DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-18 are pending in the application.
Examiner’s Note: The examiner has cited particular passages including column and line numbers, paragraphs as designated numerically and/or figures as designated numerically in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claims, other passages, paragraphs and figures of any and all cited prior art references may apply as well. It is respectfully requested from the applicant, in preparing an eventual response, to fully consider the context of the passages, paragraphs and figures as taught by the prior art and/or cited by the examiner while including in such consideration the cited prior art references in their entirety as potentially teaching all or part of the claimed invention. MPEP 2141.02 VI: “PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS."
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 11 recites a system. Thus, the claim is to a machine, which are statutory categories of invention.
Step 2A Prong one: the claim recites “a processor a processor configured to: inspect, using the sensor array, a batch of items in a stage of a process for defects that meet a predefined defect criteria”. The limitation “inspect” a batch of item in a stage of a process for defects that meet a predefined defect criteria, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, “inspect” in the context of this claim encompasses an observation and evaluation that an item is defected because the shape does not meet a predetermined specification.
The limitation “computing a statistical significance level of a difference between a proportion of defects in the stage of the process and a predefined proportion threshold by calculating a p-value of a statistical test about the proportion of the defects through computing a solution to an equation derived from inverting an Agresti-Coull confidence interval for the proportion of defects.” As evident from the specification, the limitation computing is a mathematical process for calculating a p-value by inverting an Agresti-Coull confidence interval, which involve volving mathematical equations (cubic polynomial or non-linear equation) related to a cumulative distribution function of a standard normal distribution. The Agresti-Coull method itself is a way to compute confidence intervals for binomial proportions. Thus, the limitation “computing” recites a concept that falls into the “Mathematical concept” group of abstract ideas. This limitation also falls into the “Mental process” group of abstract ideas, because the recited mathematical calculation is simple enough that it can be practically performed in the human mind with the help of pen and paper or a calculator (note: the use of such physical aid does not negate the nature of this limitation. Thus, the claim recites a concept that falls into the “Mathematical Concept” and “Mental Process” groups of abstract ideas.
Step 2A Prong Two: Besides the abstract ideas, the claim recites additional element of the processor configured to obtain from the sensor array, a number of items in the batch with the defects, the batch of items is a sample from a population of items. This additional element represents mere data gathering (obtaining number of item defected) that is necessary for used of the recited judicial exception (the number values are used in limitation computing the proportion of defects) and is recited at a high level of generality. The limitation “obtain” is the claim is thus insignificant extra-solution activity. The processor, sensor array is also an additional elements which is configured to carry out limitation inspect, obtain, compute, i.e., they are the tool that is used to obtain number of defected item and perform the mathematical calculation. But the processor, sensor array are recited so generically (no details whatsoever are provided other than it is a processor and a sensor array) that they represent no more than mere instruction to apply the judicial exceptions on a computer. As such, it is nothing more than an attempt to generally link the used of the judicial exceptions to the technological environment of a computer. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
Step 2B: The claim as a whole does not amounts to significantly more than the recited
exception. The claim has two additional elements. The first is the processor, which is configured to perform limitations inspect, obtain, and compute. As explained previously, the controller is at best the equivalent of merely adding the words “apply it” to the judicial exception. Mere instructions to apply an exception cannot provide an inventive concept. The second additional element is a sensor array performs it conventional function of detecting defects and is recited at a high level of generality. The claim does not improve the functioning of a computer or inspection system, but merely applies mathematical analysis to collected data. Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible.
Regarding independent claim 1 and 17, claim 1 is directed to a method, and claim 17 is directed to a non-transitory computer-readable storage medium storing a program. The claims, however, recite the same limitations of claim 11 and do not provide any additional element amount to significant more. Therefore, the claim 1 and 17 are not eligible.
Regarding claims 2-8, they dependent on claim 1 and do not provide any additional element amount to significant more than an abstract idea.
Regarding claim 18, it is dependent on claim 17 and does not provide any additional element amount to significant more than an abstract idea.
Claims 2 and 18 merely adds the step of “abandoning the plurality of items” based on the statistical result. This is a post-solution activity that applies the abstract statistical determination. Such a decision or classification based on mathematical analysis does not add a technological improvement and does not amount to significantly more than the abstract idea of claim 1.
Claim 3 specifies that the underlying equation is a cubic polynomial. This further limits the abstract idea to a particular mathematical form. Limiting an abstract idea to a specific equation or polynomial does not confer eligibility, as mathematical equations are themselves abstract concepts.
Claim 4 recites the root of the cubic polynomial is computed by computing a point estimate of an unknown population proportion. This is a routine statistical calculation and further mathematical processing of data. The claim remains directed to mathematical analysis.
Claim 5 specified that the root of the cubic polynomial is further computed in terms of a cumulative distribution function of a standard normal distribution. Use of a normal distribution and its CDF is a fundamental statistical tool and constitutes an abstract mathematical concept. This limitation does not add an inventive concept.
Claim 6 recites the equation is an underlying equation for computing the p- value and wherein the equation is a non-linear equation. Identifying the equation as non-linear merely characterizes the mathematics being performed. Mathematical equations, whether linear or non-linear, are abstract ideas and do not provide eligibility.
Claim 7 adds the root of the non-linear equation is computed by computing a point estimate of an unknown population proportion. This is conventional statistical analysis and does not integrate the abstract idea into a practical application.
Claim 8 specified the root of the non-linear equation is further computed in terms of a cumulative distribution function of a standard normal distribution. This limitation remains a purely mathematical refinement of the abstract idea and does not provide an inventive concept.
Claim 9 limits the process to a manufacturing process. This is a field-of-use limitation that does not integrate the abstract statistical analysis into a technological improvement of manufacturing machinery or processes.
Claim 10 recites performing the abstract steps at multiple stages of manufacturing process. Repeating the same abstract statistical analysis across stages does not transform the abstract idea into patent-eligible subject matter and remains a routine application of statistical quality control.
Therefore, claims 2-8, 18 are not eligible.
Regarding claims 12-16, they dependent on claim 11 and do not provide any additional element amount to significant more than an abstract idea. The claims recite the same limitations of claims 2-8 and do not provide any additional element amount to significant more. Therefore, the claim 12-16 are also not eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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) 1-3, 6-7, 9-12, 15, 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tang et al. US Pub. No. US 2022/0043812 (“Tang”) in view of “Approximate is Better than "Exact" for Interval Estimation of Binomial Proportions” to Alan Agresti and Brent A. Coull (“Agresti”) and further in view of Mori et al. US Pub. No. 2024/0319703 (“Mori”).
Regarding claim 1, Tang teaches a method comprising:
inspecting,
obtaining SEE fig. 1, 3, and TABLE 1 and 3 in par. 0051, 0053].
[0008] In some embodiments, the determining, by the processor, whether each production path is faultless in each production batch, based on the product defective rates of the plurality of production paths in the one or more production batches.
[0040] For example, if the production line 10 is a production line for liquid crystal panels, then process 1 may be a process for preparing source and drain photoresist patterns in array substrates, process 2 may be a process for preparing pixel electrode photoresist patterns, process 3 may be a process for forming pixel electrode patterns by etching, and process may be a process for preparing passivation layer patterns. Each node may be a device for completing a corresponding process, such as a lithography machine, a vacuum evaporator, a spinner, etc. However, it should be noted that these processes are only for example, and the present disclosure is not limited thereto. There may be more, fewer or other different processes in an actual production line.
[0048] In some embodiments, production record data related to a plurality of nodes in the production line may be obtained before the method 200 is implemented. For example, in some embodiments, the production record data may be received from the plurality of nodes in the production line. For example, in the embodiments described below, the plurality of nodes may be a plurality of nodes of a plurality of processes in a production line for producing liquid crystal panels, such as nodes 1-1 to 4-4 shown in FIG. 1. However, the present disclosure is not limited to this. For example, the production record data may further be obtained from a unified data production log.
comparing a proportion of defects in a stage of the process to a predefined proportion threshold or interval to determine whether the stage is faulty.
[0059] FIG. 3 schematically shows a flowchart of an implementation of step S220 shown in FIG. 2. As shown in FIG. 3, step S220 may include three sub-steps S310, S320, and S330. In step S310, a product defective rate of each of the plurality of production paths in each of the one or more production batches is calculated, based on the production record data. For example, as shown in Table 3 above, it is possible to calculate a product defective rate of each production path in corresponding batches. Then, in step S320, a mean μ of the product defective rates of the plurality of production paths in the one or more production batches and a standard deviation σ of the product defective rates of the plurality of production paths in the one or more production batches are calculated. Next, in step S330, for each production path in each production batch, if a product defective rate of a production path in a production batch is outside a threshold interval (μ−3σ, μ+3σ), then it is determined that the production path is faulty in the production batch; and if the product defective rate of the production path in the production batch is inside the threshold interval (μ−3σ, μ+3σ), then it is determined that the production path is faultless in the production batch. In other words, in step S320 and step S330, it is possible to determine whether each production path is faultless in each production batch based on the product defective rates of the plurality of production paths in one or more production batches.
[0060] According to statistical principles, assuming that product qualities are in a Gaussian distribution, the interval (μ−3σ, μ+3σ) may cover about 99.7% of the defective rates. Any error outside this interval is usually not a random error, but a gross error. In addition, in other embodiments, an interval (μ−2σ, μ+2σ) or (μ−σ, μ+σ) may further be used as a determination interval. However, since these two intervals only cover 95.5% and 68.3% of the defective rates in the Gaussian distribution, there is a high probability of systematic errors in final results in statistics.
Tang does not teach computing a statistical significance level of a difference between a proportion of defects in the stage of the process and a predefined proportion threshold by calculating a p-value of a statistical test about the proportion of the defects through computing a solution to an equation derived from inverting an Agresti-Coull confidence interval for the proportion of defects.
Agresti teaches an approximate confidence interval for a binomial proportion, widely known as the Agresti-Coull confidence interval. Specifically, Agresti teaches computing a statistical significance level of a difference between a proportion of observation and a predefined proportion threshold by calculating a p-value of a statistical test about the proportion of the observation through computing a solution to an equation derived from inverting an Agresti-Coull confidence interval for the proportion of observation [READ page 122-124].
To avoid approximation, most advanced statistics text-books recommend the Clopper-Pearson (1934) "exact" confidence interval for p, based on inverting equal-tailed binomial tests of Ho : p = po. It has endpoints that are the solutions in po to the equation… except that the lower bound is O when x = 0 and the upper bound is 1 when x = n. This interval estimator is guar-…READ page 119 (which is the statistical basis for inverting intervals to derive tests and corresponding significance levels (p-values) when comparing against a null threshold).
For example:
Let n = number of inspected items (20), x = number of defective units (4), p = true defect rate (unknown).
Adjust data n = 20+4 and x = 4+2
Adjusted defected rate: p = x+2/n+4. Thus, p = .25
Applying the normal-approximation interval to the adjusted rate:
(see equation (1) on page 119)
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41
200
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= .25 plus/minus 1.96 to the square root of .25(.75)/24 = .25 plus/minus 0.173
Inverting an Agresti-Coull confidence interval for the proportion of defects, we are 95% confident that the true defect rate lies between 7.7% and 42.3%, given that 4 defects were observed in 20 inspected items.
In summary, in defect-rate analysis, an Agresti-Coull interval is a confidence interval for the true defect proportion obtained by adding four pseudo-observations to the sample and applying a normal approximation, yielding a simple yet statistically reliable estimate of uncertainty.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify the defect-rate comparison method of Tang to compute a statistical significance level (p-value) through computing a solution to an equation derived from inverting an Agresti-Coull confidence interval for the proportion of observation of Agresti. The motivation for doing so would has been to improve the accuracy of robustness of defect detection decisions in manufacturing quality control. Agresti-Coull provide a well-known accepted method for estimating binomial proportions and accessing uncertainty. Substituting threshold comparisons with a confidence-interval based hypothesis test is a predictable variation using statistical tools to improve accuracy.
Tang further does not teach using a sensor array to inspect. However, such feature is old and well known in the field of production line defect inspection. For example:
Mori teaches an inspection management system manages, in a production line for a product including a plurality of processes, a final inspection for a product finished through the plurality of processes and a plurality of intermediate inspections before the final inspection. The production line includes a plurality of manufacturing apparatuses and a plurality of inspection apparatuses corresponding to the plurality of processes. The inspection management system includes an inspection data obtainer, an inspection result obtainer, and an inspection setting supporter that generates an inspection record diagram showing, as information about an inspection item in one of the plurality of intermediate inspections, a presence or an absence of a product determined defective in the final inspection and information identifying whether the product determined defective in the final inspection is determined defective under an inspection item in another of the plurality of intermediate inspections, and displays the inspection record diagram. Specifically, Mori teaches using a using a sensor array to inspect a batch of items in a stage of a process for detects that meet a predefined detect criteria [image sensor (camera – par. 0055-0058, 0066, 0075].
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify the method of Tang with the well-known sensor array of Mori to inspect a batch of items in a stage of a process for detects. The motivation for doing so would has been to automate the inspection. Thus, improve speed and accuracy.
Regarding claim 2, Tang in view of Agresti teaches abandoning the plurality of items responsive to computing statistical evidence that the proportion of defects exceeds the predefined proportion threshold [par. 0045, 0061, 0063, 0086 of Tang].
Regarding claim 3, Agresti teaches the equation is an underlying equation for computing the p-value and wherein the equation is a cubic polynomial [SEE equation on page 119-120].
Regarding claim 6, Agresti teaches the equation is an underlying equation for computing the p-value and wherein the equation is a non-linear equation [SEE equation on page 119-120].
Regarding claim 7, Agresti teaches the root of the non-linear equation is computed by computing a point estimate of an unknown population proportion [SEE equation in page 122- The “ADD tow successes and two failures” adjusted Wald interval].
Regarding claim 9, Tang teaches the process is a manufacturing process [SEE fig. 1].
Regarding claim 10, Tang in view of Agresti teaches the inspecting, obtaining, and computing the statistical significance level are performed in a plurality of different stages of the manufacturing process [SEE fig. 1 and Table 3 of Tang and discussion of Agresti in claim 1].
Regarding claims 11-12, 15, they are directed to a system to implement the method of steps as set forth in claims 1-3, 6-7. Therefore, they are rejected on the same basis as set forth hereinabove.
Regarding claim 17-18, they are directed to a non-transitory computer-readable storage medium storing a program to implement the method of steps as set forth in claims 1-2. Therefore, they are rejected on the same basis as set forth hereinabove.
Allowable Subject Matter
Claims 4-5, 8, 13-14, 16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action, and including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: Claims 4-5, 8, 13-14, 16 are considered allowable since, when reading the claims in light of the specification, none of the references of record alone or in combination disclose or suggest the combination of subject matter specified in the dependent claim: “the root of the cubic polynomial is computed by computing a point estimate of an unknown population proportion”, “the root of the non-linear equation is further computed in terms of a cumulative distribution function of a standard normal distribution”.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Pub. No. 2024/0248464 to Nainggolan et al. teach an inspection system 2 inspects the product for any defects. The inspection system 2 includes a sensor and a decision device. The sensor detects a physical quantity about the product. The decision device determines, based on the physical quantity detected by the sensor, whether the product has any defects or not. It may be determined appropriately based on, for example, a quality control standard of the product what condition of the product is regarded as a defect by the inspection system 2.
US 2019/0164101 to Koyama et al. teach a data analysis apparatus capable of identifying a defect factor of a product based on objective variables expressed in binary values. A data analysis system according to a first aspect of the present invention includes a data collecting device configured to collect manufacturing history data of units to which identification information is given and binary first key performance indicator (KPI) indicating whether the units are good products or defective products; and a data analysis apparatus configured to select a feature of explanatory variables related to a defect factor of the units from data to which the first KPIs and K (K is an integer of 2 or greater) explanatory variables are given by 1:1. The data analysis apparatus generates M (M is an integer of 3 or greater) groups each including data regarding a plurality of units from data to which the first KPIs and the K explanatory variables are given by 1:1, generates a second KPI indicating the states of the groups based on the values of a plurality of first KPIs included in the groups, and selects a feature for the first KPIs based on a correlation analysis between the second KPI of each group and a feature of each group calculated based on the explanatory variables.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VINCENT HUY TRAN whose telephone number is (571)272-7210. The examiner can normally be reached M-F 7:00-4:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamini S Shah can be reached at 571-272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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VINCENT H TRAN
Primary Examiner
Art Unit 2115
/VINCENT H TRAN/Primary Examiner, Art Unit 2115