DETAILED ACTION 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. Claim Status Claims 1-9 are pending for examination. Claim Rejections - 35 USC § 102 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 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 , 4, 6 and 8 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Amirthasamy (Pub. No.: US 2018/0218586 A1) . Regarding claim 1, Amirthasamy teaches a n industrial process automation system ( Abstract, para [0004], Figs. 1-6, a process control plant 100 ) , including: a field device level including a plurality of autonomous field devices ( Fig. 2, field device 114 with controller 200, abstract, “ The field device has a digital valve controller including a process parameter monitor to monitor a process parameter of the field device and an alarm determiner to detect an error condition associated with the process parameter. ” and para [0052], “ While an example manner of implementing the field device controller 200 and/or the controller 202 (e.g., the host 122) of FIG. 1 is illustrated in FIG. 2 , one or more of the elements, processes and/or devices illustrated in FIG. 2 may be combined, divided, re-arranged, omitted, eliminated and/or implemented in any other way. ” . The process control plant comprises of a plurality of field device 114 that includes a controller 200 ) ; and an edge device controlling data flow between the field device level and a cloud/server level ( Fig. 2, the host controller 102 connects the field device 114 and its controller 200 to the operator station 104 ) ; wherein each of the plurality of autonomous field devices includes: a signal generating module generating a measurement signal ( Fig. 2 , para [0035], the field device 114 generate s measurement signal to the system condition monitor 20 6 and process parameter monitor 204. ) ; a diagnostic module generating diagnostic data based on the measurement signal ( Fig. 2, para [003 5 ] - [00 39 ], the system condition monitor 206 and process parameter monitor 204 generate diagnostic data to the alarm determiner 208 based on the data received from the field device 114. ) ; an anomaly detection algorithm identifying an anomaly based on the diagnostic data ( Fig. 2, para [0041], “ To determine an alarm condition, the alarm determiner 208 of the illustrated example compar es one or more process parameter(s) received from the process parameter monitor 204 to threshold process parameter(s) and/or receives one or more device operational condition(s) from the system condition monitor 206. For example, the alarm determiner 208 determines that an alarm condition exists when a process parameter (e.g., a supply pressure) is less than a threshold process parameter (e.g., a threshold supply pressure) retrieved from, for example, the data store 212. The threshold process parameter(s) and alert condition(s) to be monitored by the alarm determiner 208 may be user defined and/or can be provided to the data store 212 via the user input 210. Example parameter(s) and/or alarm settings that the alarm determiner 208 of the illustrated example may monitor can include for example, but not limited to, valve alerts, device failure alerts, process plant alerts, diagnostic alerts, miscellaneous alerts, and/or any other alert(s). For example, valve alerts may include travel low alert, a travel Hi alert, a travel deviation alert, an out of range drive signal alert, etc. ”. The alarm determiner 208 determines an anomaly based on the data received from system condition monitors 206 and process parameter monitor 204 ) ; and a communication module transmitting the anomaly to the edge device diagnostic data to the edge device and then to the cloud/server level ( Fig. 2, interface 214 and para [0028], “ To allow process control system operators to visually perceive the temporal relationships of the alarms, as well as state changes and/or manual control actions of the smart field devices 110 and 112 due to delays in transmission of information from the field devices 110, 112 to the controller 102, the example operator station 104 includes and/or implements an alarm presentation interface to graphically display all active alarms in a timeline. ” ) . Regarding claim 4 , Amirthasamy teaches the industrial process automation system of claim 1, wherein at least one of the plurality of autonomous field devices includes a sensor configured to detect temperature, pressure, level, or flow in a process ( para [0024], “ The example field devices 110, 112, and 114 includes input devices capable of receiving inputs to control a process via, for example, valves, pumps, fans, heaters, coolers, and/or other devices. The example process control system 100 also includes output devices capable of generating outputs such as, for example, thermometers, pressure gauges, flow meters, and/or other devices. ” ) . Regarding claim 6 , Amirthasamy teaches the industrial process automation system of claim 1, wherein the anomaly detection algorithm is based on a comparison of the received diagnosis data with an expected value ( para [0041], “ T o determine an alarm condition, the alarm determiner 208 of the illustrated example compar es one or more process parameter(s) received from the process parameter monitor 204 to threshold process parameter(s) and/or receives one or more device operational condition(s) from the system condition monitor 206. For example, the alarm determiner 208 determines that an alarm condition exists when a process parameter (e.g., a supply pressure) is less than a threshold process parameter (e.g., a threshold supply pressure) retrieved from, for example, the data store 212. ” ) . Regarding claim 8 , Amirthasamy teaches the industrial process automation system of claim 1, wherein the anomaly detection algorithm is configured to detect variations of the measurement signals outside of the normal operating range ( para [0041], “ For example, valve alerts may include travel low alert, a travel Hi alert, a travel deviation alert, an out of range drive signal alert, etc. ” ) . 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. 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 . Claim s 5 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Amirthasamy (Pub. No.: US 2018/0218586 A1) in view of Periaswamy (Pub. No.: US 2019/0098035 A1) . Regarding claim 5 , Amirthasamy teaches the industrial process automation system of claim 1, but fails to teach wherein the anomaly detection algorithm includes a machine learning algorithm. However, in the same field anomaly detection, Periaswamy teaches an anomaly detection system that uses machine learning to detect anomaly . See para [0008], “ There is accordingly also a requirement for machine learn ing based anomaly detection solutions that reduce the overall computing overheads while simultaneously improving accuracy of such anomaly detectio n. ”. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Amirthasamy ’s field device controller with a machine learning algorithm to improve detection accuracy. Regarding claim 7 , Amirthasamy teaches the industrial process automation system of claim 1, but fails to teach wherein the plurality of autonomous field devices and the edge device communicate via an industrial wireless communication protocol. However, in the same field anomaly detection, Periaswamy teaches an anomaly detection system that uses wireless communication . See Fig. 1, para [00 91 ], “ The communication channel(s) 808 allow communication over a communication medium to various other computing entities. The communication medium provides information such as program instructions, or other data in a communication media. The communication media includes, but is not limited to, wired or wireless methodologies implemented with an electrical, optical, RF, infrared, acoustic, microwave, Bluetooth or other transmission media. ”. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Amirthasamy’s control system to use wireless communication to extend range. Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Amirthasamy (Pub. No.: US 2018/0218586 A1) in view of Yoshino (Pub. No.: US 2015/0241871 A1) . Regarding claim 2 , Amirthasamy teaches the industrial process automation system of claim 1, but fails to teach wherein each of the plurality of autonomous field devices has a global positioning system (GPS) location, wherein the GPS location is transmitted with the anomaly to the edge device. However, in the same field of field device, Yoshino teaches a field device includes a GPS unit. See Fig 1, Fig. 3 and para [0094], “ The position information acquirer 46 includes, for example, a GPS receiving unit, and acquires the own position information based on electrical waves received from a GPS satellite. ”. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Amirthasamy’s field device with a GPS locator to accurately determine the location of the field device. Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Amirthasamy (Pub. No.: US 2018/0218586 A1) in view of Yoshino (Pub. No.: US 2015/0241871 A1) as applied to claim 2, and further in view of Kawauchi (Pub. No.: US 2020/0182654 A1). Regarding claim 3 , the combination teach es the industrial process automation system of claim 2, but fails to teach wherein the edge device or the cloud/server level is configured to display a map of the industrial process automation system identifying locations of anomalies throughout the system. However, in the same field of abnormality detection , Kawauchi teaches central device/server is configured to display a map that shows the locations of the abnormal field devices/meters. See Fig. 2 – Fig. 4, and abstract “ Gas meter and center device are included. Gas meter is installed in a residence or the like of each customer, and includes flow rate measurer. Center device collects information obtained by gas meter. In addition, center device manages positional information indicating a position of gas meter, and displays information including a result of analyzing information obtained from gas meter, together with the position of gas meter on a map . By employing this configuration, the information obtained from gas meter is collected by center device, and information based on collected data is displayed on a map on which gas meter is positioned. This enables the information obtained from gas meter to be visually reported. ”. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Amirthasamy’s operator station /server 104 to display a map that shows the locations of the abnormal field devices to improve visual notification that is easily recognized by the operator. Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Amirthasamy (Pub. No.: US 2018/0218586 A1) in view of Kawauchi (Pub. No.: US 2020/0182654 A1). Regarding claim 9 , Amirthasamy teach the industrial process automation system of claim 1, but fails to teach wherein the anomaly detection algorithm generates a process disturbance map illustrating multiple anomalies. However, in the same field of abnormality detection , Kawauchi teaches central device/server is configured to display a map that shows the locations of the abnormal field devices/meters. See Fig. 2 – Fig. 4, and abstract “ Gas meter and center device are included. Gas meter is installed in a residence or the like of each customer, and includes flow rate measurer. Center device collects information obtained by gas meter. In addition, center device manages positional information indicating a position of gas meter, and displays information including a result of analyzing information obtained from gas meter, together with the position of gas meter on a map . By employing this configuration, the information obtained from gas meter is collected by center device, and information based on collected data is displayed on a map on which gas meter is positioned. This enables the information obtained from gas meter to be visually reported. ”. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Amirthasamy’s operator station/server 104 to display a map that shows the locations of the abnormal field devices to improve visual notification that is easily recognized by the operator. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT ZHEN Y WU whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-5711 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Monday-Friday, 10AM-6PM, EST . 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Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /ZHEN Y WU/ Primary Examiner, Art Unit 2685