DETAILED ACTION
This office action is in response to the filling with the office dated 08/15/2024
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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. CN 202311187482.1, filed on 14 Sep 2023.
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.
Claims 1,2,8-10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by HEO et al. (Hereinafter, “Heo”) in the Patent Application Publication Number KR 100975832 B1.
Regarding independent claim 1, Heo teaches, “A manufacturing method of an electronic device,” ([page 4, paragraph 8], “flat panel display panel” and “impression inspection apparatus.” Reads on “manufacturing method of an electronic device.”)
“Comprising: a detection step, used to detect a pressure distribution of a manufacturing element,” ([page 5, paragraph 8], “inspection server” and “indentation detection unit.” Reads on, “detection step, used to detect a pressure.”)
“The detection step comprising: providing a detection element; pressing the manufacturing element on the detection element to generate a plurality of indentation patterns;” ([page 4, paragraph 8], “the indentation inspection apparatus 100 according to the present invention is for fixing the flat panel display panel M (hereinafter, referred to as a 'panel') in which the indentation 8 is generated.” Moreover, “using the luminance distribution information of each indentation (8).” Furthermore, [page 5, paragraph 9], “inspection area selecting unit.” Reads on, “pressing the manufacturing element on the detection element to generate a plurality of indentation patterns.”)
“Converting the indentation patterns into a plurality of image data;“ ([Page 4, paragraph 8], ”The camera 130 is connected to the microscope 120 to capture the back of the camera, the auxiliary camera 130a for adjusting the focus of the microscope 120, and the driving of the microscope 120 and the lighting device 160. In addition to the control, the inspection server 140 for setting the inspection area requiring the indentation inspection of the image data captured by the camera 130.” Reads on, “converting the indentation patterns into a plurality of image data.”)
calculating an image feature value by using the image data; ([Page 6, paragraph 3], “image data analyzer” and “indentation index calculator” reads on, “calculating an image feature value by using the image data.”)
and comparing a relationship between the image feature value and a threshold and generating a comparison result.” ([Page 5, paragraph 9], “To store the offset value between the mark of the image data and the inspection area of the image data by using the mark data storage unit 141b to be stored as) and master data including pattern information corresponding to the mark and the inspection area. The offset value storage unit 141c and a matching unit 141d for matching the inspection area to a position corrected by the offset value based on the mark of the image data.” Reads on “image feature value and a threshold and generating a comparison result.”)
As per claim 2, Heo teaches, “The manufacturing method according to claim 1, wherein converting the indentation patterns into the image data comprises: converting the indentation patterns into the image data through an area imaging element.” ([Page 4 and 5, paragraph 9], “The camera 130 captures the indentation 8 observed in this way to capture image data.”) Reads on, “converting indentation patterns into the image data through an area imaging element.”)
As per claim 8, Heo teaches, “The manufacturing method according to claim 1, wherein the detection step further comprises: generating a qualified comparison result when the image feature value is less than or equal to the threshold.” ([Page 7, paragraph 5], “if the indentation index F of the detected specific detection area is smaller than the indentation index reference value, it is judged that there is not sufficient compression or other noise.” Reads on, “generating a qualified comparison result when the image feature value is less than or equal to the threshold.”)
As per claim 9, Heo teaches, “The manufacturing method according to claim 1, wherein the detection step further comprises: generating an unqualified comparison result when the image feature value is greater than the threshold.” ([Page 7, paragraph 5], “On the contrary, the indentation index F is larger than the indentation index reference value. In this case, it is judged that excessive compression or other noise is caused.”)
As per claim 10, ”The manufacturing method according to claim 1, further comprising: a threshold establishing step,” ([page 3, paragraph 10], “indentation inspection method according to the present invention comprises a substrate fixing step of fixing the indentation generated substrate to the work stage to be exposed toward the microscope;” reads on “a threshold establishing step.”)
“The threshold establishing step comprising: pressing the manufacturing element with a first set deformation amount on the detection element to generate a plurality of first reference patterns;” ([Page 5, paragraph 4], “The microscope 120 is installed on the upper side of the work stage 110 to which the panel M is fixed, and penetrates the rear surface of the glass substrate 1 to form an indentation generated in the panel electrode 4 on the front surface of the glass substrate 1.” Moreover, [page 5, paragraph 8] “information of each indentation.” Reads on, “pressing the manufacturing element with a first set deformation amount on the detection element to generate a plurality of first reference patterns;”)
“Convert the first reference patterns into a first reference value;” ([Page 6, paragraph 3], “The indentation detector 142” moreover, “An area storage unit 142c for storing predetermined edge shape and area information as a center,” reads on, “Convert the first reference patterns into a first reference value.”)
“Pressing the manufacturing element with a second set deformation amount on the detection element to generate a plurality of second reference patterns;” ([Page 6, paragraph 4], “the larger or higher the indentation part 8 appears brighter, the darker the indentation part 8 is smaller or lower part appearing darker. In the bright part, the conductive particles are sufficiently compressed by the bumps 7.” Moreover, [page 5, paragraph 1], “Accordingly, the inspection server 140 selects an inspection region from the input image data and inspects each indentation 8 of the selected inspection region,” reads on, “Pressing the manufacturing element with a second set deformation amount on the detection element to generate a plurality of second reference patterns.”)
“Converting the second reference patterns into a second reference value; and establishing the threshold according to the first reference value and the second reference value.” ([Page 6, paragraph 3], “and a height detection unit 142d for detecting the height H of the indentation 8 using the analyzed luminance. And an indentation index calculator 142e for detecting an indentation index using the area and the height H.” Reads on, “Converting the second reference patterns into a second reference value; and establishing the threshold according to the first reference value and the second reference value.”)
Claim 16 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by ABU EBAYYEH et al. (Hereinafter “Abu Ebayyeh”) A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry.
As per independent claim 16, Abu Ebayyeh, “A manufacturing method of an electronic device, ([Page 13, line 2], “assembled through electronic assembly lines” Reads on “manufacturing method of an electronic device.”)
“Wherein the manufacturing method comprises a detection step for detecting a manufacturing equipment,” ([Page 14, line 4], “AOI system.” Reads on “detection step for detecting a manufacturing equipment )
Wherein the detection step comprises: providing a plurality of manufacturing equipment,([Page 21, line 3-5], “According to Chin and Harlow in [13], a standard AOI system consists of camera and lightning setup, computer (processor), conveyor and sorting mechanism as shown in Figure 16.” Reads on, “providing a plurality of manufacturing equipment.)
”Wherein each of the manufacturing equipment has a plurality of historical feature value trends,” ([Page 21, lines 7 and 8], “The computer is responsible for applying the inspection algorithm in terms of prepossessing, feature extraction and selection and classification.” Reads on plurality of historical feature value trends”)
and generating a plurality of changing trends of the manufacturing equipment by using a plurality of feature values corresponding to a same evaluated work count among the historical feature value trends of each of the manufacturing equipment; ([Page 34, paragraph 3], “Feature extraction process involves applying one or more of image processing techniques (e.g. frequency analysis and segmentation) in order to describe the characteristics of the studied regions (e.g. defects and abnormalities).” Reads on, “generating a plurality of changing trends of the manufacturing equipment by using a plurality of feature values corresponding to a same evaluated work count among the historical feature value trends of each of the manufacturing equipment;”)
Generating a threshold by using the changing trends; and comparing the changing trends and the threshold and generating a comparison result.” ([Page 34, paragraph 3], “The purpose of feature selection step is to consider the important feature values only that can contribute to the classification process and discard the redundant ones. This step is very essential in reducing the computational time for the inspection algorithm. Principle Component Analysis is very popular technique used in feature selection. Other algorithms such as along Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Adaboost and Neural Networks are also used for this purpose.”) Reads on, “generating a threshold by using the changing trends; and comparing the changing trends and the threshold and generating a comparison result.”
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.
Claims 3-6 are rejected under 35 U.S.C. 103 as being unpatentable over Heo In view of KITAMURA et al. (Hereinafter “Kitamura”) In the Patent Application Publication Number JP 2010268009 A.
As per claim 3, Heo teaches, “The manufacturing method according to claim 1, wherein converting the indentation patterns into the image data comprises: ([Page 4 and 5, Paragraph 9], “The camera 130 captures the indentation 8 observed in this way to capture image data.” Reads on, “Converting the indentation patterns into the image data”)
Heo is silent on, “Converting the indentation patterns into the image data through a line scan imaging element.”
Kitamura teaches, “Converting the indentation patterns into the image data through a line scan imaging element.” ([Page 5, Paragraph 4], “when one electron beam is scanned in the X direction.” Reads on, “line scan imaging element.”
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, to modify Heo in view of Kitamura by implementing the teachings of electron beam scanning. Combining the two would yield the predictable result of increased precision and data gathering. Electron beam scanning is an obvious and commonly used method in the inspection process for manufacturing. (KSR)
As per claim 4, Heo is silent on, “The manufacturing method according to claim 1, wherein the image data are grayscale values respectively.”
Kitamura teaches, “The manufacturing method according to claim 1, wherein the image data are grayscale values respectively.” ([Page 45, Paragraph 6], ”which is one of the pattern deformation amounts obtained from the entire inspection unit area, into information for grayscale display and overwriting defects.” Reads on, “image date are grayscale values respectively.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, to modify Heo in view of Kitamura by implementing the teachings of grayscale would improve the inspection process for detecting surface defects such as scratches or cracks that potentially are caused by the manufacturing process. One would been motivated to include grayscale inspection to further improve the quality of their product. (KSR)
As per claim 5, Heo is silent on, “The manufacturing method according to claim 1, wherein the image feature value is a slope value.”
Kitamura teaches, “The manufacturing method according to claim 1, wherein the image feature value is a slope value.” ([Page 26, before paragraph 1], “An example in which the X component of (x, y .sub.0 ) is approximated by a regression line D (x) is shown. When the X component of the vector d (x, y .sub.0 ) is approximated by a regression line D (x) = ax + b, the slope a corresponds to the magnification fluctuation amount.” Reads on, “The manufacturing method according to claim 1, wherein the image feature value is a slope value.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention by modifying Heo in view of Kitamura to incorporate the teachings of a slope value would yield the predictable result of capturing the shape of the Indentation and the quality of the edge and boundaries. (KSR)
As per claim 6, Heo is silent on, “The manufacturing method according to claim 1, wherein the image feature value is a standard deviation value.”
Kitamura teaches, “The manufacturing method according to claim 1, wherein the image feature value is a standard deviation value.” ([Page 26, paragraph 2], “standard deviation of the X component” reads on, “image feature value is a standard deviation value.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify Heo in view of Kitamura to implement the teachings of a standard deviation value. Doing so would yield the predictable result of improving the consistency of detecting any variation of the patterns during the inspection process. This is a well-known method for those who have ordinary skill in the art. (KSR)
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Heo in view of JNAWALI et al. (Hereinafter “Jnawali”) In the Patent Application Publication Number US 20230360595 A1.
As per claim 7, Heo is silent on, “The manufacturing method according to claim 1, wherein the image feature value is a mean absolute deviation value.”
Jnawali teaches, “The manufacturing method according to claim 1, wherein the image feature value is a mean absolute deviation value.” ([0030]) “where image frame N (310/330) is not flat and frame (N+1) (320/330) is almost flat in terms of histogram distribution and mean absolute deviation.” Reads on “Image feature value is a mean absolute deviation value.”
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify Heo in view of Jnawali by incorporating the teachings of mean absolute deviation would yield the predictable result by showing how far each point is from the center mark and help distinguish rough surfaces. This formula is well-known and widely used in the manufacturing industry. (KSR)
Claims 11-14 is rejected under 35 U.S.C. 103 as being unpatentable over Heo in view of Abu Ebayyeh. “A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry"
Regarding independent claim 11, Heo teaches “A manufacturing method of an electronic device, wherein the manufacturing method comprises a detection step for detecting a manufacturing element,” ([Page 7, paragraph 5], “if the indentation index F of the detected specific detection area is smaller than the indentation index reference value, it is judged that there is not sufficient compression or other noise.” Reads on, “generating a qualified comparison result when the image feature value is less than or equal to the threshold.”)
“Wherein the detection step comprises: pressing the manufacturing element on a detection element to generate a plurality of indentation patterns,” ([Page 5, paragraph 4], “The microscope 120 is installed on the upper side of the work stage 110 to which the panel M is fixed, and penetrates the rear surface of the glass substrate 1 to form an indentation generated in the panel electrode 4 on the front surface of the glass substrate 1.” Moreover, [page 5, paragraph 8] “information of each indentation.” Reads on, “pressing the manufacturing element on a detection element to generate a plurality of indentation patterns”
“And converting the indentation patterns into a plurality of image data ([Page 4, paragraph 8], ”The camera 130 is connected to the microscope 120 to capture the back of the camera, the auxiliary camera 130a for adjusting the focus of the microscope 120, and the driving of the microscope 120 and the lighting device 160. In addition to the control, the inspection server 140 for setting the inspection area requiring the indentation inspection of the image data captured by the camera 130.” Reads on “converting the indentation patterns into a plurality of image data.”)
“To calculate a first image feature value of the manufacturing element corresponding to a first work count;” ([Page 6, paragraph 3], “a height detection unit 142d for detecting the height H of the indentation 8 using the analyzed luminance. And an indentation index calculator 142e for detecting an indentation index using the area and the height H.” Reads on, “calculate a first image feature value of the manufacturing element corresponding to a first work count”)
Heo is silent on, “Generating a feature value trend according to the first image feature value corresponding to the first work count and a plurality of image feature values corresponding to different work counts that are less than the first work count;”
Abu Ebayyeh teaches, “Generating a feature value trend according to the first image feature value corresponding to the first work count and a plurality of image feature values corresponding to different work counts that are less than the first work count;” ([Page 22, line 53-56], “Here three types of feature values were calculated using Lucas-Kanade algorithm to highlight Mura defects. These feature values are flow magnitude, mean flow magnitude and flow density in the optical flow field. A certain threshold for each feature value were decided such that if the value exceeds the threshold the sample image is considered to have a Mura defect and the opposite otherwise.” Reads on, “Generating a feature value trend according to the first image feature value corresponding to the first work count and a plurality of image feature values corresponding to different work counts that are less than the first work count”
Heo teaches, “Comparing a plurality of historical feature value trends to find a reference historical feature value trend that is most similar to the feature value trend; and calculating a work count of unqualified work that is potentially generated by the manufacturing element by using the reference historical feature value trend.” ([Page 7, paragraph 4], “FIG. 7C, the indentation index F in each detection region where the indentation 8 is located is obtained, and the indentation index F of the detected specific detection region satisfies the indentation index reference value. Only if it is determined that the normal indentation, and further by checking the number of such normal indentation (8) in the entire inspection area it is possible to confirm the electrical conduction state.” Reads on, “Comparing a plurality of historical feature value trends to find a reference historical feature value trend that is most similar to the feature value trend; and calculating a work count of unqualified work that is potentially generated by the manufacturing element by using the reference historical feature value trend.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify Heo in view of Abu Ebayyeh to implement the teachings of the Lucas-Kanade algorithm for detecting any unwanted change in a pattern. It is Well-known and widely used in the inspection industry for tracking discrepancies and displacements. (KSR)
As per claim 12, Heo teaches, “The manufacturing method according to claim 11, wherein a maximum work count corresponding to at least one of the historical feature value trends is greater than the first work count.” ([Page 8, paragraph 3], “If the detected indentation index (F) is smaller than the indentation index reference value, it is judged that there is not enough compression or other noise.” Reads on “maximum work count corresponding to at least one of the historical feature value trends is greater than the first work count.”)
As per claim 13, Heo is silent on, “The manufacturing method according to claim 11, wherein the detection step further comprises: generating the feature value trend by using a linear regression method.”
Abu Ebayyeh teaches, “The manufacturing method according to claim 11, wherein the detection step further comprises: generating the feature value trend by using a linear regression method.” ([Page, 55, Line 3-5], “The sample images were converted to gray-level data, this process was accelerated by dividing the image into several sub-images. Gray-level data are then modelled using the linear regression model.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify Heo in view of Abu Ebayyeh to implement the teachings of linear regression model which would add another mathematical concept which would be used for generating trends. Linear regression model is well-known and widely used with in the inspection industry.
As per claim 14, Heo is silent on, “The manufacturing method according to claim 11, wherein comparing the historical feature value trends to find the reference historical feature value trend that is most similar to the feature value trend comprises:”
Abu Ebayyeh teaches, ([Page 71, line 5-8], “Ideally, if the inputs from the historical data were fed to the MLP, the same corresponding outputs from the historical data have to be obtained. Therefore, a sort of comparison process has to be established between the actual outputs of the network and real outputs from the historical data to have a sense of the error.”)
Abu Ebayyeh teaches, “Comparing to find the reference historical feature value trend by using dynamic time warping.” ([page 70, paragraph 1], “Jeong et al. in [73] used DTW algorithm to detect anomaly defect patterns of WBM and compared it with nearest neighbor classifier that is based on Euclidean distance. First, they presented the spatial pattern on the WBM using a spatial correlogram, where a spatial correlogram represents the correlation between values of the same variable at different locations.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify Heo in view of Abu Ebayyeh to implement the teachings of dynamic time warping which aligns two sequences for comparison during inspecting which also excels at similarity shape measurements. Dynamic time warping is well-known in the inspection industry for its analysis prowess.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Heo et al. In view of Abu Abayyeh and further in view of WANG et al. (Hereinafter “Wang”) In the Patent Application Publication Number US 11733309 B2
As per claim 15, Heo teaches, “The manufacturing method according to claim 11, wherein comparing the historical feature value trends to find the reference historical feature value trend that is most similar to the feature value trend comprises:” ([Page 3, paragraph 7], “it is preferable to further include an indentation index reference value storage unit for storing the indentation index reference value to be able to detect the presence or absence of the indentation by comparison with the detected indentation index”)
Heo is silent on, “comparing to find the reference historical feature value trend according to a plurality of slopes and a plurality of intercepts of the historical feature value trends comparing to find the reference historical feature value trend”
Abu Abayyeh teaches, “comparing to find the reference historical feature value trend” ([Page 71 line 4-7], “In order for the MLP to work efficiently it has to be trained, this can be performed by relying on the historical data (training data) for the inputs and corresponding outputs. Ideally, if the inputs from the historical data were fed to the MLP, the same corresponding outputs from the historical data have to be obtained.”)
Abu Abayyeh is silent on, “according to a plurality of slopes and a plurality of intercepts of the historical feature value trends comparing to find the reference historical feature value trend”
Wang teaches, “comparing to find the reference historical feature value trend according to a plurality of slopes and a plurality of intercepts of the historical feature value trends comparing to find the reference historical feature value trend” ([0042], ”FIG. 7 graphically illustrates polynomial fits (using either linear regression or recursive least square estimation) determined based upon data from each of the plurality of battery cells illustrated upon FIG. 5, with graph 600 plotting upon a vertical axis 610 intercepts for each of the polynomial fits versus line slopes for each of the polynomial fits on the horizontal axis 620.” Reads on, “Comparing to find the reference historical feature value tend according to a plurality of slopes and a plurality of intercepts of the historical feature value trends.”
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify Heo in view of Abu Abayyeh and further in view of Wang to implement the teachings of slopes and intercepts to observe any change in patterns generated from pressure during the manufacturing process. This is a well-known and proven method in the manufacturing industry inspection process. (KSR)
Claims 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Abu Ebayyeh in view Wang In the Patent Application Publication Number US 11733309 B2
As per claim 17, Abu Ebayyeh is silent on, “The manufacturing method according to claim 16, wherein the detection step further comprises: obtaining slopes of the a plurality of changing trends”
Wang teaches, “The manufacturing method according to claim 16, wherein the detection step further comprises: obtaining slopes of the a plurality of changing trends” ([0017], “The negative slope provided by the α value may be compared to the slope values of other similar data or to threshold a value,” Reads on “obtaining slopes of the a plurality of changing trends.”)
“By using a linear regression method.” ([Page 55, line 3-5], “The sample images were converted to gray-level data, this process was accelerated by dividing the image into several sub-images. Gray-level data are then modelled using the linear regression model.” Reads on, “using a linear regression method.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify Abu Ebayyeh in view of Wang to combine the linear regression with the teachings of slopes would yield the predictable result of improving accuracy generating trends, rates of change and stability monitoring which would be used to further improve an inspection process. (KSR)
As per claim 18, Abu Ebayyeh teaches, “The manufacturing method according to claim 17, wherein generating the threshold by using the changing trends comprises: ([Page 39, line 10-12],” The voids are then located and labelled using certain measures and features such gray level value, area, and compactness factor and certain thresholds were estimated from these measures to perform the classification process.” Reads on, “generating the threshold by using the changing trends”
Abu Ebayyeh is silent on, “using the slopes of the changing trends as a plurality of deterioration values of the manufacturing equipment;”
Wang teaches, “using the slopes of the changing trends as a plurality of deterioration values of the manufacturing equipment” ([0042], “negative slope” reads on, “using the slopes of the changing trends as a plurality of deterioration values of the manufacturing equipment”)
Abu Ebayyeh teaches, “Obtaining a plurality of valid deterioration values from the deterioration values; and setting the threshold according to the valid deterioration values.” ([Page 39, line 22-25], “Morphological operation with the aid of thresholding were used to remove the holes along the image edge and to repair the shape of the other holes via dilation. A certain threshold was defined, such that if the sizes of the deformed holes are larger than the threshold then the LCD is considered deformed.” Reads on, “Obtaining a plurality of valid deterioration values from the deterioration values; and setting the threshold according to the valid deterioration values.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify Abu Ebayyeh in view of Wang to implement the teachings of a negative slope which would show the operator the beginning stages of generating defects. Giving the operator time to correct the defect. The use of slopes is well-known in the inspection process of manufacturing. (KSR)
As per claim 19, Abu Ebayyeh teaches, “The manufacturing method according to claim 18, wherein: the valid deterioration values comprise a first deterioration value and a second deterioration value, the first deterioration value is different from the second deterioration value, and the threshold is greater than the first deterioration value and less than the second deterioration value.” ([Page 60, paragraph 3], “The x-axis on the control chart can correspond to the location on the image and the y-axis to the frequency of analysed feature value at that location (e.g. number of pixels, intensity level etc.) [356]. Upper control limits (UCL) and lower control limits (LCL) are established on the y axis to aid the classification process, such that if the feature value exceeds the limit an action must be taken (e.g. consider a defect is detected). Many factors contribute to the quality of this method such as the window size considered on the x-axis and the range between UCL and LCL [357]. Despite of this method simplicity, it is necessary to manually determine if there is any abnormality in the control charts and what kind of abnormality occurs. Furthermore, it is easy to detect abnormalities beyond the control limit, but difficult to do so within the control limit, which is easily affected by the experience level of quality control personnel [358].”)
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Abu Ebayyeh in view of LEE et al. (Hereinafter “Lee”) In the Patent Application Publication Number US 20230153490 A1.
As per claim 20, Abu Ebayyeh is silent on, “The manufacturing method according to claim 16, wherein generating the threshold comprises: calculating the threshold by using a cumulative distribution function and a maintenance probability of the manufacturing equipment.”
Lee Teaches, “The manufacturing method according to claim 16, wherein generating the threshold comprises: calculating the threshold by using a cumulative distribution function and a maintenance probability of the manufacturing equipment.”
([0104], “n various embodiments, predictive maintenance system 602 generates reliability metrics based on the trained models. For example, predictive maintenance system 602 may generate a MTBF metric, a time to X % failure metric, a cumulative distribution function (CDF), a reliability function, a probability distribution function (PDF), a hazard rate function (HRF), and/or other statistical measures.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify Abu Ebayyeh in view of Lee to implement the teachings of a predictive maintenance system. Doing so would keep the AOI system reliable and accurate ensuring quality inspections during manufacturing processing. Preventive maintenance procedures which operate utilizing thresholds is well-known in the manufacturing industry. (KSR)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. O’Neil et al (US 20180000468 A1) teaches, Systems and methods are disclosed in which customized instruments, e.g., surgical instruments, can be manufactured to provide improved ergonomics, comfort, and accuracy. Instruments can be customized based on various parameters, including a quantitative assessment of the user, desired or intended use of the instrument, user preferences, and so forth. Exemplary instrument properties which can be customized include size, geometry, durometer, mechanical assist, texture, color, markings, modulus of elasticity, sensor inclusion, sensor type, sensor feedback type, balance, finish, and weight.
Minakawa et al (US 9858659 B2) teaches, Provided is a pattern inspecting and measuring device that decreases the influence of noise and the like and increases the reliability of an inspection or measurement result during inspection or measurement using the position of an edge extracted from image data obtained by imaging a pattern as the object of inspection or measurement. For this purpose, in the pattern inspecting and measuring device in which inspection or measurement of an inspection or measurement object pattern is performed using the position of the edge extracted, with the use of an edge extraction parameter, from the image data obtained by imaging the inspection or measurement object pattern, the edge extraction parameter is generated using a reference pattern having a shape as an inspection or measurement reference and the image data.
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/JARELL W PAXTON/Examiner, Art Unit 2858
/EMAN A ALKAFAWI/Supervisory Patent Examiner, Art Unit 2858 4/23/2026
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