DETAILED ACTION The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 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. Claim(s) 16 is/are 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 claim is directed towards “ A computer-readable recording medium ” . The claim should be directed towards “A non-transitory computer-readable recording medium”. Claim(s) 1-16 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more ( See 2019 Update: Eligibility Guidance ). Independent Claim (s) 1, 16 recites monitoring an abnormal condition, …: determining an operation mode based on condition information in a target area in which an abnormal condition is monitored; receiving sensing signals from a plurality of sensors installed in the target area in response to a first mode in which different objects are simultaneously detected being determined as the operation mode; processing the sensing signals and outputting first sensing data obtained by converting the sensing signals into digital signals; generating first input data to be input to a first neural network model by variably adjusting a size of the first sensing data; performing an operation in an analog domain on the first input data using the first neural network mode and outputting first operation data obtained by converting an operation result into digital data; and outputting a first monitoring result obtained by classifying an abnormal condition based on the first operation data [Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation] and/or [Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)] . Independent Claim (s) 14 recites abnormal condition monitoring, …: determine an operation mode based on condition information in a target area in which an abnormal condition is monitored, process sensing signals received from a plurality of sensors installed in the target area in response to a first mode in which different objects are simultaneously determined being determined as the operation mode, and output first sensing data obtained by converting the sensing signals into digital signals, generate first input data to be input to a first neural network mode by variably adjusting a size of the first sensing data, perform an operation in an analog domain on the first input data using the first neural network mode, and output first operation data obtained by converting an operation result into digital data, and output a first monitoring result obtained by classifying an abnormal condition based on the first operation data [Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation] and/or [Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)] . In combination with Independent Claim (s) 1, 14 , Claim(s) 2-13, 15 recite(s) the outputting first sensing data comprises: selecting one of the sensing signals based on a first selection control signal; amplifying the selected sensing signal based on an amplification control signal and a gain control signal; and performing digital conversion on the amplified sensing signal to output the sensing data. updating the condition information in the target area based on the first monitoring result. selecting a target sensor from among the plurality of sensors installed in the target area in response to a second mode in which an object related to the condition information is detected in real time being determined as the operation mode; processing a target signal received from the target sensor and outputting second sensing data obtained by converting the target signal into a digital signal; generating second input data to be input to a second neural network model by variably adjusting a size of the second sensing data; performing an operation in the analog domain on the second input data using the second neural network model and outputting second operation data obtained by converting an operation result into digital data; and outputting a second monitoring result obtained by classifying an abnormal condition based on the second operation data. the selecting a target sensor comprises selecting a sensor configured to detect a main object related to the condition information as a main target sensor among the plurality of sensors, and selecting a sensor configured to detect a sub-object related to the main object as a sub- target sensor. the outputting second sensing data comprises: receiving the target signal from the target sensor based on a second selection control signal in response to the target sensor being selected; amplifying the target signal based on an amplification control signal and a gain control signal; and outputting the second sensing data by performing digital conversion on the amplified target signal. the second neural network model is a model used in response to the second mode being determined as the operation mode, and is a model that performs the operation by considering input data at a time prior to a time at which the operation is performed. the generating second input data comprises accumulating the second sensing data to correspond to a number of bits enabled within a maximum resolution based on an enable signal that specifies the number of bits, and generating the second input data having an output resolution corresponding to the number of bits. the outputting second operation data comprises: converting the second input data into a second input value in the analog domain; performing a convolution operation in the analog domain on the second input value using a plurality of SRAM operators; and outputting the second operation data obtained by converting a second analog convolution result, which is an output value of the operation, into data in the digital domain. the outputting a second monitoring result comprises: determining a category of the abnormal condition in response to an object detected by the target sensor; and outputting the second monitoring result by classifying a risk level within the determined category of the abnormal condition. the outputting a second monitoring result comprises: determining a category of the abnormal condition based on an abnormal condition classification result for each of the main object and the sub-object; and outputting the second monitoring result by considering risk level classification for the main object and risk level classification for the sub-object within the determined category of the abnormal condition. updating the condition information in the target area based on the second monitoring result [Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation] and/or [Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)] . This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP § 2106 .05(f) ) (i.e. A computer-readable recording medium on which is stored a program for executing ; a memory configured to store at least one program; and a processor configured to execute the at least one program, wherein the processor is configured to: ); Adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106 .05(g) ) (i.e. generic data acquisition ); or Generally linking the use of the judicial exception to a particular technological environment or field of use ( MPEP § 2106 .05(h) ) (i.e. based on multiple sensors ; wherein the plurality of sensors includes at least one of a gas sensor, a pressure sensor, or a temperature sensor ). The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because looking at the additional elements 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. The additional elements simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106 .05(d) ) (i.e. See Alice Corp. and cited references for evidence of additional elements (i.e., generic computer structure; generic sensors )). 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. 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 ) 1-9, 12-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over BHAUMIK ET AL. ( US 20210243260 A1 ) (hereinafter “ BHAUMIK ”) in view of YOON ET AL. ( KR 10-2389444 B1 ) (hereinafter “ YOON ”) (Refer to attached IP.com translation for citations) . With respect to Claim(s) 1 , BHAUMIK teaches applying a hierarchical sensor selection process and adaptively chooses sensors among multiple sensors deployed in the IoT network. Further, on-the-fly changes operation modes of the sensors to automatically produce the best possible inference from the selected sensor data, in time, power and latency at the edge. Further, sensors of the system include a waveform and diversity control mechanism that enables controlling of an excitation signal of the sensor and the BRI of : monitoring an abnormal condition based on multiple sensors ( See, e.g., ¶ ABSTRACT ) , …: determining an operation mode based on condition information in a target area in which an abnormal condition is monitored ( See, e.g., ¶ 0007, 0010, 0042 ) ; receiving sensing signals from a plurality of sensors installed in the target area in response to a first mode in which different objects are simultaneously detected being determined as the operation mode ( See, e.g., ¶ 0007, 0050, 0051, 0074 ) ; processing the sensing signals and outputting first sensing data ( See, e.g., ¶ 0007, 0008, 0048 ); and outputting a first monitoring result obtained by classifying an abnormal condition based on the first operation data ( See, e.g., ¶ 0048, 0063 ) . However, BHAUMIK is lacking the explicit language of: sensing data obtained by converting the sensing signals into digital signals; generating first input data to be input to a first neural network model by variably adjusting a size of the first sensing data; performing an operation in an analog domain on the first input data using the first neural network mode and outputting first operation data obtained by converting an operation result into digital data. YOON teaches a scalable analog PIM module, an analog PIM control method, a signal processing circuit and a sensor device and the BRI of: sensing data obtained by converting the sensing signals into digital signals; generating input data to be input to a neural network model by variably adjusting a size of the sensing data; performing an operation in an analog domain on the input data using the neural network mode and outputting operation data obtained by converting an operation result into digital data ( See, e.g., Page(s) 11 : ¶ 8-11 ) . It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify BHAUMIK to include sensing data obtained by converting the sensing signals into digital signals; generating input data to be input to a neural network model by variably adjusting a size of the sensing data; performing an operation in an analog domain on the input data using the neural network mode and outputting operation data obtained by converting an operation result into digital data. One of ordinary skill in the art would have been motivated to modify BHAUMIK because it would be beneficial to effectively reduce power consumption. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. With respect to Claim(s) 16 , BHAUMIK, YOON teaches the BRI of the parent claim(s). BHAUMIK further teaches the BRI of: A computer-readable recording medium on which is stored a program for executing ( See, e.g., Fig(s). 1, 2 ) the method according to claim 1 on a computer. With respect to Claim(s) 14 , BHAUMIK teaches applying a hierarchical sensor selection process and adaptively chooses sensors among multiple sensors deployed in the IoT network. Further, on-the-fly changes operation modes of the sensors to automatically produce the best possible inference from the selected sensor data, in time, power and latency at the edge. Further, sensors of the system include a waveform and diversity control mechanism that enables controlling of an excitation signal of the sensor and the BRI of: An abnormal condition monitoring device based on multiple sensors ( See, e.g., ¶ ABSTRACT ), …: a memory configured to store at least one program; and a processor configured to execute the at least one program ( See, e.g., Fig(s). 1, 2 ), wherein the processor is configured to: determine an operation mode based on condition information in a target area in which an abnormal condition is monitored ( See, e.g., ¶ 0007, 0010, 0042 ) , process sensing signals received from a plurality of sensors installed in the target area in response to a first mode in which different objects are simultaneously determined being determined as the operation mode ( See, e.g., ¶ 0007, 0008, 0048, 0050, 0051, 0074 ) , and output first sensing data ( See, e.g., ¶ 0007, 0008, 0048, 0050, 0051, 0074 ), and output a first monitoring result obtained by classifying an abnormal condition based on the first operation data ( See, e.g., ¶ 0048, 0063 ) . However, BHAUMIK is lacking the explicit language of: sensing data obtained by converting the sensing signals into digital signals; generate first input data to be input to a first neural network mode by variably adjusting a size of the first sensing data, perform an operation in an analog domain on the first input data using the first neural network mode, and output first operation data obtained by converting an operation result into digital data. YOON teaches a scalable analog PIM module, an analog PIM control method, a signal processing circuit and a sensor device and the BRI of: sensing data obtained by converting the sensing signals into digital signals; generate input data to be input to a neural network model by variably adjusting a size of the sensing data; perform an operation in an analog domain on the input data using the neural network mode and outputting operation data obtained by converting an operation result into digital data ( See, e.g., Page(s) 11 : ¶ 8-11 ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify BHAUMIK to include sensing data obtained by converting the sensing signals into digital signals; generating input data to be input to a neural network model by variably adjusting a size of the sensing data; performing an operation in an analog domain on the input data using the neural network mode and outputting operation data obtained by converting an operation result into digital data. One of ordinary skill in the art would have been motivated to modify BHAUMIK because it would be beneficial to effectively reduce power consumption. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. With respect to Claim(s) 2 , BHAUMIK, YOON teaches the BRI of the parent claim(s). YOON further teaches the BRI of: the outputting first sensing data comprises: selecting one of the sensing signals based on a first selection control signal; amplifying the selected sensing signal based on an amplification control signal and a gain control signal; and performing digital conversion on the amplified sensing signal to output the sensing data ( See, e.g., Page(s) 11: ¶ 3 ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify BHAUMIK to include the outputting first sensing data comprises: selecting one of the sensing signals based on a first selection control signal; amplifying the selected sensing signal based on an amplification control signal and a gain control signal; and performing digital conversion on the amplified sensing signal to output the sensing data. One of ordinary skill in the art would have been motivated to modify BHAUMIK because it would be beneficial to effectively reduce power consumption. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. With respect to Claim(s) 3 , BHAUMIK, YOON teaches the BRI of the parent claim(s). BHAUMIK further teaches the BRI of: updating the condition information in the target area based on the first monitoring result ( See, e.g., ¶ 0048, 0063 ). With respect to Claim(s) 4, 15 , BHAUMIK, YOON teaches the BRI of the parent claim(s). BHAUMIK further teaches the BRI of: selecting a target sensor from among the plurality of sensors installed in the target area in response to a second mode in which an object related to the condition information is detected in real time being determined as the operation mode ( See, e.g., ¶ 0007, 0010, 0042 ) ; processing a target signal received from the target sensor and outputting second sensing data ( See, e.g., ¶ 0007, 0008, 0048, 0050, 0051, 0074 ); and outputting a second monitoring result obtained by classifying an abnormal condition based on the second operation data ( See, e.g., ¶ 0048, 0063 ). YOON further teaches the BRI of: sensing data obtained by converting the sensing signals into digital signals; generate input data to be input to a neural network model by variably adjusting a size of the sensing data; perform an operation in an analog domain on the input data using the neural network mode and outputting operation data obtained by converting an operation result into digital data ( See, e.g., Page(s) 11: ¶ 8-11 ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify BHAUMIK to include sensing data obtained by converting the sensing signals into digital signals; generating input data to be input to a neural network model by variably adjusting a size of the sensing data; performing an operation in an analog domain on the input data using the neural network mode and outputting operation data obtained by converting an operation result into digital data. One of ordinary skill in the art would have been motivated to modify BHAUMIK because it would be beneficial to effectively reduce power consumption. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. With respect to Claim(s) 5 , BHAUMIK, YOON teaches the BRI of the parent claim(s). BHAUMIK further teaches the BRI of: the selecting a target sensor comprises selecting a sensor configured to detect a main object related to the condition information as a main target sensor among the plurality of sensors ( See, e.g., ¶ 0007, 0010, 0042 ), and selecting a sensor configured to detect a sub-object related to the main object as a sub- target sensor ( See, e.g., ¶ 0007, 0010, 0042 ). With respect to Claim(s) 6 , BHAUMIK, YOON teaches the BRI of the parent claim(s). YOON further teaches the BRI of: the outputting second sensing data comprises: receiving the target signal from the target sensor based on a second selection control signal in response to the target sensor being selected; amplifying the target signal based on an amplification control signal and a gain control signal; and outputting the second sensing data by performing digital conversion on the amplified target signal ( See, e.g., Page(s) 11: ¶ 3 ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify BHAUMIK to include the outputting second sensing data comprises: receiving the target signal from the target sensor based on a second selection control signal in response to the target sensor being selected; amplifying the target signal based on an amplification control signal and a gain control signal; and outputting the second sensing data by performing digital conversion on the amplified target signal. One of ordinary skill in the art would have been motivated to modify BHAUMIK because it would be beneficial to effectively reduce power consumption. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. With respect to Claim(s) 7 , BHAUMIK, YOON teaches the BRI of the parent claim(s). YOON further teaches the BRI of: the second neural network model BHAUMIK further teaches the BRI of: a model used in response to the second mode being determined as the operation mode, and is a model that performs the operation by considering input data at a time prior to a time at which the operation is performed ( See, e.g., ¶ 0056, 0057, 0062 ). With respect to Claim(s) 8 , BHAUMIK, YOON teaches the BRI of the parent claim(s). YOON further teaches the BRI of: the generating second input data comprises accumulating the second sensing data to correspond to a number of bits enabled within a maximum resolution based on an enable signal that specifies the number of bits, and generating the second input data having an output resolution corresponding to the number of bits ( See, e.g., Page(s) 11: ¶ 8-11 ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify BHAUMIK to include the generating second input data comprises accumulating the second sensing data to correspond to a number of bits enabled within a maximum resolution based on an enable signal that specifies the number of bits, and generating the second input data having an output resolution corresponding to the number of bits. One of ordinary skill in the art would have been motivated to modify BHAUMIK because it would be beneficial to effectively reduce power consumption. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. With respect to Claim(s) 9 , BHAUMIK, YOON teaches the BRI of the parent claim(s). YOON further teaches the BRI of: the outputting second operation data comprises: converting the second input data into a second input value in the analog domain; performing a convolution operation in the analog domain on the second input value using a plurality of SRAM operators; and outputting the second operation data obtained by converting a second analog convolution result, which is an output value of the operation, into data in the digital domain ( See, e.g., Page(s) 12-15 ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify BHAUMIK to include the outputting second operation data comprises: converting the second input data into a second input value in the analog domain; performing a convolution operation in the analog domain on the second input value using a plurality of SRAM operators; and outputting the second operation data obtained by converting a second analog convolution result, which is an output value of the operation, into data in the digital domain One of ordinary skill in the art would have been motivated to modify BHAUMIK because it would be beneficial to effectively reduce power consumption. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. With respect to Claim(s) 12 , BHAUMIK, YOON teaches the BRI of the parent claim(s). BHAUMIK further teaches the BRI of: updating the condition information in the target area based on the second monitoring result ( See, e.g., ¶ 0048, 0063 ). With respect to Claim(s) 13 , BHAUMIK, YOON teaches the BRI of the parent claim(s). BHAUMIK further teaches the BRI of: wherein the plurality of sensors includes at least one of a gas sensor, a pressure sensor, or a temperature sensor ( See, e.g., ¶ 0037 ). Claim ( s ) 10, 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over the cited references of the parent claim(s) in view of STELL ( US 8294567 B1 ) . With respect to Claim(s) 10 , BHAUMIK, YOON teaches the BRI of the parent claim(s). BHAUMIK, YOON further teaches the BRI of: the outputting a second monitoring result. However, BHAUMIK is lacking the explicit language of: determining a category of the abnormal condition in response to an object detected by the target sensor; and outputting the second monitoring result by classifying a risk level within the determined category of the abnormal condition. STELL teaches an automated system includes a combination of sensors configured to measure various factors associated with a hazard, such as a fire or gas leakage, and generate sensor readings. Factors measured can include smoke, carbon monoxide and heat. The system further includes a detection device that is configured to determine whether a hazard or fire exists by performing a fuzzy analysis of sensor readings. The fuzzy analysis includes categorizing respective sensor readings into fuzzy sets, and determining whether the hazard exists based on a combination of the categorizations. In addition the size and direction of a fire can be determined from multiple sensors and the BRI of: determining a category of the abnormal condition in response to an object detected by the target sensor; and outputting the monitoring result by classifying a risk level within the determined category of the abnormal condition ( See, e.g., Col 6 Line(s) 16-43 ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify BHAUMIK to include determining a category of the abnormal condition in response to an object detected by the target sensor; and outputting the monitoring result by classifying a risk level within the determined category of the abnormal condition. One of ordinary skill in the art would have been motivated to modify BHAUMIK because it would be beneficial to categorize potential hazards. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. With respect to Claim(s) 11 , BHAUMIK, YOON teaches the BRI of the parent claim(s). BHAUMIK, YOON further teaches the BRI of: the outputting a second monitoring result. However, BHAUMIK is lacking the explicit language of: determining a category of the abnormal condition based on an abnormal condition classification result for each of the main object and the sub-object; and outputting the second monitoring result by considering risk level classification for the main object and risk level classification for the sub-object within the determined category of the abnormal condition. STELL teaches an automated system includes a combination of sensors configured to measure various factors associated with a hazard, such as a fire or gas leakage, and generate sensor readings. Factors measured can include smoke, carbon monoxide and heat. The system further includes a detection device that is configured to determine whether a hazard or fire exists by performing a fuzzy analysis of sensor readings. The fuzzy analysis includes categorizing respective sensor readings into fuzzy sets, and determining whether the hazard exists based on a combination of the categorizations. In addition the size and direction of a fire can be determined from multiple sensors and the BRI of:: determining a category of the abnormal condition based on an abnormal condition classification result for each of the main object and the sub-object; and outputting the monitoring result by considering risk level classification for the main object and risk level classification for the sub-object within the determined category of the abnormal condition ( See, e.g., Col 6 Line(s) 16-43 ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify BHAUMIK to include determining a category of the abnormal condition based on an abnormal condition classification result for each of the main object and the sub-object; and outputting the monitoring result by considering risk level classification for the main object and risk level classification for the sub-object within the determined category of the abnormal condition. One of ordinary skill in the art would have been motivated to modify BHAUMIK because it would be beneficial to categorize potential hazards. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT RAYMOND NIMOX whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (469)295-9226 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Mon-Thu 10am-8pm CT . 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. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. FILLIN "Examiner Stamp" \* MERGEFORMAT RAYMOND NIMOX Primary Examiner Art Unit 2857 /RAYMOND L NIMOX/ Primary Examiner, Art Unit