DETAILED ACTION
This final office action is in response to amendments filed on 7/21/26.
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 Rejections - 35 USC § 103
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ogun et al., US Patent no. 12,299,547 [Ogun] and Cross et al., US Patent Application Publication no. 2023/0394335 [Cross], in view of Yoon, US Patent Application Publication no. 2024/04191551.
Regarding claims 1, 10 and 17, Ogun discloses a system, comprising: processing circuitry; and a memory, accessible by the processing circuitry, the memory storing instructions that, when executed by the processing circuitry [edge devices which include computing devices that inherently include processing circuitry and memory storing instructions for execution, column 5, lines 13-20], cause the processing circuitry to perform operations comprising:
receiving data collected during operation of an automation device of an automation system configured to perform an automation process [receiving data from IoT devices, such as autonomous vehicles, column 4, lines 14-27 and column 5, lines 13-36];
processing the received data, comprising applying one or more rules to filter the received data, prioritize the received data, or both [filtering data received from the IoT devices, column 5, lines 28-54]; and
transmitting the processed received data to a remote server in the automation system [transmitting the filtered data to a remote server, column 5, lines 34-54].
Ogun discloses a system that includes an edge device that receives data from an IoT device, filters the data and transmits filtered data to a remote server. Ogun does not disclose that the data is filtered based on available bandwidth. Like Ogun, Cross discloses a system that receives data from devices, filters the data and transmits filtered data to a remote server. Specifically, Cross discloses that the received data is processed, filtered or prioritized to make the data fit for transmission over given communication channels [paragraph 0062]. Cross further discloses that raw sensor data may be transmitted using a high-speed communication channel, processed sensor data may be transmitted using a medium-speed communication channel and model inferences of sensor data may be transmitted using a lightweight communication channel [paragraph 0061]. Since it was known in the art before the effective filing date to process, filter or prioritize data based on available communication channel bandwidths, it would have been obvious to one of ordinary skill in the to apply the Cross teachings to the Ogun system by performing the data filtering based on available communication channel bandwidth in order to ensure that the amount of data transmitted fits the available bandwidth of the communication channels [Cross, paragraph 0062].
Ogun and Cross disclose a system that includes an edge device that receives data from an IoT device, filters the data and transmits filtered data to a remote server. Ogun does not disclose that the system is an industrial automation system that includes a digital twin that interacts with data from industrial automation devices. Like Ogun and Cross, Yoon discloses a system in which device data is collected by an edge device and transmitted to a remote server. Specifically, Yoon discloses that the system is an industrial automation system in which machine tool data is collected by an edge device, processed and transmitted to a digital twin via a remote server [Figure 5 and paragraphs 0068-0074]. Since industrial automation systems that included a digital twins for interacting with data from industrial automation devices were known in the art before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to apply the Ogun and Cross edge device data filtering teachings to the known industrial automation systems in order to more efficiently handle transmission of large amounts of data by scheduling transmission of data based on a determined relevance or priority of the data [Ogun, column 2, lines 21-51].
Regarding claim 2, Ogun further discloses that the data is received and the processed received data is transmitted continuously [collected data is filtered when received and uploaded to the remote server without delay, column 11, lines 38-64].
Regarding claim 3, Ogun further discloses that the data is received, received data is processed, and the processed received data is transmitted in batches [collected data is filtered when received and uploaded to the remote server in batches, column 11, lines 38-49].
Regarding claim 4, Ogun further discloses that the data is received continuously and the processed received data is transmitted in batches [collected data is filtered when received and uploaded to the remote server in batches, column 11, lines 38-49].
Regarding claim 5, Ogun further discloses that the operations comprise adjusting a transmission rate at which the processed received data is transmitted based on a data collection rate at which the received data was collected [collected data is filtered when received and uploaded to the remote server without delay, column 11, lines 38-64. One of ordinary skill in the art would realize that transmission rate of data is required to be set such that it is equal to or higher than received data rate in order to achieve continuous or real-time data providing services].
Regarding claim 6, Yoon further discloses that the operations comprise adjusting a sampling rate for the received data based on a rate of change determined during the processing of the received data [the edge device may set a processing time, processing cycle or processing duration according to operating state of the machine tool when receiving data from the machine tool in real-time, paragraph 0080].
Regarding claim 7, Ogun further discloses that the system comprises an edge device [edge devices, column 5, lines 13-20].
Regarding claim 8, Ogun further discloses that the processing the received data is via a filter running on a compute surface of the edge device [the edge device filtering data received from the IoT devices, column 5, lines 28-54].
Regarding claim 9, Yoon further discloses that the system comprises the industrial automation device [machine tools, paragraphs 0071-0072].
Regarding claim 11, Ogun further discloses receiving inputs defining a new rule; collecting additional data from the industrial automation device; processing the collected additional data, comprising applying the new rule to filter the collected additional data, prioritize the collected additional data, or both; and transmitting the processed collected additional data to the device twin for the industrial automation device [the cloud server may deploy the latest version of trained machine learning models to edge devices for performing data filtering and transmitting functions, column 2, lines 11-20].
Regarding claim 12, Ogun further discloses receiving information about the transmission of the processed collected data to the device twin; applying a machine learning algorithm to the received information; and generating, via the machine learning algorithm, a new rule based on the received information [data received from the edge device is used to train a machine learning model, column 8, lines 1-37].
Regarding claim 13, Ogun further discloses applying the one or more rules to the collected data to classify the collected data into categories [classifying and determining received data relevance scores or levels using a machine learning model, column 11, lines 5-49].
Regarding claim 14, Ogun further discloses that the categories comprise configuration data, industrial automation device state data, application state data, alarm data, or any combination thereof [data indicating abnormal operation state of an automation device, column 11, lines 5-22].
Regarding claim 15, Yoon further discloses that the device twin runs in a cloud environment [cloud Digital Twin service platform 502 in Figure 5].
Regarding claim 16, Yoon further discloses that the device twin runs on a server or computing device disposed on-premises with the industrial automation system [production site Digital Twin 54 in Figure 5].
Regarding claim 18, Ogun further discloses that filtering the received data reduces a number of data points in the received data to reduce a size of the processed received data [compressing a filtered data object for faster transmission, column 5, lines 55-64].
Regarding claim 19, Ogun further discloses that prioritizing the received data comprises determining an order in which the processed received data is transmitted [transmitting data of higher relevance without delay and delaying transmitting data of lower relevance, column 11, line 50 – column 12, line 13].
Regarding claim 20, Ogun further discloses that the one or more rules define when the processed received data is transmitted [transmitting data of higher relevance without delay and delaying transmitting data of lower relevance, column 11, line 50 – column 12, line 13].
Response to Arguments
Applicant’s arguments with respect to the claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/PAUL B YANCHUS III/Primary Examiner, Art Unit 2115 October 2, 2026
1 Ogun and Yoon were previously cited.
