DETAILED ACTION
This action is responsive to claims filed 07/29/2022.
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 the Claims
Claims 1-11 are pending.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 07/29/2022 and 01/13/2023 were filed before the mailing of the first office action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 9 objected to because of the following informalities:
“An information processing device comprising controller that…” should be “An information processing device comprising a controller that…” or similar
Appropriate correction is required.
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.
Claim 2 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.
The use of the term “cause data” makes “cause data” sound as if it is a specific data type or kind of data, like causal data. Applicant’s Specification paragraph [0057] seems to indicate otherwise, where the “cause data” seems to be any kind of data. It is unclear to the Examiner if “cause data” is used intentionally, or if “cause” is only present as a result of translation.
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 1-8 and 9 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because The “information processing device comprising [a] controller” is not limited to hardware. The Specification does not define these terms to limit them to hardware; therefore, the claims can be interpreted as software per se (See MPEP 2106.03(I, second list) for software per se is not directed toward a statutory category).
Claims 9-11 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; and because the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al, 573 U.S. (2014). In determining whether the claims are subject matter eligible, the Examiner applies the 2019 USPTO Patent Eligibility Guidelines. (2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, Jan. 7, 2019.)
Step 1:
Claim 9 recites an information processing device, which would fall under the statutory category of a machine, if claim 9 or the Specification were properly amended to limit the device to hardware. Claim 10 recites a model generation method, which falls under the statutory category of a process. Claim 11 recites a non-transitory computer-readable recording medium, which falls under the statutory category of a manufacture.
Step 2A – Prong 1:
Claim 9 recites an abstract idea, law of nature, or natural phenomenon. The limitations of “generates a physical model that predicts, by a simulation that uses data that are obtained from a raw material of a product that is produced by each of plants, a state of one of the plants;”, “generates a first machine learning model that predicts a state that is common to each of the plants, based on experiment data concerning a state of each of the plants as training data;”, and “and generates each second machine learning model that corresponds to each of the plants, based on plant data that are generated uniquely at each of the plants as training data.” under the broadest reasonable interpretation, cover a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper.
Per the Applicant’s Specification, a “physical model” represents typical formulas or behaviors representative of typical processes in an industrial plant, therefore the generating of a physical model is practically performed within the human mind or with the aid of pen and paper. The generating of a machine learning model based on one or more plants and data associated with the one or more plants is also practically performed within the human mind or with the aid of pen and paper.
Step 2A – Prong 2:
The additional elements of claim 9 do not integrate the abstract idea into a judicial exception. The claim recites the additional elements “An information processing device comprising controller” and “a simulation that uses data” are recognized as generic computer components recited at a high level of generality. Although they have and execute instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(2) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application).
The additional elements of “a physical model”, “a raw material of a product that is produced by each of plants”, “a state of one of the plants”, “a first machine learning model”, and “training data” are recognized as non-generic computer components, but are recited at a high level of generality and are found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
Step 2B:
The only limitation on the performance of the described method is a limitation reciting “An information processing device comprising controller” and “a simulation that uses data” These elements are insufficient to transform a judicial exception to a patentable invention because the recited elements are considered insignificant extra-solution activity (generic computer system, processing resources, links the judicial exception to a particular, respective, technological environment). The claim thus recites computing components only at a high-level of generality such that it 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 (see MPEP 2106.05(f)).
The additional elements of “a physical model”, “a raw material of a product that is produced by each of plants”, “a state of one of the plants”, “a first machine learning model”, and “training data” are recognized as non-generic computer components, but are recited at a high level of generality and are found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
Taken alone or in ordered combination, these additional elements do not amount to significantly more than the above-identified abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
For the reasons above, claim 9 is rejected as being directed to non-patentable subject matter under §101. This rejection applies equally to independent claims 10 and 11.
Claim 10 recites similar limitations to claim 9, with the exception of “A model generation method comprising:” (generic computer components), therefore both claims are similarly rejected.
Claim 11 recites similar limitations to claim 9, with the exception of “A non-transitory computer-readable recording medium having stored therein model generation instructions that cause a computer to perform a process comprising:” (generic computer components), therefore both claims are similarly rejected.
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.
Claim(s) 1-2 and 7-8 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Chan et al.(US 11,853,032 B2, filed 05/06/2020), hereinafter Chan.
In regards to claim 1: The present invention claims: “An information processing device comprising a controller that: acquires a first prediction result that indicates a state of a plant based on a physical model;” Chan teaches “In turn, module 106 automatically generates a list of features based on first principles knowledge (such as thermodynamic and chemical engineering domain knowledge) and models of chemical processes. These features could include physical properties such as density, viscosity, heat capacity, dimensionless numbers corresponding to phenomena occurring in the process as well results calculated by a theoretical model of the process. The automatically generated list of features transforms the raw input measurements from module 104 into more reliable and representative inputs into the machine learning training model 108. The resulting new features or augmented input data (XA) of module 106 can be a transform of an existing input, a combination of inputs, or a calculation from a model.” (Column 7, Lines 7-21). Mapping “first principle knowledge” to “a physical model” based on “These "full-scale" models may consist on thousands to millions of mathematical equations representing physical and chemical properties as well as mass and
energy balances in a chemical process under consideration.” (Chan Column 1, Lines 19-22) and Applicant’s Specification [0028]).
“acquires other prediction results that indicate a state of the plant, based on machine learning models that are generated based on data concerning the plant;” Chan teaches “In embodiments of the present invention, the process modeling system 130 generates and deploys hybrid models 116, 516, 716 (detailed later) of the subject chemical process 124 by combining a first principles model 126 and a machine learning model 108, 508, 706.” (Column 6, Lines 20-24).
“and outputs information concerning a state of the plant based on the first prediction result and the other prediction results.” Chan teaches “By combining these methods (machine learning and first principles modeling)“ (Column 5, Lines 54-55) and “The hybrid models 116, 516, 716 predict, with improved accuracy, the progress and physical characteristics/conditions of the subject chemical process 124.” (Column 6, Lines 25-27). Mapping the combination of first principles modeling (“a first prediction result…based on a physical model”) and machine learning (“other prediction results…based on data concerning the plant;”) resulting in an output.
In regards to claim 2: The present invention claims ”wherein the machine learning models include a cause data machine learning model that is generated based on, as training data, cause data that provide an error to prediction of the physical model,” See the Rejection of claim 1 where Chan teaches the physical model and machine learning models. Chan also teaches “Instead, error calculation module or step 708 calculates the errors of the output prediction (YS) of combined machine learning and first principles models 706, 707 relative to the field measurement outputs (Y). If the calculated error (/(YS)-(Y)/) does not satisfy a threshold acceptability level, then error calculation module 708 propagates at 709 the calculated errors into the machine learning model 706 for training.” (Column 12, Lines 50-57, mapping to training the machine learning model on an error based on the physical model).
“and the controller acquires the other prediction results that include a prediction result that is provided based on the cause data machine learning model.” Chan teaches “In embodiments, the computer-implemented method and system further comprises the step of or a configured module calculating a predicted value for output from a simulator, the first principles model forming the simulator. The method/system trains and develops the machine learning model to represent differences between observed output variable values from plant data and corresponding output variable values predicted by the simulator.” (Column 2, Lines 53-60).
In regards to claim 7: Claim 7 recites similar limitations to claim 1, with the exception of “An information processing method comprising:” therefore, both claims are similarly rejected.
In regards to claim 8: Claim 8 recites similar limitations to claim 1, with the exception of “A non-transitory computer-readable recording medium having stored therein information processing instructions that cause a computer to perform a process comprising:” therefore, both claims are similarly rejected.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
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.
Claim(s) 3-4 and 9-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chan as applied to claims 1-2 and 7-8 above, in further view of Xu et al. (US 2018/0136617 A1, filed 11/08/2017), hereinafter Xu.
In regards to claim 3: While Chan teaches multiple models (column 6, Lines 20-25), and the machine learning model(s) of Chan reads on “a second machine learning model that is generated based on plant data that are generated at a target plant that is a prediction target,” and “and acquires a third prediction result based on the second machine learning model,” in “The interface is coupled to the modeling subsystem in a manner that enables improvements in performance of the chemical process at the subject 10 industrial plant based on predictions made by the generated model.” (Column 2, Lines 7-11)
Chan fails to explicitly teach “wherein the machine learning models include a first machine learning model that is generated based on data that are common to a plurality of plants” and “the controller acquires a second prediction result based on the first machine learning model” However, Xu, in a similar field of industrial system monitoring, teaches “A method of continuously modeling industrial asset performance includes an initial model build block creating a first model based on a combination of an industrial asset historical data, configuration data and training data…” (Abstract). Mapping the use of historical data to “data that are common to a plurality of plants” based on “The training data can also include historical data, which can include monitored data from sensors for the particular physical asset and monitored data from other industrial assets of the same type and nature. The historical data, asset configuration data and domain knowledge can be used to create an initial model.” ([0044]). Data collected regarding similar industrial sensors or assets would reasonably be common among plants of similar kinds.
“and the controller outputs information concerning a state of the plant based on the first prediction result, the second prediction result, and the third prediction result.” Both Chan (referenced above) and Xu (Figure 1, item 155) reference outputting based on multiple models’ results.
Xu highlights the complexity of industrial systems and the difficulty standard machine learning practices can have modeling them ([0003]). It would have been obvious to one of ordinary skill in the art at the time of the Applicant’s filing to incorporate additional, similar, or historic data into the model(s) of Chan (which it is noted also performs augmentation of input data to increase accuracy) to increase predictive performance of the one or more models.
In regards to claim 4: The present invention claims “wherein the controller executes an operation of the plant based on a correction result that is provided by correcting the first prediction result with the second prediction result and the third prediction result.” Chan teaches “Process modeling system 130 uses the corresponding residual model 516 to correct the first principle model predicted amounts of a physical condition or property. The resulting corrected physical condition prediction (resulting predicted amounts) is improved in accuracy, thus improving output (model of the chemical process of interest 124) of the process modeling system 130. As a consequence, controller 122 outputs improved in accuracy settings (values) 132 and updates thereto for controlling the chemical process 124 and industrial plant 120 operations.” (Column 11, Lines 57-66).
In regards to claim 9: The present invention claims: “An information processing device comprising controller that: generates a physical model that predicts, by a simulation that uses data that are obtained from a raw material of a product that is produced by each of plants, a state of one of the plants;” Chan teaches “In embodiments, the computer-implemented method and system further comprises the step of or a configured module calculating a predicted value for output from a simulator, the first principles model forming the simulator.”
“generates a first machine learning model that predicts a state that is common to each of the plants, based on experiment data concerning a state of each of the plants as training data;” See the above Rejection of claim 3 how Xu reads on generating multiple models, including trained on historical or similar data.
“and generates each second machine learning model that corresponds to each of the plants, based on plant data that are generated uniquely at each of the plants as training data.” See the above Rejection of claim 3 how Chan reads on one or more machine learning models based on a subject industrial plant, and how the combination of Chan and Xu would have been obvious to one of ordinary skill in the art at the time of the Applicant’s filing.
In regards to claim 10: Claim 10 recites similar limitations to claim 9, with the exception of “An information processing method comprising:” therefore, both claims are similarly rejected.
In regards to claim 11: Claim 11 recites similar limitations to claim 9, with the exception of “A non-transitory computer-readable recording medium having stored therein model generation instructions that cause a computer to perform a process comprising:” therefore, both claims are similarly rejected.
Claim(s) 5 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chan and Xu as applied to claim 3 above, and further in view of Montavon et al. (Machine learning of molecular electronic properties in chemical compound space, 2013), hereinafter Montavon.
In regards to claim 5: While the combination of Chan and Xu reads on “wherein the controller generates: the first machine learning model based on training data” and “and the second machine learning model based on training data that include plant data that are obtained at a target chemical plant that is a prediction target.“ in “The interface is coupled to the modeling subsystem in a manner that enables improvements in performance of the chemical process at the subject 10 industrial plant based on predictions made by the generated model.” (Column 2, Lines 7-11) and both references use of multiple models.
Chan and Xu fail to explicitly teach “that include experiment data that are obtained by a study concerning a chemical plant that produces a new chemical product where a used chemical product is provided as a raw material thereof,” However, Montavon teaches the use of machine learning for identifying structure-relationships of chemical compounds (Abstract, Page 3).
Montavon highlights the benefits of using machine learning in the analysis for the identification of meaningful, novel and predictive structure–property relationships in chemical compounds (Abstract). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s filing to use relevant chemical compound data from experiments, in similar fashion to the application of outside or historical data from Xu, in an industrial analysis system like that of Chan to improve the overall accuracy and information robustness of the system.
In regards to claim 6: The present invention claims: “wherein the controller: inputs optical spectrum data of the raw material that are input to the target chemical plant to the physical model to acquire the first prediction result, inputs the optical spectrum data and the first prediction result to the first machine learning model to acquire the second prediction result, and inputs the optical spectrum data, the first prediction result, and the second prediction result to the second machine learning model to acquire the third prediction result.” Montavon teaches the use of optical spectrum data in the analysis of chemical compounds (Section 2.3, first paragraph and Page 10). It would have reasonable to one of ordinary skill in the art to include similar data into a system combining aspects of Chan, Xu, and Montavon.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GRIFFIN T BEAN whose telephone number is (703)756-1473. The examiner can normally be reached M - F 7:30 - 4:30.
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/GRIFFIN TANNER BEAN/ Examiner, Art Unit 2121
/Li B. Zhen/ Supervisory Patent Examiner, Art Unit 2121