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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Response to Amendment/Argument
Applicant argument and amendment filed on 06/13/2025 are considered.
Claim objection: Applicant amended claims 1, 8, 9 and 17 as suggested. Therefore, claim objection is withdrawn.
Claim rejection 35 U.S.C112:
112(a):
Applicant amended claims 1, 2, 9, 10, 17 and 18 to remove the recitation “control threshold”. The amendment overcomes the rejection under 35 U.S.C 112 (a). Therefore, the rejection under 35 U.S.C 112 (a) is withdrawn.
112(b):
Applicant amended claim 6, to clarify the color for their respective warning information. The amendment overcomes the rejection under 35 U.S.C 112 (b). Therefore, the rejection under 35 U.S.C 112 (b) is withdrawn for claim 6.
Applicant amended claims 1, 2, 9, 10, 17 and 18 to remove the recitation “control threshold”. The amendment overcomes the rejection under 35 U.S.C 112 (b). Therefore, the rejection under 35 U.S.C 112 (b) is withdrawn for claims 1, 2, 9, 10, 17 and 18.
Applicant notes claim 3 and 7-8 have been cancelled in page 15/22 of remarks. But these claims are not cancelled in claim set filed on 06/13/2025.
Rejection under 35 U.S.C 101:
Applicant argues “Regarding Prong I of Step 2A, the Office Action alleges that claim limitations in claim 1 fall into the or mathematical concept and mental process, therefore, claim 1 recites an abstract idea.
In amended independent claim 1, the defective item and corresponding test values are input into a prediction model of defect cause of a global quality control system, to output a plurality of defect causes and a probability of each defect cause through the prediction model of defect cause, the above steps cannot be performed manually, and are not abstract idea. Therefore, amended independent claim 1 does not recite abstract idea.” Examiner respectfully disagrees because examiner views inputting test item (or defective item) and the input test values into a prediction model (i.e., prediction equation or mathematical relationship) to outputting a plurality of defect causes and a probability (i.e., result of mathematical calculation or relationship) of each defect cause through the prediction model of defect cause still involve mathematical relationships and mental process. The inputting defects items and test values are insignificant extra solution activity to the judicial exceptions. Therefore, the amended independent claims still recite abstract idea. MPEP also suggests “a claim to “collecting information, analyzing” it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); Please see MPEP 2106.04 (a) (2) III A.
Applicant argues “Regarding Prong II of Step 2A, the Office Action further alleges that claim 1 does not integrate the abstract idea into a practical application.
Even if, arguendo, amended independent claim 1 involves an abstract idea at some level, the defective item and corresponding test values are input into a prediction model of defect cause of a global quality control system, to output a plurality of defect causes and a probability of each defect cause through the prediction model of defect cause in amended independent claim 1.
Therefore, amended independent claim 1 integrates the abstract idea into a practical application of defect cause prediction.” Examiner respectfully disagrees because the amendment further directs towards an abstract idea as discussed above, therefore, do not provide a practical application of the defect cause prediction.
Applicant argues “Furthermore, regarding Step 2B, the Office Action further alleges that claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Even if, arguendo, claim 1 involves an abstract idea at some level, it is directed to "significantly more" than an abstract idea, and thus complies with section 101 under Part 2 of the two-part test. Indeed, as stated in Alice, "... an invention is not rendered ineligible for patent simply because it involves an abstract concept. Application of such concepts to a new and useful end remains eligible for patent protection." (Slip opinion at p. 4, citing Diamond v. Diehr.) Thus, claim 1 at issue cannot properly be rejected under section 101 merely because they may, at some level, involve an abstract concept.
As the USPTO guidelines make clear, as part of the step 2B analysis, "the rejection should identify the additional elements in the claim and explain why the elements taken individually and in combination do not amount to a claim as a while that is significantly more than the exception identified in Step 2A." USPTO Memorandum dated May 4, 2016, titled Formulating a Subject Matter Eligibility Rejection...., at page 1.
The Office Action does not address the individual elements of the claim, taken alone or in combination. Instead, the Office Action makes a blanket statement that the additional elements are merely related to a high level of generality to simply perform the generic computer functions.
Moreover, Applicant submits that claim 1 does include significantly more than the alleged abstract idea.
According to the features of amended independent claim 1, at least one cause of each defect of the product is predicted according to historical maintenance data of the product and a self-learning record of the electronic device, the defective item and corresponding test values are input into a prediction model of defect cause of a global quality control system, to output a plurality of defect causes and a probability of each defect cause through the prediction model of defect cause. Therefore, the present application does have an actual application scenario of defect cause prediction, and improve the technology field of quality control of products.”
Examiner respectfully disagrees because Examiner views the amended claim limitation as an abstract idea, which cannot be considered as an inventive concept. MPEP 2106.05 I also suggests “a claim for a new abstract idea is still an abstract idea.” and an inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself." Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016). Therefore, the claims do not provide inventive concepts and are not sufficient to amount to significantly more than the judicial exception. Therefore, taking into the consideration of all the limitations of the independent claims, Examiner views the use of the abstract idea (mathematical relationships or mental processes) recited in the independent claims do not provide any significant improvement in the related field of technology as the amended independent claims are further directed towards the judicial exceptions.
For at least the reasons above, examiner considers that amended independent claim 1 not only recites statutory subject matter pursuant to 35 U.S.C. § 101, but also recites limitations that are insignificant and directed towards the abstract ideas. Therefore, amended independent claims 1, 9 and 17 do not overcome the rejection under 35 USC101.
Regarding prior art rejection 103:
predicting at least one cause of each defect of the product according to historical maintenance data of the product and a self-learning record of the electronic device (para 74 In some embodiments, when a defect detection model has been deployed in the system, the defect detection model that has been deployed is also obtained by training based on pictures… When the user selects historical pictures and new pictures for training, the trained model can either learn the parameters to detect the latest defects, or retain the ability to detect previous defects.
Para 99. Specifically, the product identification is used to identify the product. The product identifier may be generated according to a preset rule. For example, the identification is generated according to the production sequence of the products and the discharge location. Different defect categories correspond to different processing methods. Some defective products need to be scrapped. Some defects can be further optimized, and the optimization method can be different. For example, the optimization method can be to add glue or remove dirt. For example, if the defect is a chipped glass, the glass needs to be scrapped. The processing method corresponding to each defect category can be set in advance.).
Herein examiner views the steps of the defect detection method implemented in the prior art Shen uses a machine learning technique to record (i.e., create history) and learn the defect (i.e., reinforcement learning which is a type of self-learning). In para 99 examiner views Shen provides an example of determining or predicting defect cause, i.e., if defect is a chipped glass (which is at least one of the causes of the defect), the glass needs to be scrapped for product optimization. The optimization is realized according to historical maintenance data (i.e., the product needs to be scrapped) of the product.
However, there is no prior art to address for the newly added limitation for the independent 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 1-20 are rejected under 35 U.S.C 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, natural phenomenon, or an abstract idea) without significantly more.
Specifically, claim 1 recites:
A method for monitoring products for defects implemented in an electronic device comprising:
obtaining product data in real time, and determining whether a product is defective based on the product data;
in response that the product is determined to be defective, outputting first warning information based on the number of defects of the product satisfying a first preset condition;
the first preset condition comprising a number of defects in a first preset time period being greater than or equal to a first threshold, the number of defects of a defective item in the first preset period being greater than or equal to a second threshold, the number of machines outputting products with defects being greater than or equal to a third threshold, a rate of defects being greater than a mean value, and the number of defects in a manufacturing line being less than a fourth threshold;
obtaining the rate of defects of the product every first preset time period, and outputting second warning information based on the rate of defects of the product satisfying at least one of a second preset condition, a third preset condition, and a fourth preset condition;
the second preset condition comprising the rate of defects being greater than an upper control limit value of rate of defects within a second preset time period, the rate of defects being greater than a fifth threshold, the number of defects being greater than or equal to a sixth threshold, and the number of produced products being greater than or equal to a seventh threshold, the third preset condition comprising a consecutive eighth threshold number of points in a control chart of rate of defects exceeding a median, and the number of defects being greater than or equal to a ninth threshold, the fourth preset condition comprises a consecutive tenth threshold number of points raising in the control chart;
in response that any warning information is output, analyzing distribution of the defects of the product;
predicting at least one cause of each defect of the product according to historical maintenance data of the product and a self-learning record of the electronic device; and
inputting the defective item and corresponding test values into a prediction model of defect cause of a global quality control system, and outputting a plurality of defect causes and a probability of each defect cause through the prediction model of defect cause, the prediction model of defect cause being established by a convolutional neural network being trained based on the training data comprising a plurality of defective items in the historical maintenance data, the corresponding test values of each defective item, and the defect causes of each defective item.
The claim limitations in the abstract idea have been highlighted in bold above.
Under the step 1 of the eligibility analysis, it is determined whether the claims are drawn to a statutory category by considering whether the claimed subject matter fall within the four statutory categories of patentable subject matter identified by 35 U.S.C 101: process, machine, manufacture, or composition of matter. The above claim is considered to be in the statutory category of (process).
Under the step 2A, prong one, it is considered whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into groupings of subject matter when recited as such in a claim limitation, that cover mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and mental process – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, a step of
determining whether a product is defective based on the product data; (considered to be a mental process)
in response that the product is determined to be defective, outputting first warning information based on the number of defects of the product satisfying a first preset condition;(considered to be a mental process)
the first preset condition comprising the number of defects in a first preset period being greater than or equal to a first threshold, the number of defects of a defective item in the first preset period being greater than or equal to a second threshold, the number of machines outputting products with defects being greater than or equal to a third threshold, a rate of defects being greater than a mean value, and the number of defects in a manufacturing line being less than a fourth threshold; (considered to be a mathematical relationship and/or mental process)
obtaining a rate of defects of the product every first preset time period, and outputting second warning information based on the rate of defects of the product satisfying at least one of a second preset condition, a third preset condition, and a fourth preset condition; (considered to be a mathematical relationship and/or mental process)
the second preset condition comprising the rate of defects being greater than a control threshold within a second preset time period, the rate of defects being greater than a fifth threshold, the number of defects being greater than or equal to a sixth threshold, and the number of produced products being greater than or equal to a seventh threshold, the third preset condition comprising a consecutive eighth threshold number of points in a control chart of rate of defects exceeding a median, and the number of defects being greater than or equal to a ninth threshold, the fourth preset condition comprises a consecutive tenth threshold number of points raising in the control chart; (considered to be a mathematical relationship and/or mental process)
in response that any warning information is output, analyzing distribution of the defects of the product; (considered to be a mathematical relationship and/or mental process)
predicting at least one cause of each defect of the product according to historical maintenance data of the product and a self-learning record of the electronic device; (considered to be a mathematical relationship or mental process) and
outputting a plurality of defect causes and a probability of each defect cause through the prediction model of defect cause, the prediction model of defect cause being established by a convolutional neural network being trained based on the training data comprising a plurality of defective items in the historical maintenance data, the corresponding test values of each defective item, and the defect causes of each defective item (considered to be a mathematical relationship and/or mental process) and
These mental steps represent that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind.
Similar limitations comprise the abstract ideas of the independent claims 9 and 17.
Next, under the step 2A, prong two, it is considered whether the claim that recites a judicial exception is integrated into a practical application.
In this step, it is evaluated whether the claim recites meaningful additional elements that integrate the exception into a practical application of that exception.
In claim 1, the additional element is electronic device. The above additional element is recited in generality and represent extra solution activity to the judicial exception. The additional element in the preamble of “A method for monitoring ….” is not qualified for a meaningful limitation because it only generally links the use of the judicial exception to a particular technological environment or field of use. The additional elements/steps “obtaining product data in real time, inputting the defective item and corresponding test values…” are also recited in generality which seem to merely be gathering data, providing data for calculations and not really performing any kind of measurement to provide any meaningful additional element. Also, it represents an extra-solution activity to the judicial exception. All uses of judicial exception require it.
In claim 9, the additional elements/steps recite the similar additional elements/steps as of claim 1. The additional elements/steps (processor, storage device) are recited in generality and represent extra- solution activity to the judicial exception. The additional element in the preamble of “An electronic device comprising” is not qualified for a meaningful limitation because it only generally links the use of the judicial exception to a particular technological environment or field of use. The additional elements/steps “obtaining product data in real time, inputting the defective item and corresponding test values…” are also recited in generality which seem to merely be gathering data, providing data for calculations and not really performing any kind of measurement to provide any meaningful additional element. Also, it represents an extra-solution activity to the judicial exception. All uses of judicial exception require it.
In claim 17, the additional element is: a computer readable storage medium having instructions stored thereon, when the instructions are executed by a processor of an electronic device, the processor is configured to perform a method for monitoring products for defects. The above additional elements/steps (a generic computer equipment) are recited in generality and represent extra solution activity to the judicial exception. The additional element in the preamble of “A computer readable storage medium having instructions stored thereon, when the instructions are executed by a processor of an electronic device, the processor is configured to perform a method for monitoring products for defects” is not qualified for a meaningful limitation because it is only generally links the use of the judicial exception to a particular technology environment or field of use. The additional elements/steps “obtaining product data in real time, inputting the defective item and corresponding test values…” are also recited in generality which seem to merely be gathering data, providing data for calculations and not really performing any kind of measurement to provide any meaningful additional element. Also, it represents an extra-solution activity to the judicial exception. All uses of judicial exception require it. The storage medium and the program/instructions recited are not qualified as particular machines; a generic computer equipment that is well understood and conventional and is significantly insufficient.
In conclusion, the above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the step 2B.
Considering the claim as a whole, one of ordinary skill in the art would not know the practical application of the present invention since the claims do not apply or use the judicial exception in some meaningful way.
The independent claims, therefore, are not patent eligible.
With regards to the dependent claims, the claims 2-8, 9-16, 18-20 comprise the analogous subject matter and also comprise additional features/steps which are the part of an expanded abstract idea of the independent claim 1, 9 and 17 (additionally comprising mathematical relationship/mental process steps) and, therefore, the dependent claims are not eligible without additional elements that reflect a practical application and qualified for significantly more for substantially similar reason as discussed with regards to independent claims.
Examiner note:
There are no prior art rejections for independent claims 1, 9 and 17 for limitation : inputting the defective item and corresponding test values into a prediction model of defect cause of a global quality control system, and outputting a plurality of defect causes and a probability of each defect cause through the prediction model of defect cause, the prediction model of defect cause being established by a convolutional neural network being trained based on the training data comprising a plurality of defective items in the historical maintenance data, the corresponding test values of each defective item, and the defect causes of each defective item.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wang et al. (US 20200004219 A1) discusses defect rate analysis to reduce defects in manufacturing.
Kano et al. (US 20190360942 A1) discusses machine learning for each defect type using defective product data as teacher data.
Weiss et al. (US 20180300865 A1) discusses predicting manufacturing defects.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHARAD TIMILSINA whose telephone number is (571)272-7104. The examiner can normally be reached Monday-Friday 9:00-5:00.
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/SHARAD TIMILSINA/Examiner, Art Unit 2863
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2863