DETAILED ACTION
This action is filed in response to the application filed on 9/06/2023.
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 .
Information Disclosure Statement
Acknowledgement is made of Applicant’s Information Disclosure Statements (IDS) form PTO-1149 filed on 9/06/2023, 9/28/2023, 2/10/2025, and 3/05/2026. These IDS have been considered.
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 1-13, 15, 17-20 and 39 are rejected under 35 U.S.C. 101. The claimed invention is directed to the abstract concept of performing mental steps without significantly more. Claim 1, and similarly Claims 20 and 39 recites the following abstract concepts in BOLD of:
A method comprising: extracting, using a computer system, training data comprising one or more parameters from each catalyst of a plurality of catalysts, wherein each parameter is collected from a respective catalyst of the plurality of catalyst;
classifying the training data in accordance with at least one catalyst feature at least one of the contaminations of the catalyst and the aging time of the catalyst;
determining a feature vector from the classified training data based on the one or more parameters extracted from catalyst of the plurality of catalysts, wherein the feature vector is indicative of whether the catalyst performs normally or abnormally;
generating, using the computer system, a machine learning model, wherein the machine learning model is trained based on the feature vector, to predict the function and performance of a catalyst;
generating, using the computer system, a performance baseline curve from the training data in accordance with the destruction removal efficiency (DRE) of a gas; and providing, by the computer system based on the trained machine learning model, a maintenance recommendation for the catalyst.
Under Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category as Claim 1 discloses a method, Claim 20 discloses a system, and Claim 39 teaches a non-transitory computer readable medium.
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portions constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers performing mathematics or mental steps. The steps of extracting training data can be interpreted as a mental process that can be performed in the human mind as teaches selecting data out of an existing pool based on certain parameters. This process of selection can be a mental process.
The step of classifying the training data is also a mental process.
The step of determining a feature vector based on the data, as well as the step of generating a performance baseline curve can both be considered as performing mathematics or a mental process depending on one's interpretation of the limitation.
The step of generating a machine learning model to predict the function and performance of a catalyst can be interpreted as performing mathematics.
And finally, the step of providing a maintenance recommendation can be interpreted as a mental process the limitation discloses looking at data and providing a binary decision based on that observation.
Examiner notes Claims 1, 20, and 39 disclose and are directed towards computer implemented mental steps, which are ineligible under 35 USC 101. Examiner cites to a recent PTAB decision in Application 15/347,806 from 3/03/2025, which said “That is the mental step, implemented using software on a computer, of fitting vibration measurement data to IBDs. ‘[M]erely requir[ing] generic computer implementation[s] fail[s] to transform that abstract idea into a patent-eligible invention.’ Alice, 573 U.S. at 221. That computer-implemented mental step is what the claimed invention is directed toward.”
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; effecting a transformation or reduction of a particular article to a different state or thing. Examiner notes that the claimed methods and system are not tied to a particular machine or apparatus, they do not represent an improvement to another technology or technical field, furthermore as stated in MPEP 2106.05(a) “It is important to note, the judicial exception alone cannot provide the improvement.”
Additionally, there is nothing in the claims that indicates an improvement to the functioning of the computer itself or transform a particular article to a new state. Examiner also points to a 4/04/2023 PTAB decision in Application 15/119/889 in regards to deciding to performing maintenance not being considered an improvement, “But merely restoring the functionality of an air-conditioning system through prioritized preventative maintenance does not represent an improvement to the functioning of a technology that integrates the underlying abstract idea of claim 1 into a patent-eligible application. Rather, the improvement here is to the abstract idea itself, i.e., to the process of determining the divergence and performance and using those determinations to prioritize and perform the maintenance.”
Under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because a computer system, a machine learning model, and a computer program code segment are generic computer elements and not considered significantly more than the abstract idea. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94.
Additionally, while Examiner notes the step of providing a maintenance recommendation to be a mental process, Examiner further notes the courts have found performing maintenance to be a well known routine and conventional activity, as well as designating the performance of maintenance to be insignificant post solution activity (e.g. see [PTAB decision in Application 15/119889 issued on 4/04/2023] “As discussed above, such maintenance merely represents insignificant post-solution activity. MPEP § 2106.05(g). It is not an additional element beyond the abstract idea. As discussed below as part of the step 2B judicial exception analysis, the claimed prioritized maintenance encompasses activities that were well known”).
Claims 2-13, 15, 17-19 Claims further limit the abstract ideas without integrating the abstract concept into a practical application or including additional limitations that can be considered significantly more than the abstract idea:
Claims 2, 7, 17, and 19 further limit the mental processes performed in Claim 1 and does not integrate them into any practical application.
Claims 8 and 9 further limit both the mental processes and the mathematics performed in claim 1 without significantly more.
Claims 12-13 and 15 disclose the type of data gathered and does not integrate the abstract ideas into a practical application. The limitation amounts to necessary data gathering and outputting. See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering).
Claims 3-6, 10-11, and 18 disclose selecting a particular data source or type of data to be manipulated which is insignificant extra solution activity. See MPEP 2106.05(g) “Below are examples of activities that the courts have found to be insignificant extra solution activity:…iii. Selecting information, based on types of information and availability of information in a power grid environment for collection, analysis, and display, Electric Power Group LLC v Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Circ. 2016).”
Allowable Subject Matter
Claims 1-13, 15, 17-20 and 39 contain allowable subject matter. The following is a statement of reasons for indication of allowable subject matter:
Regarding Claims 1 and 20 Examiner notes the closest prior art to be Ikedo (US20190292970 A1), Bogojeski, Mihail et al. “Forecasting Industrial Aging Processes with Machine Learning Methods.” Comput. Chem. Eng. 144 (2020) (hereinafter “Bogojeski”), Cella (US20190324432A1), Campbell (US10532315 B1), and Cheng (CN111177915 A).
Examiner notes Ikedo teaches a method comprising: extracting, using a computer system, training data (e.g. see [0029] “The program causes a computer to execute an acquisition step and an estimation step. The acquisition step is a step in which a sensor acquires information about a catalyst that removes a toxic substance in an exhaust gas”) comprising one or more parameters from each catalyst of a plurality of catalysts, wherein each parameter is collected from a respective catalyst of the plurality of catalyst (e.g. see [0019] “The plurality of the first models may be created using training data acquired from a plurality of the catalysts having different deterioration degrees (i.e. a parameter)”);
classifying the training data in accordance with at least one catalyst feature (e.g. see [0014] “The first model may receive at least one of the temperature of a front end of the catalyst in the main passage”) at least one of the contaminations of the catalyst (e.g. see [0014] “The first model may…output the removal efficiency for nitrogen oxide in the catalyst. The processor may be configured to estimate the removal efficiency for the nitrogen oxide as the removal performance of the catalyst”);
determining a feature vector from the classified training data based on the one or more parameters extracted from catalyst of the plurality of catalysts (e.g. see [0050] “A vector U that is an input variable of the first model 121 is configured by n components (u1, u2, . . . , un; n is a natural number). As the components of the vector U, parameters shown in the following items a1 to a3 can be employed. (a1) Information about Catalyst: at least one of the temperature of a front end of the SCR catalyst 40, the temperature of the SCR catalyst 40, and the adsorption amount of NH3 that is adsorbed”), wherein the feature vector is indicative of whether the catalyst performs normally or abnormally (e.g. see [0075] “In this way, in step S26, the estimation unit 110 estimates the NOx removal efficiency NOxConv[k] as the removal performance of the SCR catalyst 40, using the first model 121. If there is a causal relation between the input and output variables (for example, the input variable vector U and the output variable Z), the machine learning model such as the first model 121 can obtain an output (estimation result) at a low computation load”).
Examiner notes the cited prior art does not disclose or render obvious the method as claimed comprising, classifying the training data in accordance with the aging time of the catalyst;
generating, using the computer system, a machine learning model, wherein the machine learning model is trained based on the feature vector, to predict the function and performance of a catalyst;
generating, using the computer system, a performance baseline curve from the training data in accordance with the destruction removal efficiency (DRE) of a gas; and providing, by the computer system based on the trained machine learning model, a maintenance recommendation for the catalyst.”
Claims 2-13, 15, and 17-19 would be allowable based on their dependence on Claim 1.
Regarding Claim 39, Examiner notes the closest prior art to be Ikedo (US20190292970 A1), Bogojeski, Mihail et al. “Forecasting Industrial Aging Processes with Machine Learning Methods.” Comput. Chem. Eng. 144 (2020) (hereinafter “Bogojeski”), Cella (US20190324432A1), Campbell (US10532315 B1), and Cheng (CN111177915 A). Examiner notes Ikedo teaches the same limitations as Claims 1 and 20, and further discloses A non-transitory computer readable medium comprising: a computer program code segment (e.g. see [0002] “The present disclosure relates to a catalyst state estimation apparatus, a catalyst state estimation method with an information processing apparatus, and a non-transitory recording medium in which a program is stored”).
Examiner notes the cited prior art does not disclose or render obvious the method as claimed comprising, classifying the training data in accordance with the aging time of the catalyst;
generating, using the computer system, a machine learning model, wherein the machine learning model is trained based on the feature vector, to predict the function and performance of a catalyst;
generating, using the computer system, a performance baseline curve from the training data in accordance with the destruction removal efficiency (DRE) of a gas; and providing, by the computer system based on the trained machine learning model, a maintenance recommendation for the catalyst.”
Conclusion
Examiner notes while there are no prior art rejections, Examiner is unable to comment on the allowability of the claims until the 35 U.S.C. 101 Rejections are addressed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NYLA GAVIA whose telephone number is (703)756-1592. The examiner can normally be reached M-F 8:30-5:30pm.
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/NYLA GAVIA/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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