and execute the ML model using the selected to provide inference results; The inference engine may communicate with the RPC to send and receive data associated with loading, executing, providing results, etc. associated with the ML model).
Regarding Claim 3, the combination of Goli and Farabet teaches all the limitations of claim 1 and simulation environment above; and Goli further teaches further comprising before the combining a preset [simulation] environment with at least two runtimes, after an actual computing device of a user is virtualized, mounting the virtualized computing device to the preset [simulation] environment (Goli: Paragraph(s) 0044, 0062-0063, 0065 teach(es) the edge stack may include a virtualized implementation with a hypervisor running on the physical layer and the operating system running on the hypervisor).
Regarding Claim 5, the combination of Goli and Farabet teaches all the limitations of claim 4 above; and Goli further teaches wherein determining one combination from the at least two combinations according to the data flow results comprises determining the one combination from the at least two combinations according to a preset sorting rule of the data flow results (Goli: Paragraph(s) 0101-0102 teach(es) evaluating the ML model to determine the ML model metrics. The ML model metrics include floating point operations per second, a size of the ML model, or combinations thereof).
Regarding Claim 6, the combination of Goli and Farabet teaches all the limitations of claim 4 above; and Goli further teaches wherein determining one combination from the at least two combinations according to the data flow results comprises
determining the one combination from the at least two combinations according to a data flow result selected by a user (Goli: Paragraph(s) 0035, 0021 teach(es) The inference engine may communicate with a remote procedure call server to send and receive data associated with loading, executing, providing results, etc. associated with the ML model).
Regarding Claim 7, the combination of Goli and Farabet teaches all the limitations of claim 4 above; and Goli further teaches wherein the simulation environments comprise at least one of the following:
a central processing unit (CPU) or a graphic processing unit (GPU) (Goli: Paragraph(s) 0028 teach(es) The independent generation of each ML model application by the ML model application generator, may include choosing respective runtime environment settings and memory usage based on specialized hardware (e.g., GPU, TPU, hardware accelerators, VPU, Movidius, etc.) and other hardware configurations of the edge system. Runtime information for the ML model may be determined based on heuristics and statistics collected for similar ML models, which can be estimated based on size).
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goli in view of Farabet, as applied to claim 1 above, and in further view of Nerush (WO 2023036400 A1).
Regarding Claim 2, the combination of Goli and Farabet teaches all the limitations of claim 1 above; however the combination does not explicitly teach wherein determining a formats of an Al model comprises converting a format of the Al model when at least one runtime is unable to run the format of the Al model.
Nerush from same or similar field of endeavor teaches wherein determining a formats of an Al model comprises converting a format of the Al model when at least one runtime is unable to run the format of the Al model (Nerush: Abstract; Page 13, line 14 ~ Page 14, line 2 teach(es) converting the retrieved trained function from the first format into a second format; The conversion of the trained function from the first format to the second format may, in some examples, be done using ONNX ("Open Neural Network Exchange" ) , an open- source artificial intelligence ecosystem allowing for switching between machine learning frameworks, such as PyTorch and Caffe2, or similar tools or formats).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the combination of Goli and Farabet to incorporate the teachings of Nerush for wherein determining a formats of an Al model comprises converting a format of the Al model when at least one runtime is unable to run the format of the Al model.
There is motivation to combine Nerush into the combination of Goli and Farabet because Nerush’s teachings of format conversion would facilitate allowing for switching between machine learning frameworks (Nerush: Abstract; Page 13, line 14 ~ Page 14, line 2).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sriharsha (US 20220036177 A1) teaches Data Field Extraction By A Data Intake And Query System, including ONNX, convert, runtime, simulation environment.
Gold (WO 2018218259 A1) teaches Ensuring Reproducibility In An Artificial Intelligence Infrastructure, including runtime requirements, conversion, converting (704) the unstructured dataset into a structured dataset, cleaning and transforming the data in a format convenient for training, and different machine learning models may require input data that is in different formats.
Xu (US 20250217446 A1) teaches Optimizing Parameter Estimation For Training Neural Networks, including Keras, TensorFlow, training framework, and convert.
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/CLAY C LEE/Primary Examiner, Art Unit 3699
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