DETAILED ACTION
This Office Action is taken in response to Applicant’s Amendment and Remarks filed on 02/18/2026 regarding Application No. 18/333,430 originally filed on 06/12/2023. Claims 1-10 and 20-21 as filed are currently pending and have been considered as follows:
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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 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.
Claim Objections
Claim 20 is objected to because of the following informalities:
“a state monitoring unit connected with the autonomous driving apparatus configured to be to continuously be input” in Claim 20 should read “a state monitoring unit connected with the autonomous driving apparatus configured to be continuously input”
Appropriate correction is required.
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) 1, 2, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Jang (WO Pub. No. 2023090844) in view of Guo (US Pub. No. 20230400847) in view of Lee (US Pub. No. 20220113715) in further view of Nahas (US Pub. No. 20220246454).
As per Claim 1, Jang discloses detecting anomaly in logistics robot, comprising:
an autonomous driving apparatus configured to transfer an article which is required by semiconductor manufacturing facilities (as per “improve work efficiency, and reduce costs by using overhead hoist transport (OHT) devices in semiconductor factories to transport wafers, etc., or using automated guided vehicles (AGV) or autonomous mobile robots (ARROs) in logistics warehouses or factories to transport products or parts” in ¶3, as per “the logistics robot may include an overhead hoist transport (OHT) used in a semiconductor factory, an automated guided vehicle (AGV) or an autonomous mobile robot (AMR) that performs work while moving along a pre-designated path in a logistics warehouse,” in ¶46)
autonomously driving within a space at which the semiconductor manufacturing facilities are installed; (as per “wherein the logistics robot status detection system collects one or more sensing data for each logistics robot; a step of inputting the one or more sensing data into a pre-trained autoencoder neural network to calculate a reconstruction loss corresponding to each logistics robot; and a step of determining a status of each logistics robot using an anomaly score calculated based on the reconstruction loss” in ¶13, as per “by taking into consideration the sensing data of an acceleration sensor provided in the OHT (10) and the sensing data for the speed and torque of a motor for driving the OHT (10) as input values, thereby enabling detection of an error and the status of the OHT (10) with higher accuracy” in ¶77)
a state monitoring unit connected with the autonomous driving apparatus and configured to continuously be input with the state information of the semiconductor manufacturing facilities and monitor a change value of the state information, (as per “may include a logistics robot status detection system (120) that detects an abnormal state of the logistics robot (110) and a communication network (130) that connects the logistics robot (110) and the logistics robot status detection system (120)” in ¶45, as per “wherein the logistics robot status detection system collects one or more sensing data for each logistics robot;” in ¶13, as per “an anomaly score for the status of the logistics robot (110) is calculated using sensing data measured from the logistics robot (110), and based on this, the status of the logistics robot (110) is determined, thereby enabling maintenance work to be performed” in ¶67)
Jang fails to expressly disclose:
collects a state information of a semiconductor manufacturing facility
the state monitoring unit including an abnormality detection unit and a grade alarm unit,
the abnormality detection unit configured to monitor the change value of the state information from a pre-stored value and
determine a grade according to a change rate based on an intensity of change and a range of change that the change value changes with respect to the pre-stored value, the grade including a normal grade, an inspection grade, and a replacement grade, and
the grade alarm unit configured to generate an alarm information based on the determined grade.
Guo discloses of predictive maintenance for semiconductor manufacturing equipment, comprising:
collects a state information of a semiconductor manufacturing facility (as per “receive offline data that indicates historical operating conditions and historical manufacturing information corresponding to manufacturing equipment that conducts a manufacturing process; calculate predicted equipment health status information associated with the manufacturing equipment by using a trained model that takes the offline data as an input; receive real-time data that indicates current operating conditions and current manufacturing information corresponding to the manufacturing equipment;” in ¶5, as per “the offline data that indicates historical operating conditions and the real-time data that indicates current operating conditions comprises data received from one or more sensors of the manufacturing equipment” in ¶6)
In this way, Guo operates to improve efficiency of semiconductor manufacturing equipment by reducing downtime of equipment due to unforeseen anomalies in equipment (e.g., broken components) and by reducing the need for manual inspection and troubleshooting (¶187). Like Jang, Guo is concerned with semiconductor manufacturing.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the anomaly detection in logistics robot(s) as taught by Jang with the predictive maintenance of Guo to enable another standard means of collecting information of a facility and/or manufacturing equipment (¶5). Such modification also enables the method to calculate adjusted equipment health status information by combining the predicted equipment health status information and the estimated equipment health status information (Abstract).
Jang and Guo fail to expressly disclose:
the state monitoring unit including an abnormality detection unit and a grade alarm unit,
the abnormality detection unit configured to monitor the change value of the state information from a pre-stored value and
determine a grade according to a change rate based on an intensity of change and a range of change that the change value changes with respect to the pre-stored value, the grade including a normal grade, an inspection grade, and a replacement grade, and
the grade alarm unit configured to generate an alarm information based on the determined grade.
Lee discloses of predictive maintenance method for device by means of control output signal, comprising:
the state monitoring unit including an abnormality detection unit and a grade alarm unit, (as per “when the time interval values between the control output signals output from the controller to control the operation of the device in real time do not exceed the warning value or the danger value of the first suspect value, the device is detected as in a normal state. When the time interval values between the control output signals output from the controller exceed the warning value, the device is detected as in a warning state. When the time interval values between the control output signals output from the controller exceed the danger value, the device is detected as in a danger state” in ¶59)
the grade including a normal grade, an inspection grade, and a replacement grade, (as per “when the time value consumed for the operation of the device in real time does not exceed the warning value or the danger value of the second suspect value, the device is detected as in a normal state and when the time value consumed for the operation of the device exceeds the warning value, the device is detected as in a warning state, and when the time value consumed for the operation of the device exceeds the danger value, the device is detected as in a danger state” in ¶69, as per “Here, the warning value indicates a lower level of malfunction dangerousness than the danger value, a warning state of the device is a degree to which attention and caution of the device are required, and a dangerous state of the device is a degree to which repair, inspection, or replacement of the device is required” in ¶70)
the grade alarm unit configured to generate an alarm information based on the determined grade. (as per “and if a condition in which an anomaly in the device is suspected is satisfied by comparing a collected value in accordance with operational information collected in real time with the suspect value, issues an warning to induce a service and replacement of the device at an appropriate time, to prevent huge losses of money due to device malfunction in advance” in ¶8, as per “detection conditions to efficiently search for an anomaly generated in a device and detects the device which satisfies the detection condition as an abnormal state not only to very precisely and effectively detect the anomaly occurring in the device, but also to ensure excellent reliability of the detection result” in ¶9)
In this way, Lee operates to improve predictive maintenance by detecting abnormal states of a device using suspect values derived from normal-state information and pre-malfunction information, and by recognizing graded states including normal, warning, and danger states to induce service, inspection, or replacement of the device (¶8, ¶15-¶16, ¶24). Like Jang and Guo, Lee is concerned with monitoring equipment conditions and detecting abnormal operation before failure.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s) of Jang and Guo with the predictive maintenance method of Lee to enable another standard means of detecting an abnormal state by comparing real-time collected information with suspect values and recognizing graded states that trigger warning information and maintenance actions (¶10, ¶13-¶18, ¶58-¶60, ¶68-¶70). Such modification also allows the system to classify the detected condition into multiple levels of severity including normal, warning, and danger states, where the higher-severity state requires repair, inspection, or replacement (¶15-¶18, ¶69-¶70).
Jang, Guo, and Lee fail to expressly disclose:
the abnormality detection unit configured to monitor the change value of the state information from a pre-stored value and
determine a grade according to a change rate based on an intensity of change and a range of change that the change value changes with respect to the pre-stored value,
Nahas discloses of process abnormality identification using measurement violation analysis, comprising:
the abnormality detection unit configured to monitor the change value of the state information from a pre-stored value (as per “The metrology data includes a current value for a parameter at each of one or more locations on the current sample as well as samples from prior metrology process steps. The method includes obtaining a reference rate of change of the parameter value of the parameter for each of the one or more locations. The method further includes determining a current rate of parameter change of the parameter for each of the one or more locations. The current rate of change is associated with the current sample. The method further includes comparing the current rate of change of the parameter value to the reference rate of change of the parameter value and identifying an instance of abnormality of the fabrication process based on the comparison” in ¶4, as per “The rate of change tool 135 compares a reference rate of change of the parameter value (e.g., a historic rate of change of the parameter value) received from the SPC system 116 or calculated locally by the equipment engineering system 130 to a current rate of change of the parameter value of a current sample. In some embodiments, the reference rate of change of the parameter value is determined using one or more historical rates of change of the parameter value from historical metrology data for the operation on one or more previous samples in the fabrication process” in ¶32)
determine a grade according to a change rate based on an intensity of change and a range of change that the change value changes with respect to the pre-stored value, (as per “The current rate of change of the parameter value is compared to the reference rate of change of the parameter value. In one embodiment, the comparison is performed by applying a statistical distribution to the reference rate of change of the parameter value to identify a variance between the current rate of change of the parameter value and the reference rate of change of the parameter value. For example, a mean and a standard deviation is calculated from the historical rates of parameter values change and are compared against the current rate of change of the parameter value to identify how many standard deviations the current rate of change of the parameter value is from the mean of the one or more historical rates of change of a parameter value” in ¶51, as per “the violating locations on a sample may be indicated by a scoring system or by placing each location into a tier or degree of violation rather than a binary indication of a violating measurement. For example, a location may be classified into tiers such as “passing”, “level one violation”, “level two violation”, “level three violation”, etc. The violation tiers may correspond to the variance of the current rate of change of the parameter value from the reference rate of change of the parameter value, at each location. For example, a location that is less than one standard deviation off may be labeled as “passing,” a location that is between one and two standard deviations may be labeled as a “level-one violation,” a location that is between two and three standard deviations may be labeled as a “level-two violation,” and so forth. Each violation may be assigned a weight or a score than may be used to further establish a sample pattern” in ¶54)
In this way, Nahas operates to identify abnormalities in a fabrication process by comparing a current rate of change of a parameter value to a reference rate of change derived from historical data, and by using threshold ranges and violation tiers to determine the degree of abnormality and initiate corrective action (¶4, ¶15, ¶31-¶36, ¶51-¶54). Like Jang, Guo, and Lee, Nahas is concerned with monitoring manufacturing conditions and detecting abnormal operation based on measured data.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s) of Jang, Guo, and Lee with the rate-of-change abnormality analysis of Nahas to enable another standard means of determining abnormality according to a current change rate relative to a reference change rate, including threshold-based ranges and tiered levels of violation (¶4, ¶15, ¶31-¶34, ¶51-¶54, ¶73-¶77). Such modification also allows the system to use historical baseline information and current measured changes to determine the severity of the abnormal condition in a more structured and quantitative manner (¶27, ¶31-¶34, ¶51-¶54).
As per Claim 2, the combination of Jang, Guo, Lee, and Nahas teaches or suggests all limitations of Claim 1. Jang further discloses wherein the autonomous driving apparatus consists of at least one among an OHT and a mobile robot. (as per “by using overhead hoist transport (OHT) devices in semiconductor factories to transport wafers, etc., or using automated guided vehicles (AGV) or autonomous mobile robots (ARROs) in logistics warehouses or factories to transport products or parts” in ¶3, as per “a logistics automation system (100) according to one embodiment of the present invention may be configured to include a plurality of logistics robots (110, 110a, 110n) such as OHTs (10) and a server, and may include a logistics robot status detection system (120) that detects an abnormal state of the logistics robot” in ¶45)
As per Claim 21, the combination of Jang, Guo, Lee, and Nahas teaches or suggests all limitations of Claim 1. Jang and Guo fail to expressly disclose wherein the abnormality detection unit is configured to determine the grade as the normal grade based on the change rate being same as or below a standard value, determine the grade as the inspection grade based on the change rate being greater than the standard value and not exceeding a critical value which is same as or above the standard value, and determine the grade as the replacement grade based on the change rate exceeding the critical value.
See Claim 1 for teachings of Lee. Lee further discloses wherein the abnormality detection unit is configured to determine the grade as the normal grade based on the change rate being same as or below a standard value, (as per “between the control output signals output from the controller to control the operation of the device in real time do not exceed the warning value or the danger value of the first suspect value, the device is detected as in a normal state” in ¶59) determine the grade as the inspection grade based on the change rate being greater than the standard value and not exceeding a critical value which is same as or above the standard value, (as per “When the time interval values between the control output signals output from the controller exceed the warning value, the device is detected as in a warning state” in ¶59, as per “the warning value indicates a lower level of malfunction dangerousness than the danger value, a warning state of the device is a degree to which attention and caution of the device are required, and a dangerous state of the device is a degree to which repair, inspection, or replacement of the device is required” in ¶60) and determine the grade as the replacement grade based on the change rate exceeding the critical value. (as per “When the time interval values between the control output signals output from the controller exceed the danger value, the device is detected as in a danger state.” in ¶59, as per “the warning value indicates a lower level of malfunction dangerousness than the danger value, a warning state of the device is a degree to which attention and caution of the device are required, and a dangerous state of the device is a degree to which repair, inspection, or replacement of the device is required” in ¶60)
In this way, Lee operates to improve predictive maintenance by detecting abnormal states of a device using suspect values derived from normal-state information and pre-malfunction information, and by recognizing graded states including normal, warning, and danger states to induce service, inspection, or replacement of the device (¶8, ¶15-¶16, ¶24). Like Jang, Guo, and Nahas, Lee is concerned with monitoring equipment conditions and detecting abnormal operation before failure.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s) of Jang, Guo, and Nahas with the predictive maintenance method of Lee to enable another standard means of detecting an abnormal state by comparing real-time collected information with suspect values and recognizing graded states that trigger warning information and maintenance actions (¶10, ¶13-¶18, ¶58-¶60, ¶68-¶70). Such modification also allows the system to classify the detected condition into multiple levels of severity including normal, warning, and danger states, where the higher-severity state requires repair, inspection, or replacement (¶15-¶18, ¶69-¶70).
Claim(s) 3 is rejected under 35 U.S.C. 103 as being unpatentable over Jang (WO Pub. No. 2023090844) in view of Guo (US Pub. No. 20230400847) in view of Lee (US Pub. No. 20220113715) in view of Nahas (US Pub. No. 20220246454) in further view of Liu (US Pub. No. 20210247764).
As per Claim 3, the combination of Jang, Guo, Lee, and Nahas teaches or suggests all limitations of Claim 1. Jang further discloses of an autonomous driving apparatus. (as per “the logistics robot may include an overhead hoist transport (OHT) used in a semiconductor factory, an automated guided vehicle (AGV) or an autonomous mobile robot” in ¶46, as per Fig. 2)
Jang, Guo, Lee, and Nahas fail to expressly disclose a proximity sensor configured to generate a facility scan information with respect to a shape of the semiconductor manufacturing facility into the state information.
Liu discloses of multi-sensor environmental mapping, comprising a proximity sensor (as per “the plurality of sensors can comprise a global positioning system (GPS) sensor, a vision sensor, or a proximity sensor. The proximity sensor can comprise at least one of the following: a lidar sensor, an ultrasonic sensor, or a time-of-flight camera sensor” in ¶8) configured to generate a facility scan information with respect to a shape of the semiconductor manufacturing facility into the state information. (as per “he second sensing data including depth information for the environment; and generating an environmental map including depth information for the environment using the first and second sensing data” in ¶38, as per “FIG. 1B illustrates a UAV 152 operating in an indoor environment 150, in accordance with embodiments. The indoor environment 150 is within the interior of a building 154 having a floor 156, one or more walls 158, and/or a ceiling or roof 160. Exemplary buildings include residential, commercial, or industrial buildings such as houses, apartments, offices, manufacturing facilities, storage facilities, and so on” in ¶71, as per “the map can provide information indicating the geometry (e.g., length, width, height, thickness, shape, surface area, volume), spatial disposition (e.g., position, orientation), and type of environmental objects such as obstacles, structures, landmarks, or features” in ¶89)
In this way, Liu operates to improve the accuracy of environmental mapping even in diverse environments and operating conditions, thereby enhancing the robustness and flexibility of functionalities such as navigation and obstacle avoidance. (¶66). Like Jang, Guo, Lee, and Nahas, Liu is concerned with robotics.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s) of Jang, Guo, Lee, and Nahas with the multi-sensor environmental mapping of Liu to enable another standard means of generating an environmental map reflecting the shape/depth of an environment, notably an indoor environment (¶71).
Claim(s) 4-7 are rejected under 35 U.S.C. 103 as being unpatentable over Jang (WO Pub. No. 2023090844) in view of Guo (US Pub. No. 20230400847) in view of Lee (US Pub. No. 20220113715) in view of Nahas (US Pub. No. 20220246454) in further view of Kyungsoo (KR Pat. No. 102096175).
As per Claim 4, the combination of Jang, Guo, Lee, and Nahas teaches or suggests all limitations of Claim 1. Jang further discloses of an autonomous driving apparatus. (as per “the logistics robot may include an overhead hoist transport (OHT) used in a semiconductor factory, an automated guided vehicle (AGV) or an autonomous mobile robot” in ¶46, as per Fig. 2).
Jang, Guo, Lee, and Nahas fail to expressly disclose a camera sensor configured to generate a facility image information of an image of the semiconductor manufacturing facility into the state information.
Kyungsoo discloses of a ceiling rail type IoT based surveillance robot device, comprising a camera sensor configured to generate a facility image information of an image of the semiconductor manufacturing facility into the state information. (as per “Additionally, in the case of cameras, it can be implemented by configuring a 360-degree camera that has multiple lenses to capture the facility in all directions and create an all-round video” in ¶34, as per “The above acquisition unit (1000) acquires information from temperature, humidity, vibration, and sound (S110) sensors, as well as real-world image, thermal image, and infrared (S210) image information and location (S370) information of the mobile robot from a camera mounted on the mobile robot.)” in ¶21)
In this way, Kyungsoo operates to enable realtime surveillance of facilities through IoT technology and image analysis using a robot (Abstract). Like Jang, Guo, Lee, and Nahas, Kyungsoo is concerned with robotics.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s) of Jang, Guo, Lee, and Nahas with the ceiling rail type IoT based surveillance robot of Kyungsoo to enable another standard means of using a camera sensor on the robot to generate state information (¶34).
As per Claim 5, the combination of Jang, Guo, Lee, and Nahas teaches or suggests all limitations of Claim 1. Jang further discloses of an autonomous driving apparatus. (as per “the logistics robot may include an overhead hoist transport (OHT) used in a semiconductor factory, an automated guided vehicle (AGV) or an autonomous mobile robot” in ¶46, as per Fig. 2)
Jang, Guo, Lee, and Nahas fail to expressly disclose a sound detection sensor configured to generate a facility sound information of a sound of the semiconductor manufacturing facility into the state information.
Kyungsoo discloses of a ceiling rail type IoT based surveillance robot device, comprising a sound detection sensor configured to generate a facility sound information of a sound of the semiconductor manufacturing facility into the state information. (as per “The present invention is characterized by comprising the steps of: measuring temperature, humidity, vibration, and sound data values from sensor nodes attached to each section of a facility;” in ¶6, as per “The above acquisition unit (1000) acquires information from temperature, humidity, vibration, and sound (S110) sensors, as well as real-world image, thermal image, and infrared (S210) image information and location (S370) information of the mobile robot from a camera mounted on the mobile robot” in ¶21)
In this way, Kyungsoo operates to enable realtime surveillance of facilities through IoT technology and image analysis using a robot (Abstract). Like Jang, Guo, Lee, and Nahas, Kyungsoo is concerned with robotics.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s) of Jang, Guo, Lee, and Nahas with the ceiling rail type IoT based surveillance robot of Kyungsoo to enable another standard means of using a sound detection sensor on the robot to generate state information (¶34).
As per Claim 6, the combination of Jang, Guo, Lee, and Nahas teaches or suggests all limitations of Claim 1. Jang further discloses of an autonomous driving apparatus. (as per “the logistics robot may include an overhead hoist transport (OHT) used in a semiconductor factory, an automated guided vehicle (AGV) or an autonomous mobile robot” in ¶46, as per Fig. 2)
Jang, Guo, Lee, and Nahas fail to expressly disclose a vibration detection sensor which generates a facility vibration information of the semiconductor manufacturing facility into the state information.
Kyungsoo discloses of a ceiling rail type IoT based surveillance robot device, comprising a vibration detection sensor which generates a facility vibration information of the semiconductor manufacturing facility into the state information. (as per “The present invention is characterized by comprising the steps of: measuring temperature, humidity, vibration, and sound data values from sensor nodes attached to each section of a facility;” in ¶6, as per “The above acquisition unit (1000) acquires information from temperature, humidity, vibration, and sound (S110) sensors, as well as real-world image, thermal image, and infrared (S210) image information and location (S370) information of the mobile robot from a camera mounted on the mobile robot” in ¶21)
In this way, Kyungsoo operates to enable realtime surveillance of facilities through IoT technology and image analysis using a robot (Abstract). Like Jang, Guo, Lee, and Nahas, Kyungsoo is concerned with robotics.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s) of Jang, Guo, Lee, and Nahas with the ceiling rail type IoT based surveillance robot of Kyungsoo to enable another standard means of using a vibration sensor on the robot to generate state information (¶34).
As per Claim 7, the combination of Jang, Guo, Lee, and Nahas teaches or suggests all limitations of Claim 1. Jang further discloses of an autonomous driving apparatus. (as per “the logistics robot may include an overhead hoist transport (OHT) used in a semiconductor factory, an automated guided vehicle (AGV) or an autonomous mobile robot” in ¶46, as per Fig. 2)
Jang, Guo, Lee, and Nahas fail to expressly disclose a temperature detection sensor configured to generate a facility temperature information of a temperature of the semiconductor manufacturing facility into the state information.
Kyungsoo discloses of a ceiling rail type IoT based surveillance robot device, comprising a temperature detection sensor configured to generate a facility temperature information of a temperature of the semiconductor manufacturing facility into the state information. (as per “The present invention is characterized by comprising the steps of: measuring temperature, humidity, vibration, and sound data values from sensor nodes attached to each section of a facility;” in ¶6, as per “and abnormality diagnosis, as well as real-time temperature confirmation” in ¶5)
In this way, Kyungsoo operates to enable realtime surveillance of facilities through IoT technology and image analysis using a robot (Abstract). Like Jang, Guo, Lee, and Nahas, Kyungsoo is concerned with robotics.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s) of Jang, Guo, Lee, and Nahas with the ceiling rail type IoT based surveillance robot of Kyungsoo to enable another standard means of using a temperature sensor on the robot to generate state information (¶34).
Claim(s) 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Jang (WO Pub. No. 2023090844) in view of Guo (US Pub. No. 20230400847) in view of Lee (US Pub. No. 20220113715) in view of Nahas (US Pub. No. 20220246454) in further view of Tsuji (US Pub. No. 20240273460).
As per Claim 8, the combination of Jang, Guo, Lee, and Nahas teaches or suggests all limitations of Claim 1. Jang and Guo fail to expressly disclose wherein the state monitoring unit further includes an inspection information storage unit configured to store a facility name of the semiconductor manufacturing facility and an inspection time every set cycle, together with the state information.
Tsuji discloses of an information processing method, wherein the state monitoring unit further includes an inspection information storage unit (as per “FIG. 7 is a diagram illustrating an example of an inspection apparatus database (DB) 701 according to the example embodiment” in ¶44) configured to store a facility name of the semiconductor manufacturing facility and an inspection time every set cycle, together with the state information. (as per “an inspection state, a remaining battery capacity, a current location (position), a delivery request ID, inspection apparatus information, and an inspection schedule are recorded in the inspection apparatus DB 701 in association with the inspection apparatus ID. The inspection state is information indicating a state related to inspection performed by the inspection apparatus 40. The inspection state may include, for example, “available”, “under inspection”, and “inspection completed”” in ¶49, as per “the facility DB 901 records a facility name, a facility location (position or address), owned equipment, and an inspection history in association with the facility ID. Information regarding the facility ID, the facility name, the facility location, and the owned equipment may be registered in advance by, for example, the operator of the like of the information processing apparatus 10” in ¶64, as per “the information processing apparatus 10 may determine additional inspection work in the facility to be presently inspected based on information measured by the sensor 41 of the inspection apparatus 40. Here, for example, in a case where an abnormality such as bird nesting, oil leakage, or breakage of an insulator is detected by the sensor 41, the information processing apparatus 10 may transmit an instruction for inspection work such as capturing of an image of an abnormal portion and re-measurement with higher accuracy by the sensor 41 to the inspection apparatus 40” in ¶60)
In this way, Tsuji operates to perform facility inspection (Abstract). Like Jang and Guo, Tsuji is concerned with robotics.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the anomaly detection in logistics robot(s) as taught by Jang and the predictive maintenance of Guo with the information processing method of Tsuji to enable another standard means of storing a facility name & inspection time with the state information of a facility (¶49, ¶60-¶64).
As per Claim 9, the combination of Jang, Guo, Lee, Nahas, and Tsuji teaches or suggests all limitations of Claim 8. Jang, Guo, Lee, and Nahas fail to expressly disclose wherein the pre-stored value is a previously stored state information stored in the inspection information storage unit.
See Claim 8 for teachings of Tsuji. Tsuji further discloses wherein the pre-stored value is a previously stored state information stored in the inspection information storage unit. (as per “an inspection state, a remaining battery capacity, a current location (position), a delivery request ID, inspection apparatus information, and an inspection schedule are recorded in the inspection apparatus DB 701 in association with the inspection apparatus ID. The inspection state is information indicating a state related to inspection performed by the inspection apparatus 40. The inspection state may include, for example, “available”, “under inspection”, and “inspection completed”” in ¶49, as per “Information regarding the facility ID, the facility name, the facility location, and the owned equipment may be registered in advance by, for example, the operator of the like of the information processing apparatus 10 or the like. The inspection history may be recorded by the information processing apparatus 10 when inspection performed by the inspection apparatus 40 is completed. The inspection history may include, for example, information regarding the inspection apparatus ID of the inspection apparatus 40 that has performed the inspection, an inspection date and time, and an inspection result. The inspection result may include information indicating the presence or absence of an abnormality and a content of an abnormality” in ¶64)
In this way, Tsuji operates to perform facility inspection (Abstract). Like Jang and Guo, Tsuji is concerned with robotics.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the anomaly detection in logistics robot(s) as taught by Jang and the predictive maintenance of Guo with the information processing method of Tsuji to enable another standard means of storing a facility name & inspection time with the state information of a facility (¶49, ¶60-¶64).
As per Claim 10, the combination of Jang, Guo, Lee, Nahas, and Tsuji teaches or suggests all limitations of Claim 9. Jang fails to expressly disclose wherein the state monitoring unit further includes a facility life prediction unit configured to monitor how much the change rate of the intensity or the range of the state information increases during a predetermined period, in communication with the abnormality detection unit.
See Claim 9 for teachings of Guo. Guo further discloses wherein the state monitoring unit further includes a facility life prediction unit in communication with the abnormality detection unit. (as per “of equipment health status scores or metrics for components of a system or sub-system can include a Remaining Useful Life (RUL) of the component. For example, in some embodiments, a predictive maintenance system can determine that the component will need to be replaced at a particular time in the future (e.g., in ten days, in twenty days, etc.).” in ¶65, as per “the predictive maintenance system can generate a predicted equipment health status information that indicates a health status of the equipment based on previously measured characteristics of the equipment (referred to herein as offline information) assuming a typical rate of deterioration of the equipment (e.g., due to wear and tear). Continuing further with this particular example, in some embodiments, the predictive maintenance system can generate an estimated equipment health status information that indicates estimates of a current health status of the equipment based on real-time data (e.g., real-time data collected from sensors associated with the equipment, real-time spectroscopy information, real-time manufacturing conditions of the equipment, and/or any other suitable real-time data)” in ¶69, as per “adjusted equipment health status information 164 can include any suitable scores or metrics, such as RUL prediction 276 that predicts an expected RUL for an individual component (e.g., a pedestal, an edge ring, a valve, etc.)” in ¶136)
In this way, Guo operates to improve efficiency of semiconductor manufacturing equipment by reducing downtime of equipment due to unforeseen anomalies in equipment (e.g., broken components) and by reducing the need for manual inspection and troubleshooting (¶187). Like Jang, Lee, Nahas, and Tsuji, Guo is concerned with manufacturing and/or robotics.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the systems(s) of Jang, Lee, Nahas, and Tsuji with the predictive maintenance of Guo to enable another standard means of collecting information of a facility and/or manufacturing equipment (¶5). Such modification also enables the method to calculate adjusted equipment health status information by combining the predicted equipment health status information and the estimated equipment health status information (Abstract).
Jang, Guo, and Lee fail to expressly disclose:
monitor how much the change rate of the intensity or the range of the state information increases during a predetermined period,
See Claim 9 for teachings of Nahas. Nahas further discloses:
monitor how much the change rate of the intensity or the range of the state information increases during a predetermined period, (as per “The rate of change tool 135 calculates a current rate of change of a current sample being measured by metrology system 110. The current rate of change may include a determination of a current parameter value rate of change. For example, a parameter is measured by metrology system 110 and a set of parameter values are obtained at various locations across a sample. These parameter values are compared against historical parameter values to determine how much the current parameter values have changed. In some embodiments, the rate of change of the parameter value is calculated by comparing the current parameter values to a sample that was fabricated immediately previous to the current sample. The difference between the current parameter values and parameter values taken from the sample fabricated immediately previous to the current sample may yield a rate of change of the parameter value” in ¶31, as per “comparison is performed by applying a statistical distribution to the reference rate of change of the parameter value to identify a variance between the current rate of change of the parameter value and the reference rate of change of the parameter value. For example, a mean and a standard deviation is calculated from the historical rates of parameter values change and are compared against the current rate of change of the parameter value to identify how many standard deviations the current rate of change of the parameter value is from the mean of the one or more historical rates of change of a parameter value.” in ¶51)
In this way, Nahas operates to identify abnormalities in a fabrication process by comparing a current rate of change of a parameter value to a reference rate of change derived from historical data, and by using threshold ranges and violation tiers to determine the degree of abnormality and initiate corrective action (¶4, ¶15, ¶31-¶36, ¶51-¶54). Like Jang, Guo, and Lee, Nahas is concerned with monitoring manufacturing conditions and detecting abnormal operation based on measured data.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s) of Jang, Guo, and Lee with the rate-of-change abnormality analysis of Nahas to enable another standard means of determining abnormality according to a current change rate relative to a reference change rate, including threshold-based ranges and tiered levels of violation (¶4, ¶15, ¶31-¶34, ¶51-¶54, ¶73-¶77). Such modification also allows the system to use historical baseline information and current measured changes to determine the severity of the abnormal condition in a more structured and quantitative manner (¶27, ¶31-¶34, ¶51-¶54).
Claim(s) 20 is rejected under 35 U.S.C. 103 as being unpatentable over Jang (WO Pub. No. 2023090844) in view of Guo (US Pub. No. 20230400847) in view of Liu (US Pub. No. 20210247764) in view of Kyungsoo (KR Pat. No. 102096175) in view of Tsuji (US Pub. No. 20240273460) in view of Lee (US Pub. No. 20220113715) in further view of Nahas (US Pub. No. 20220246454).
As per Claim 20, Jang discloses detecting anomaly in logistics robot, comprising:
an autonomous driving apparatus configured to transfer an article to semiconductor manufacturing facilities by autonomously driving within a space at which the semiconductor manufacturing facilities are installed, (as per “improve work efficiency, and reduce costs by using overhead hoist transport (OHT) devices in semiconductor factories to transport wafers, etc., or using automated guided vehicles (AGV) or autonomous mobile robots (ARROs) in logistics warehouses or factories to transport products or parts” in ¶3, as per “the logistics robot may include an overhead hoist transport (OHT) used in a semiconductor factory, an automated guided vehicle (AGV) or an autonomous mobile robot (AMR) that performs work while moving along a pre-designated path in a logistics warehouse,” in ¶46)
the autonomous driving apparatus including at least one among an OHT and a mobile robot; (as per “by using overhead hoist transport (OHT) devices in semiconductor factories to transport wafers, etc., or using automated guided vehicles (AGV) or autonomous mobile robots (ARROs) in logistics warehouses or factories to transport products or parts” in ¶3, as per “a logistics automation system (100) according to one embodiment of the present invention may be configured to include a plurality of logistics robots (110, 110a, 110n) such as OHTs (10) and a server, and may include a logistics robot status detection system (120) that detects an abnormal state of the logistics robot” in ¶45)
a state monitoring unit connected with the autonomous driving apparatus configured to be continuously input with the state information of the semiconductor manufacturing facilities and for monitoring a change value of the state information, (as per “may include a logistics robot status detection system (120) that detects an abnormal state of the logistics robot (110) and a communication network (130) that connects the logistics robot (110) and the logistics robot status detection system (120)” in ¶45, as per “wherein the logistics robot status detection system collects one or more sensing data for each logistics robot;” in ¶13, as per “an anomaly score for the status of the logistics robot (110) is calculated using sensing data measured from the logistics robot (110), and based on this, the status of the logistics robot (110) is determined, thereby enabling maintenance work to be performed” in ¶67)
Jang fails to expressly disclose:
collects a state information of a semiconductor manufacturing facility, and
a facility life prediction unit in communication with the abnormality detection unit.
wherein the transfer robot system further comprises a proximity sensor configured to generate a facility scan information a shape of the semiconductor manufacturing facility into the state information;
a camera sensor configured to generate a facility image information of an image of the semiconductor manufacturing facility into the state information;
a sound detection sensor configured to generate a facility sound information of a sound of the semiconductor manufacturing facility into the state information;
a vibration detection sensor configured to generate a facility vibration information of the semiconductor manufacturing facility into the state information; and
a temperature detection sensor configured to generate a facility temperature information of a temperature of the semiconductor manufacturing facility into the state information, and
wherein the transfer robot system further comprises an inspection information storage unit configured to store a facility name of the semiconductor manufacturing facility and an inspection time every set cycle, together with the state information;
an abnormality detection unit configured to monitor the change value of the state information from a pre-stored value and determine the grade according to a change rate based on an intensity of change and a range of change that the change values changes with respect to the pre-stored value, the grade including a normal grade, an inspection grade, and a replacement grade;
a grade alarm unit configured to generate an alarm information based on the determined grade;
a facility life prediction unit configured to monitor how much the change rate of the state information increases during a predetermined period,
Guo discloses of predictive maintenance for semiconductor manufacturing equipment, comprising:
collects a state information of a semiconductor manufacturing facility, (as per “receive offline data that indicates historical operating conditions and historical manufacturing information corresponding to manufacturing equipment that conducts a manufacturing process; calculate predicted equipment health status information associated with the manufacturing equipment by using a trained model that takes the offline data as an input; receive real-time data that indicates current operating conditions and current manufacturing information corresponding to the manufacturing equipment;” in ¶5, as per “the offline data that indicates historical operating conditions and the real-time data that indicates current operating conditions comprises data received from one or more sensors of the manufacturing equipment” in ¶6)
a facility life prediction unit in communication with the abnormality detection unit. (as per “of equipment health status scores or metrics for components of a system or sub-system can include a Remaining Useful Life (RUL) of the component. For example, in some embodiments, a predictive maintenance system can determine that the component will need to be replaced at a particular time in the future (e.g., in ten days, in twenty days, etc.).” in ¶65, as per “the predictive maintenance system can generate a predicted equipment health status information that indicates a health status of the equipment based on previously measured characteristics of the equipment (referred to herein as offline information) assuming a typical rate of deterioration of the equipment (e.g., due to wear and tear). Continuing further with this particular example, in some embodiments, the predictive maintenance system can generate an estimated equipment health status information that indicates estimates of a current health status of the equipment based on real-time data (e.g., real-time data collected from sensors associated with the equipment, real-time spectroscopy information, real-time manufacturing conditions of the equipment, and/or any other suitable real-time data)” in ¶69, as per “adjusted equipment health status information 164 can include any suitable scores or metrics, such as RUL prediction 276 that predicts an expected RUL for an individual component (e.g., a pedestal, an edge ring, a valve, etc.)” in ¶136)
In this way, Guo operates to improve efficiency of semiconductor manufacturing equipment by reducing downtime of equipment due to unforeseen anomalies in equipment (e.g., broken components) and by reducing the need for manual inspection and troubleshooting (¶187). Like Jang, Guo is concerned with semiconductor manufacturing.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the anomaly detection in logistics robot(s) as taught by Jang with the predictive maintenance of Guo to enable another standard means of collecting information of a facility and/or manufacturing equipment (¶5). Such modification also enables the method to calculate adjusted equipment health status information by combining the predicted equipment health status information and the estimated equipment health status information (Abstract).
Jang and Guo fail to expressly disclose:
wherein the transfer robot system further comprises a proximity sensor configured to generate a facility scan information a shape of the semiconductor manufacturing facility into the state information;
a camera sensor configured to generate a facility image information of an image of the semiconductor manufacturing facility into the state information;
a sound detection sensor configured to generate a facility sound information of a sound of the semiconductor manufacturing facility into the state information;
a vibration detection sensor configured to generate a facility vibration information of the semiconductor manufacturing facility into the state information; and
a temperature detection sensor configured to generate a facility temperature information of a temperature of the semiconductor manufacturing facility into the state information, and
wherein the transfer robot system further comprises an inspection information storage unit configured to store a facility name of the semiconductor manufacturing facility and an inspection time every set cycle, together with the state information;
an abnormality detection unit configured to monitor the change value of the state information from a pre-stored value and determine the grade according to a change rate based on an intensity of change and a range of change that the change values changes with respect to the pre-stored value, the grade including a normal grade, an inspection grade, and a replacement grade;
a grade alarm unit configured to generate an alarm information based on the determined grade;
a facility life prediction unit configured to monitor how much the change rate of the state information increases during a predetermined period,
Liu discloses of multi-sensor environmental mapping, wherein the transfer robot system further comprises a proximity sensor (as per “the plurality of sensors can comprise a global positioning system (GPS) sensor, a vision sensor, or a proximity sensor. The proximity sensor can comprise at least one of the following: a lidar sensor, an ultrasonic sensor, or a time-of-flight camera sensor” in ¶8) configured to generate a facility scan information a shape of the semiconductor manufacturing facility into the state information; (as per “he second sensing data including depth information for the environment; and generating an environmental map including depth information for the environment using the first and second sensing data” in ¶38, as per “FIG. 1B illustrates a UAV 152 operating in an indoor environment 150, in accordance with embodiments. The indoor environment 150 is within the interior of a building 154 having a floor 156, one or more walls 158, and/or a ceiling or roof 160. Exemplary buildings include residential, commercial, or industrial buildings such as houses, apartments, offices, manufacturing facilities, storage facilities, and so on” in ¶71, as per “the map can provide information indicating the geometry (e.g., length, width, height, thickness, shape, surface area, volume), spatial disposition (e.g., position, orientation), and type of environmental objects such as obstacles, structures, landmarks, or features” in ¶89)
In this way, Liu operates to improve the accuracy of environmental mapping even in diverse environments and operating conditions, thereby enhancing the robustness and flexibility of functionalities such as navigation and obstacle avoidance. (¶66). Like Jang and Guo, Liu is concerned with robotics.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the anomaly detection in logistics robot(s) as taught by Jang and the predictive maintenance of Guo with the multi-sensor environmental mapping of Liu to enable another standard means of generating an environmental map reflecting the shape/depth of an environment, notably an indoor environment (¶71).
Jang, Guo, and Liu fail to expressly disclose:
a camera sensor configured to generate a facility image information of an image of the semiconductor manufacturing facility into the state information;
a sound detection sensor configured to generate a facility sound information of a sound of the semiconductor manufacturing facility into the state information;
a vibration detection sensor configured to generate a facility vibration information of the semiconductor manufacturing facility into the state information; and
a temperature detection sensor configured to generate a facility temperature information of a temperature of the semiconductor manufacturing facility into the state information, and
wherein the transfer robot system further comprises an inspection information storage unit configured to store a facility name of the semiconductor manufacturing facility and an inspection time every set cycle, together with the state information;
an abnormality detection unit configured to monitor the change value of the state information from a pre-stored value and determine the grade according to a change rate based on an intensity of change and a range of change that the change values changes with respect to the pre-stored value, the grade including a normal grade, an inspection grade, and a replacement grade;
a grade alarm unit configured to generate an alarm information based on the determined grade;
a facility life prediction unit configured to monitor how much the change rate of the state information increases during a predetermined period,
Kyungsoo discloses of a ceiling rail type IoT based surveillance robot device, comprising:
a camera sensor configured to generate a facility image information of an image of the semiconductor manufacturing facility into the state information; (as per “Additionally, in the case of cameras, it can be implemented by configuring a 360-degree camera that has multiple lenses to capture the facility in all directions and create an all-round video” in ¶34, as per “The above acquisition unit (1000) acquires information from temperature, humidity, vibration, and sound (S110) sensors, as well as real-world image, thermal image, and infrared (S210) image information and location (S370) information of the mobile robot from a camera mounted on the mobile robot.)” in ¶21)
a sound detection sensor configured to generate a facility sound information of a sound of the semiconductor manufacturing facility into the state information; (as per “The present invention is characterized by comprising the steps of: measuring temperature, humidity, vibration, and sound data values from sensor nodes attached to each section of a facility;” in ¶6, as per “The above acquisition unit (1000) acquires information from temperature, humidity, vibration, and sound (S110) sensors, as well as real-world image, thermal image, and infrared (S210) image information and location (S370) information of the mobile robot from a camera mounted on the mobile robot” in ¶21)
a vibration detection sensor configured to generate a facility vibration information of the semiconductor manufacturing facility into the state information; and (as per “The present invention is characterized by comprising the steps of: measuring temperature, humidity, vibration, and sound data values from sensor nodes attached to each section of a facility;” in ¶6, as per “The above acquisition unit (1000) acquires information from temperature, humidity, vibration, and sound (S110) sensors, as well as real-world image, thermal image, and infrared (S210) image information and location (S370) information of the mobile robot from a camera mounted on the mobile robot” in ¶21)
a temperature detection sensor configured to generate a facility temperature information of a temperature of the semiconductor manufacturing facility into the state information, (as per “The present invention is characterized by comprising the steps of: measuring temperature, humidity, vibration, and sound data values from sensor nodes attached to each section of a facility;” in ¶6, as per “and abnormality diagnosis, as well as real-time temperature confirmation” in ¶5)
In this way, Kyungsoo operates to enable realtime surveillance of facilities through IoT technology and image analysis using a robot (Abstract). Like Jang, Guo, and Liu, Kyungsoo is concerned with robotics/monitoring systems.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the anomaly detection in logistics robot(s) as taught by Jang, the predictive maintenance of Guo, and the multi-sensor environmental mapping of Liu with the ceiling rail type IoT based surveillance robot of Kyungsoo to enable another standard means of using a camera/sound/vibration/temperature sensor on the robot to generate state information (¶34).
Jang, Guo, Liu, and Kyungsoo fail to expressly disclose:
wherein the transfer robot system further comprises an inspection information storage unit configured to store a facility name of the semiconductor manufacturing facility and an inspection time every set cycle, together with the state information;
an abnormality detection unit configured to monitor the change value of the state information from a pre-stored value and determine the grade according to a change rate based on an intensity of change and a range of change that the change values changes with respect to the pre-stored value, the grade including a normal grade, an inspection grade, and a replacement grade;
a grade alarm unit configured to generate an alarm information based on the determined grade;
a facility life prediction unit configured to monitor how much the change rate of the state information increases during a predetermined period,
Tsuji discloses of an information processing method, wherein the transfer robot system further comprises an inspection information storage unit (as per “FIG. 7 is a diagram illustrating an example of an inspection apparatus database (DB) 701 according to the example embodiment” in ¶44) configured to store a facility name of the semiconductor manufacturing facility and an inspection time every set cycle, together with the state information; (as per “an inspection state, a remaining battery capacity, a current location (position), a delivery request ID, inspection apparatus information, and an inspection schedule are recorded in the inspection apparatus DB 701 in association with the inspection apparatus ID. The inspection state is information indicating a state related to inspection performed by the inspection apparatus 40. The inspection state may include, for example, “available”, “under inspection”, and “inspection completed”” in ¶49, as per “the facility DB 901 records a facility name, a facility location (position or address), owned equipment, and an inspection history in association with the facility ID. Information regarding the facility ID, the facility name, the facility location, and the owned equipment may be registered in advance by, for example, the operator of the like of the information processing apparatus 10” in ¶64, as per “the information processing apparatus 10 may determine additional inspection work in the facility to be presently inspected based on information measured by the sensor 41 of the inspection apparatus 40. Here, for example, in a case where an abnormality such as bird nesting, oil leakage, or breakage of an insulator is detected by the sensor 41, the information processing apparatus 10 may transmit an instruction for inspection work such as capturing of an image of an abnormal portion and re-measurement with higher accuracy by the sensor 41 to the inspection apparatus 40” in ¶60)
In this way, Tsuji operates to perform facility inspection (Abstract). Like Jang, Guo, Liu, and Kyungsoo, Tsuji is concerned with robotics/monitoring systems.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the anomaly detection in logistics robot(s) as taught by Jang, the predictive maintenance of Guo, the multi-sensor environmental mapping of Liu, and the ceiling rail type IoT based surveillance robot of Kyungsoo with the information processing method of Tsuji to enable another standard means of storing a facility name & inspection time with the state information of a facility (¶49, ¶60-¶64).
Jang, Guo, Liu, Kyungsoo, and Tsuji fail to expressly disclose:
an abnormality detection unit configured to monitor the change value of the state information from a pre-stored value and determine the grade according to a change rate based on an intensity of change and a range of change that the change values changes with respect to the pre-stored value, the grade including a normal grade, an inspection grade, and a replacement grade;
a grade alarm unit configured to generate an alarm information based on the determined grade;
a facility life prediction unit configured to monitor how much the change rate of the state information increases during a predetermined period,
Lee discloses of predictive maintenance method for device by means of control output signal, comprising:
the grade including a normal grade, an inspection grade, and a replacement grade; (as per “when the time value consumed for the operation of the device in real time does not exceed the warning value or the danger value of the second suspect value, the device is detected as in a normal state and when the time value consumed for the operation of the device exceeds the warning value, the device is detected as in a warning state, and when the time value consumed for the operation of the device exceeds the danger value, the device is detected as in a danger state” in ¶69, as per “Here, the warning value indicates a lower level of malfunction dangerousness than the danger value, a warning state of the device is a degree to which attention and caution of the device are required, and a dangerous state of the device is a degree to which repair, inspection, or replacement of the device is required” in ¶70)
a grade alarm unit configured to generate an alarm information based on the determined grade; (as per “and if a condition in which an anomaly in the device is suspected is satisfied by comparing a collected value in accordance with operational information collected in real time with the suspect value, issues an warning to induce a service and replacement of the device at an appropriate time, to prevent huge losses of money due to device malfunction in advance” in ¶8, as per “detection conditions to efficiently search for an anomaly generated in a device and detects the device which satisfies the detection condition as an abnormal state not only to very precisely and effectively detect the anomaly occurring in the device, but also to ensure excellent reliability of the detection result” in ¶9)
In this way, Lee operates to improve predictive maintenance by detecting abnormal states of a device using suspect values derived from normal-state information and pre-malfunction information, and by recognizing graded states including normal, warning, and danger states to induce service, inspection, or replacement of the device (¶8, ¶15-¶16, ¶24). Like Jang, Guo, Liu, Kyungsoo, and Tsuji, Lee is concerned with systems relating to robotics.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s) of Jang, Guo, Liu, Kyungsoo, and Tsuji with the predictive maintenance method of Lee to enable another standard means of detecting an abnormal state by comparing real-time collected information with suspect values and recognizing graded states that trigger warning information and maintenance actions (¶10, ¶13-¶18, ¶58-¶60, ¶68-¶70). Such modification also allows the system to classify the detected condition into multiple levels of severity including normal, warning, and danger states, where the higher-severity state requires repair, inspection, or replacement (¶15-¶18, ¶69-¶70).
Jang, Guo, Liu, Kyungsoo, Tsuji, and Lee fail to expressly disclose:
an abnormality detection unit configured to monitor the change value of the state information from a pre-stored value and determine the grade according to a change rate based on an intensity of change and a range of change that the change values changes with respect to the pre-stored value,
a facility life prediction unit configured to monitor how much the change rate of the state information increases during a predetermined period,
Nahas discloses of process abnormality identification using measurement violation analysis, comprising:
an abnormality detection unit configured to monitor the change value of the state information from a pre-stored value and (as per “The metrology data includes a current value for a parameter at each of one or more locations on the current sample as well as samples from prior metrology process steps. The method includes obtaining a reference rate of change of the parameter value of the parameter for each of the one or more locations. The method further includes determining a current rate of parameter change of the parameter for each of the one or more locations. The current rate of change is associated with the current sample. The method further includes comparing the current rate of change of the parameter value to the reference rate of change of the parameter value and identifying an instance of abnormality of the fabrication process based on the comparison” in ¶4, as per “The rate of change tool 135 compares a reference rate of change of the parameter value (e.g., a historic rate of change of the parameter value) received from the SPC system 116 or calculated locally by the equipment engineering system 130 to a current rate of change of the parameter value of a current sample. In some embodiments, the reference rate of change of the parameter value is determined using one or more historical rates of change of the parameter value from historical metrology data for the operation on one or more previous samples in the fabrication process” in ¶32)
determine the grade according to a change rate based on an intensity of change and a range of change that the change values changes with respect to the pre-stored value, (as per “The current rate of change of the parameter value is compared to the reference rate of change of the parameter value. In one embodiment, the comparison is performed by applying a statistical distribution to the reference rate of change of the parameter value to identify a variance between the current rate of change of the parameter value and the reference rate of change of the parameter value. For example, a mean and a standard deviation is calculated from the historical rates of parameter values change and are compared against the current rate of change of the parameter value to identify how many standard deviations the current rate of change of the parameter value is from the mean of the one or more historical rates of change of a parameter value” in ¶51, as per “the violating locations on a sample may be indicated by a scoring system or by placing each location into a tier or degree of violation rather than a binary indication of a violating measurement. For example, a location may be classified into tiers such as “passing”, “level one violation”, “level two violation”, “level three violation”, etc. The violation tiers may correspond to the variance of the current rate of change of the parameter value from the reference rate of change of the parameter value, at each location. For example, a location that is less than one standard deviation off may be labeled as “passing,” a location that is between one and two standard deviations may be labeled as a “level-one violation,” a location that is between two and three standard deviations may be labeled as a “level-two violation,” and so forth. Each violation may be assigned a weight or a score than may be used to further establish a sample pattern” in ¶54)
a facility life prediction unit configured to monitor how much the change rate of the state information increases during a predetermined period, (as per “The rate of change tool 135 calculates a current rate of change of a current sample being measured by metrology system 110. The current rate of change may include a determination of a current parameter value rate of change. For example, a parameter is measured by metrology system 110 and a set of parameter values are obtained at various locations across a sample. These parameter values are compared against historical parameter values to determine how much the current parameter values have changed. In some embodiments, the rate of change of the parameter value is calculated by comparing the current parameter values to a sample that was fabricated immediately previous to the current sample. The difference between the current parameter values and parameter values taken from the sample fabricated immediately previous to the current sample may yield a rate of change of the parameter value” in ¶31, as per “comparison is performed by applying a statistical distribution to the reference rate of change of the parameter value to identify a variance between the current rate of change of the parameter value and the reference rate of change of the parameter value. For example, a mean and a standard deviation is calculated from the historical rates of parameter values change and are compared against the current rate of change of the parameter value to identify how many standard deviations the current rate of change of the parameter value is from the mean of the one or more historical rates of change of a parameter value.” in ¶51)
In this way, Nahas operates to identify abnormalities in a fabrication process by comparing a current rate of change of a parameter value to a reference rate of change derived from historical data, and by using threshold ranges and violation tiers to determine the degree of abnormality and initiate corrective action (¶4, ¶15, ¶31-¶36, ¶51-¶54). Like Jang, Guo, Liu, Kyungsoo, Tsuji, and Lee, Nahas is concerned with systems relating to robotics.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s) of Jang, Guo, Liu, Kyungsoo, Tsuji, and Lee with the rate-of-change abnormality analysis of Nahas to enable another standard means of determining abnormality according to a current change rate relative to a reference change rate, including threshold-based ranges and tiered levels of violation (¶4, ¶15, ¶31-¶34, ¶51-¶54, ¶73-¶77). Such modification also allows the system to use historical baseline information and current measured changes to determine the severity of the abnormal condition in a more structured and quantitative manner (¶27, ¶31-¶34, ¶51-¶54).
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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/T.R.R./Examiner, Art Unit 3658
/Ramon A. Mercado/Supervisory Patent Examiner, Art Unit 3658
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