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 Application
2. Claim 1-7 have been examined in this application. This communication is the first action on the merits.
Drawings
3. The drawings filed on 7/13/23 are acceptable for examination proceedings.
Specification
4. The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Rejections - 35 USC § 112
5. 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.
6. Claim 2 recites the limitation "the relative variable", “the premise result”. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 101
7. 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.
8. Claims 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more as fully discussed below.
9. Regarding Independent claim 1, 6, and 7:
Step 1: Yes
Claim 1 is drawn to a analysis method, claim 6 is drawn to non-transitory computer-readable recording medium having stored therein an analysis program that causes a computer to execute a process, and claim 7 is drawn to an information processing device. Therefore claim 1, and 6-7 falls under one of four categories of statutory subject matter (process/method, machines/products/apparatus, manufactures, and compositions of matter).
Step 2A, Prong 1: Yes
Independent claim 1, and 6-7 are directed to a judicially recognized exception of an abstract idea without significantly more.
Claim 1, and 6-7 recites claim limitation of “based on the prediction result, specifying a relevant variable dependent on the premise from the plurality of variables” that under their broadest reasonable interpretation, enumerates a mental concept. A human can mentally specify a relevant variable that are dependent on the premise. Thus, these claimed functions are the judicial exceptions that are no more than a mental abstract idea (See MPEP 2106.04(a)(2)(III)).
Step 2A, Prong 2: No
Claim 1, and 6-7 recites additional limitation of “acquiring a prediction result obtained when a premise is applied to a causal model having a plurality of variables related to operation of a plant”, “and with respect to the relevant variable, displaying information on a state of the relevant variable…”. The claimed functions of “acquiring and displaying” are forms of insignificant input or output solution activities (i.e., extra solution), such that acquiring data and outputting data (i.e., display) are necessary for the use of the judicial exception (See MPEP 2106.05(g)). The combination of these additional elements does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B: No
The additional limitation that are a form of insignificant extra-solution activities, do not amount to significantly more than an abstract idea because the court decisions have determined that this additional element as discussed above in step 2A of acquiring and displaying to be well-understood, routine, and conventional when claimed in a merely generic manner for data collecting (i.e., acquiring) and data outputting (i.e., displaying) (See MPEP § 2106.05(d)(II) (i: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (See Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) and Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)).
As such, claim 1, and 6-7 are not patent eligible.
10. Dependent claims 2-5:
Step 1: Yes
Claim 2-5 are drawn to an analysis method, therefore claim 2-5 are falls under one of four categories of statutory subject matter (process/method, machines/products/apparatus, manufactures, and compositions of matter). Nonetheless, dependent claims 2-5 are also ineligible for the same reasons given with respect to claim 1.
Step 2A, Prong 1: Yes
Dependent claim 2-5 are directed to a judicially recognized exception of an abstract idea without significantly more.
Claim 3 recites limitation of “and using training data including the process data and a result of the clustering, executing a structure training on the causal model”; claim 4 recites “and using the training data including the process data, the result of clustering, and the parent-child relationship, executing the structure training on the causal model”, and claim 5 recites “wherein the executing includes, using the training data and an objective variable representing a state of the plant, executing structure training on a Bayesian network” that under their broadest reasonable interpretation, enumerates a mathematical concept.
As discloses in the specification, executing the structure training on a Bayesian network is employed by a mathematical algorithm:
[0114] Training and prediction of the Bayesian network is executable regularly in a certain period or is executable after operation of a day by batch processing, or the like. Deep learning is an example of machine learning and various algorithms, such as a neural network, deep learning, and support vector machine, can be employed.
Thus, these claimed functions are the judicial exceptions that are no more than an abstract idea processed by a mathematical algorithm (See MPEP 2106.04(a)(2)(I)).
Claim 4, and 5 recites limitation of “specifying a parent-child relationship of the constituent devices”, and “the specifying includes specifying, …., a node with the highest probability value”, and claim recites limitation of “executing clustering of classifying the sets of process data” that under their broadest reasonable interpretation, enumerates a mental concept. A human can mentally specify a claimed function of specifying and clustering of claim 3-5. Thus, these claimed functions are the judicial exceptions that are no more than a mental abstract idea (See MPEP 2106.04(a)(2)(III)).
Step 2A, Prong 2: No
Claim 2 recites additional limitation of “displaying includes displaying, as the information on the state of the relative variable”, Claim 3 recites additional limitation of “collecting a plurality of sets of process data that are output from the plant”, and claim 5 recites additional limitation of “acquiring the prediction result, and the displaying includes, with respect to the relevant variable, displaying the condition and the probability value,”. The functions of displaying, and collecting of data, and acquiring the result is forms of insignificant input or output solution activities (i.e., extra solution), such that acquiring and collecting of data and data outputting (i.e., displaying) are necessary for the use of the judicial exception (See MPEP 2106.05(g)). The combination of these additional elements does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B: No
The additional limitation that are a form of insignificant extra-solution activities, do not amount to significantly more than an abstract idea because the court decisions have determined that this additional element as discussed above in step 2A of acquiring and displaying to be well-understood, routine, and conventional when claimed in a merely generic manner for data receiving (i.e., acquiring, and collecting) and data outputting (i.e., displaying) (See MPEP § 2106.05(d)(II) (i: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (See Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) and Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)).
As such, dependent claim 2-5 are not patent eligible.
Claim Rejections - 35 USC § 102
11. 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)(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.
12. Claims 1, and 6-7 are rejected under 35 U.S.C. 102(a) (2) as being anticipated by Wang (US PG Pub: 2021/0240174).
13. Regarding claim 1, Wang discloses:
An analysis method comprising: acquiring a prediction result obtained when a premise is applied to a causal model having a plurality of variables related to operation of a plant (e.g., The remote monitoring system inputs test data 170 of the monitoring-target system to the estimated (generated) causal structure model 215 and acquires an estimated value of the monitoring-target response variable (S12). For example, the test data 170 is real time data, and includes data indicating the current status and situation of the monitoring-target system) (target-system is interpreted as a plant) (Para. [0048]);
based on the prediction result, specifying a relevant variable dependent on the premise from the plurality of variables (e.g., and acquires an estimated value of the monitoring-target response variable (S12))) (Para. [0048]);
and with respect to the relevant variable, displaying information on a state of the relevant variable obtained according to the prediction result and a statistic of plant data corresponding to the relevant variable in plant data that is generated in the plant (e.g., FIG. 29 illustrates a display example of predicted values of the response variable according to the causal structure model 215. The client apparatus 240 acquires the image data from the response-variable-value estimation program 231 and displays the image on the output device 317.) (Para. [0145], also refer to Para. [0147]-[0148], and Fig. 30).
14. Regarding claim 6, Claim 6 recites a non-transitory computer-readable recording medium having stored therein an analysis program that causes a computer to execute a process that implement the analysis method of claim 1, with substantially the same limitations. Therefore the rejection applied to claim 1 also applies to claim 6 respectively.
Wherein Wang further teaches a non-transitory computer-readable recording medium having stored therein an analysis program that causes a computer to execute a process comprising (e.g., FIG. 3 schematically illustrates a hardware configuration example of a computer. The computer includes one or more storage apparatuses that store programs, and one or more processors that operate according to the programs. The computer includes a processor 311, a memory 312, an auxiliary storage apparatus 313, a network interface 314, an input/output interface 315, an input device 316, and an output device 317. These components are connected with each other by a bus. The memory 312, the auxiliary storage apparatus 313 or a combination of the memory 312 and the auxiliary storage apparatus 313 is a storage apparatus and includes a storage medium that stores software) (Para. [0052], Fig. 3).
15. Regarding claim 7, Claim 7 an information processing device that implement the analysis method of claim 1, with substantially the same limitations, respectively. Therefore the rejection applied to claim 1 also applies to claim 7 respectively.
Wherein Wang further teaches an information processing device comprising: a processor configured to: (e.g., FIG. 3 schematically illustrates a hardware configuration example of a computer. The computer includes one or more storage apparatuses that store programs, and one or more processors that operate according to the programs. The computer includes a processor 311, a memory 312, an auxiliary storage apparatus 313, a network interface 314, an input/output interface 315, an input device 316, and an output device 317. These components are connected with each other by a bus. The memory 312, the auxiliary storage apparatus 313 or a combination of the memory 312 and the auxiliary storage apparatus 313 is a storage apparatus and includes a storage medium that stores software) (Para. [0052], Fig. 3).
Pertinent Art Cited
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The closest prior art Shida (Pub: 2019/0384275) disclose a diagnostic device which diagnoses a sign and a cause of abnormality of a plant, and includes a reception unit that receives from the plant, operation data indicating an operation state of the plant; a storage unit that stores first information including a first abnormal event which occurred in the past in the plant, at least one or more first attribution events that are causes of the first abnormal event, and a first occurrence probability that is an occurrence probability of the first attribution event, in which a causal relationship between the first abnormal event and the first attribution event is indicated by a tree structure, and second information including a second abnormal event which is supposed to occur in the plant but does not occur yet, at least one or more second attribution events that are causes of the second abnormal event, and a second occurrence probability that is an occurrence probability of the second attribution event, in which a causal relationship between the second abnormal event and the second attribution event is indicated by a tree structure; a diagnostic unit that diagnoses abnormality by detecting the sign of the abnormality in the plant, based on the operation data; and an estimation unit that estimates the cause of the sign of the abnormality, based on the first information and the second information, when the sign of the abnormality is detected and diagnosed as abnormality by the diagnostic unit, wherein the estimation unit estimates the cause of the sign of the abnormality, by making weighting of the first occurrence probability heavier than weighting of the second occurrence probability (Para. [0007]).
Lee (Pub: 2019/0094843) disclose the method may also include, based on at least one of the manufacturing process characteristics, determining a prediction time at which to execute a selected machine learning model selected from multiple trained machine learning models, and at the prediction time, executing the selected machine learning model based on process data associated with the manufacturing process, the executing of the selected machine learning model predicting a control set point for future values of state variables of the manufacturing process. The method may also include sending the control set point to a control system to control the manufacturing process by adjusting to the control set point (Para. [0005]).
Foslien (Pub: 2009/0112532) disclose a method includes identifying at least one statistical output associated with a process model and a rate of change associated with each statistical output. The method also includes generating a graphical display that has at least one point, where each point is based on one of the statistical outputs and its associated rate of change. In addition, the method includes presenting the graphical display to a user (Para. [0006]).
Allowable Subject Matter
The claim 2-5 are allowable once the outstanding rejection 35 U.S.C 112(b) and 35 U.S.C 101 abstract idea rejection is overcome as discussed above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JIGNESHKUMAR C PATEL whose telephone number is (571)270-0698. The examiner can normally be reached Monday - Friday, 7:00 AM - 5:00 PM.
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/JIGNESHKUMAR C PATEL/Primary Examiner, Art Unit 2116