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. 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. DETAILED ACTION Election/Restrictions 1. A response on 0 3 / 16 /20 26 a provisional election was made without traverse to prosecute the invention of claims 1- 7 and 14-19 . Claims 8 - 13 are withdrawn from further consideration by the examiner, 37 CFR 1.142(b), as being drawn to a non-elected invention. Because these inventions are distinct for the reasons given on action dated 0 2 / 03 /20 26 , restriction for examination purposes as indicated is proper . The requirement is still deemed proper and is therefore made FINAL. 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- 7 and 14-19 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. Claim 1, Step 1 the claim is a process (or machine) ( Yes ), Step 2A Prong One , does the claim recite an abstract idea? current claim related to an artificial intelligence apparatus for detecting a target gas comprising: a mixed gas measurement unit configured to measure a mixed gas collected in a plurality of domains through a sensor array to generate sensing data including heterogeneous domain measurement data measured from the mixed gas collected in a domain different from the target gas and target domain measurement data measured from the mixed gas collected from the same domain as the target gas appears is an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w] ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes . Step 2A Prong Two , is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? the additional elements of a heterogeneous intelligence model deep learning unit configured to receive the heterogeneous domain measurement data to train a heterogeneous intelligence model; a target intelligence model deep learning unit configured to receive the heterogeneous intelligence model and the target domain measurement data to train a target intelligence model are recited at a high level of generality and merely amount to a particular field of use (see MPEP 2106.05(h)) and/or insignificant post-solution activity (MPEP 2106.05(g)), this does not integrate the Judicial Exception into a practical application, Step 2A Prong Two: NO . Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? The additional element of a target gas detection unit configured to determine whether an environmental gas includes the target gas using the target intelligence model appears to be field of use (See MPEP 2106.05(h) and MPEP 2106.05(f)) and/or merely amounts to insignificant extra-solution output of the results (see MPEP 2106.05(g)) and therefore fails to integrate the abstract idea into a practical application or amount to significantly more. Step 2B: No. claim 1 not eligible. Claim 1 4 , Step 1 the claim is a process (or machine) ( Yes ), Step 2A Prong One , does the claim recite an abstract idea? current claim related to a non-transitory computer-readable medium comprising a program code that, when executed by a processor, causes the processor to: measure a mixed gas collected in a plurality of domains through a sensor array to generate sensing data including heterogeneous domain measurement data measured from the mixed gas collected in a domain different from the target gas and target domain measurement data measured from the mixed gas collected from the same domain as the target gas appears is an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w] ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes . Step 2A Prong Two , is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? the additional elements of receive the heterogeneous domain measurement data to train a heterogeneous intelligence model; receive the heterogeneous intelligence model and the target domain measurement data to train a target intelligence model are recited at a high level of generality and merely amount to a particular field of use (see MPEP 2106.05(h)) and/or insignificant post-solution activity (MPEP 2106.05(g)), this does not integrate the Judicial Exception into a practical application, Step 2A Prong Two: NO . Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? The additional element of determine whether an environmental gas includes the target gas using the target intelligence model appears to be field of use (See MPEP 2106.05(h) and MPEP 2106.05(f)) and/or merely amounts to insignificant extra-solution output of the results (see MPEP 2106.05(g)) and therefore fails to integrate the abstract idea into a practical application or amount to significantly more. Step 2B: No. claim 1 4 not eligible. Claim 2 related to wherein the mixed gas measurement unit is configured to divide the collected mixed gas into a plurality of environmental gas bags, to distribute the target gas to a plurality of target gas bags according to a concentration, to generate a plurality of sample gas bags by mixing each of the environmental gas bags and each of the target gas bags, to set a measurement environment of the sensor array with respect to the sample gas bags, and to measure the mixed gas of each of the sample gas bags depending on the set measurement environment to generate the sensing data appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 2 not eligible. Claim 3 related to wherein the measurement environment includes a measurement temperature, a gas pressure, and a sensor voltage appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 3 not eligible. Claim 4 related to wherein the heterogeneous intelligence model includes a data part intelligence model that trains the sensing data and a domain part intelligence model that trains a characteristic of a domain including the target gas, and wherein the heterogeneous intelligence model deep learning unit reads and preprocesses the heterogeneous domain measurement data, trains the heterogeneous intelligence model using the preprocessed heterogeneous domain measurement data, deletes the domain part intelligence model from the trained heterogeneous intelligence model, and stores only the data part intelligence model in a heterogeneous intelligence model database appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 4 not eligible. Claim 5 related to wherein the target intelligence model deep learning unit configures a target domain part intelligence model encompassing data part intelligence models fetched from the heterogeneous intelligence model database, connects the data part intelligence models and the target domain part intelligence model to configure the target intelligence model, fixes the data part intelligence models, reads and preprocesses the target domain measurement data, trains the target intelligence model using the preprocessed target domain measurement data, and stores the trained target intelligence model in a target intelligence model database appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 5 not eligible. Claim 6 related to wherein the sensing data is first sensing data, and wherein the target gas detection unit measures the environmental gas through the sensor array to generate second sensing data, preprocesses the second sensing data, determines whether the target gas is included in the environmental gas by inputting the preprocessed second sensing data into the target intelligence model, and visualizes and outputs a determined result appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 6 not eligible. Claim 7 related to wherein the heterogeneous intelligence model and the target intelligence model are trained using any one of a CNN (Convolutional Neural Network), an LSTM (Long Short-Term Memory), an RNN (Recurrent Neural Network), and a ResNet a ppears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 7 not eligible. Claim 15 related to wherein the generation of the sensing data includes: dividing the collected mixed gas into a plurality of environmental gas bags; distributing the target gas to a plurality of target gas bags according to a concentration; generating a plurality of sample gas bags by mixing each of the environmental gas bags and each of the target gas bags; setting a measurement environment of the sensor array with respect to the sample gas bags; and measuring the mixed gas of each of the sample gas bags depending on the set measurement environment to generate the sensing data appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 15 not eligible. Claim 16 related to wherein the heterogeneous intelligence model includes a data part intelligence model that trains the sensing data and a domain part intelligence model that trains a characteristic of a domain including the target gas, and wherein the training of the heterogeneous intelligence model includes: reading and preprocessing the heterogeneous domain measurement data; training the heterogeneous intelligence model using the preprocessed heterogeneous domain measurement data; deleting the domain part intelligence model from the trained heterogeneous intelligence model; and storing only the data part intelligence model in a heterogeneous intelligence model database appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 16 not eligible. Claim 17 related to wherein the training of the target intelligence model includes: configuring a target domain part intelligence model encompassing data part intelligence models fetched from the heterogeneous intelligence model database; connecting the data part intelligence models and the target domain part intelligence model to configure the target intelligence model; fixing the data part intelligence models; reading and preprocessing the target domain measurement data; training the target intelligence model using the preprocessed target domain measurement data; and storing the trained target intelligence model in a target intelligence model database appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 17 not eligible. Claim 18 related to wherein the sensing data is first sensing data, and wherein the determining of whether the environmental gas includes the target gas includes: measuring the environmental gas through a sensor array to generate second sensing data; preprocessing the second sensing data; determining whether the target gas is included in the environmental gas by inputting the preprocessed second sensing data into the target intelligence model; and visualizing and outputting the determined result appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 18 not eligible. Claim 19 related to wherein the heterogeneous intelligence model and the target intelligence model are trained using any one of a CNN (Convolutional Neural Network), an LSTM (Long Short-Term Memory), an RNN (Recurrent Neural Network), and a ResNet appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 19 not eligible. Claim Rejections - 35 USC § 10 2 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale , or otherwise available to the public before the effective filing date of the claimed invention. Claim(s) 1- 7 and 14-19 are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by JO, HAN JAE , WO 2022145812 A1 , DATE PUBLISHED: 2022-07-07, CPC B01D 53/8625. Regarding claim 1: JO, HAN JAE described an artificial intelligence apparatus for detecting a target gas comprising: a mixed gas measurement unit configured to measure a mixed gas collected in a plurality of domains through a sensor array to generate sensing data ( page 15, 17, mixed with the exhaust gas , measured in real time ) including heterogeneous domain measurement data measured from the mixed gas collected in a domain different from the target gas and target domain measurement data measured from the mixed gas collected from the same domain as the target gas ( page 9, heterogeneous gas) ; a heterogeneous intelligence model deep learning unit configured to receive the heterogeneous domain measurement data to train a heterogeneous intelligence model ( page 10, artificial intelligence learning algorithm) ; a target intelligence model deep learning unit configured to receive the heterogeneous intelligence model and the target domain measurement data to train a target intelligence model ( page 13, learning algorithm of the concept of training with data) ; and a target gas detection unit configured to determine whether an environmental gas includes the target gas using the target intelligence model ( page 14, contents related to the combustion environment such as temperature and humidity) . Regarding claim 1 4 : JO, HAN JAE described a non-transitory computer-readable medium comprising a program code that, when executed by a processor, causes the processor to ( page 16, use computer device ) : measure a mixed gas collected in a plurality of domains through a sensor array to generate sensing data including heterogeneous domain measurement data measured from the mixed gas collected in a domain different from the target gas and target domain measurement data measured from the mixed gas collected from the same domain as the target gas ( page 9, 15, 17, heterogeneous gas mixed with the exhaust gas , measured in real time ) ; receive the heterogeneous domain measurement data to train a heterogeneous intelligence model ( page 9, 10, heterogeneous gas , artificial intelligence learning algorithm ) ; receive the heterogeneous intelligence model and the target domain measurement data to train a target intelligence model ( page 13, learning algorithm of the concept of training with data) ; and determine whether an environmental gas includes the target gas using the target intelligence model ( page 14, contents related to the combustion environment such as temperature and humidity) . Claim 2, JO, HAN JAE further described wherein the mixed gas measurement unit is configured to divide the collected mixed gas into a plurality of environmental gas bags, to distribute the target gas to a plurality of target gas bags according to a concentration ( page 19, 20, different concentration ) , to generate a plurality of sample gas bags by mixing each of the environmental gas bags and each of the target gas bags, to set a measurement environment of the sensor array with respect to the sample gas bags, and to measure the mixed gas of each of the sample gas bags depending on the set measurement environment to generate the sensing data ( page 20, different % of oxygen, nitrogen oxide ) . Claim 3 , JO, HAN JAE further described wherein the measurement environment includes a measurement temperature ( page 26, temperature ) , a gas pressure ( page 26, pressure ) , and a sensor voltage ( page 1 6 , any other various sensors or monitor devices) . Claim 4 , JO, HAN JAE further described wherein the heterogeneous intelligence model includes a data part intelligence model that trains the sensing data and a domain part intelligence model that trains a characteristic of a domain including the target gas, and wherein the heterogeneous intelligence model deep learning unit reads and preprocesses the heterogeneous domain measurement data, trains the heterogeneous intelligence model using the preprocessed heterogeneous domain measurement data, deletes the domain part intelligence model from the trained heterogeneous intelligence model, and stores only the data part intelligence model in a heterogeneous intelligence model database ( page 13, learning algorithm of the concept of training with data , page 9, 15, 17, heterogeneous gas mixed with the exhaust gas , measured in real time , page 12, included in the database ) . Claim 5 , JO, HAN JAE further described wherein the target intelligence model deep learning unit configures a target domain part intelligence model encompassing data part intelligence models fetched from the heterogeneous intelligence model database, connects the data part intelligence models and the target domain part intelligence model to configure the target intelligence model, fixes the data part intelligence models, reads and preprocesses the target domain measurement data, trains the target intelligence model using the preprocessed target domain measurement data, and stores the trained target intelligence model in a target intelligence model database ( page 13, learning algorithm of the concept of training with data , page 9, 15, 17, heterogeneous gas mixed with the exhaust gas , measured in real time , page 12, included in the database ) . Claim 6 , JO, HAN JAE further described wherein the sensing data is first sensing data, and wherein the target gas detection unit measures the environmental gas through the sensor array to generate second sensing data, preprocesses the second sensing data, determines whether the target gas is included in the environmental gas by inputting the preprocessed second sensing data into the target intelligence model, and visualizes and outputs a determined result ( page 13, plurality of nodes formed in the hidden layer processing, page 14, contents related to the combustion environment such as temperature and humidity ) . Claim 7 , JO, HAN JAE further described wherein the heterogeneous intelligence model and the target intelligence model are trained using any one of a CNN (Convolutional Neural Network), an LSTM (Long Short-Term Memory) ( page 12, memory processing ) , an RNN (Recurrent Neural Network) (page 13) , and a ResNet . Claim 15 , JO, HAN JAE further described wherein the generation of the sensing data includes: dividing the collected mixed gas into a plurality of environmental gas bags; distributing the target gas to a plurality of target gas bags according to a concentration ( page 19, 20, different concentration ) ; generating a plurality of sample gas bags by mixing each of the environmental gas bags and each of the target gas bags; setting a measurement environment of the sensor array with respect to the sample gas bags; and measuring the mixed gas of each of the sample gas bags depending on the set measurement environment to generate the sensing data ( page 20, different % of oxygen, nitrogen oxide ) . Claim 16 , JO, HAN JAE further described wherein the heterogeneous intelligence model includes a data part intelligence model that trains the sensing data and a domain part intelligence model that trains a characteristic of a domain including the target gas, and wherein the training of the heterogeneous intelligence model includes: reading and preprocessing the heterogeneous domain measurement data; training the heterogeneous intelligence model using the preprocessed heterogeneous domain measurement data; deleting the domain part intelligence model from the trained heterogeneous intelligence model; and storing only the data part intelligence model in a heterogeneous intelligence model database ( page 13, learning algorithm of the concept of training with data , page 9, 15, 17, heterogeneous gas mixed with the exhaust gas , measured in real time, page 12, included in the database ) . Claim 17 , JO, HAN JAE further described wherein the training of the target intelligence model includes: configuring a target domain part intelligence model encompassing data part intelligence models fetched from the heterogeneous intelligence model database; connecting the data part intelligence models and the target domain part intelligence model to configure the target intelligence model; fixing the data part intelligence models; reading and preprocessing the target domain measurement data; training the target intelligence model using the preprocessed target domain measurement data; and storing the trained target intelligence model in a target intelligence model database ( page 13, learning algorithm of the concept of training with data , page 9, 15, 17, heterogeneous gas mixed with the exhaust gas , measured in real time, page 12, included in the database ) . Claim 18 , JO, HAN JAE further described wherein the sensing data is first sensing data, and wherein the determining of whether the environmental gas includes the target gas includes: measuring the environmental gas through a sensor array to generate second sensing data; preprocessing the second sensing data; determining whether the target gas is included in the environmental gas by inputting the preprocessed second sensing data into the target intelligence model; and visualizing and outputting the determined result ( page 13, plurality of nodes formed in the hidden layer processing, page 14, contents related to the combustion environment such as temperature and humidity ) . Claim 19 , JO, HAN JAE further described wherein the heterogeneous intelligence model and the target intelligence model are trained using any one of a CNN (Convolutional Neural Network), an LSTM (Long Short-Term Memory) ( page 12, memory processing ) , an RNN (Recurrent Neural Network) (page 13) , and a ResNet Contact information 4 . Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tung Lau whose telephone number is (571)272-2274, email is Tungs.lau@uspto.gov. The examiner can normally be reached on Tuesday-Friday 7:00 AM-5:00 PM EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice . If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, TURNER SHELBY, can be reached on 571-272-6334. 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 https://ppair-my.uspto.gov/pair/PrivatePair. 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. /TUNG S LAU/ Primary Examiner, Art Unit 2857 Technology Center 2800 March 24, 2026