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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 2/2/2026 was in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS is being considered by the examiner.
Response to Amendment
Claims 1 and 13-14 are amended, claim 5 is cancelled, and claims 15-18 are new. Claims 1-4 and 6-18 are pending.
Response to Arguments
Applicant's arguments filed 11/24/2025 have been fully considered.
Applicant’s arguments regarding whether the amended claims overcome the rejections of independent claims 1 and 13-14, and all claims depending therefrom under 101 are unpersuasive for the following reasons.
On pages 11-14 of the response, Applicant contends that the claims do not recite a judicial exception under Step 2A Prong 1. In support, on page 13 of the response, Applicant contends that claim 1 is directed to a technological solution for estimating manufacturing abnormality causes that involve use of processing circuitry to acquire manufacturing condition data, compute index values representing the contribution of each condition to the abnormality, and calculate similarities between multi-dimensional data sets. Applicant further asserts that these tasks involve processing “large volumes” of manufacturing and state data, performing statistical analysis, and executing similarity computations that require significant computational resources and therefore cannot be practically performed in the human mind or as aided by pen-and-paper.
Examiner notes that Applicant’s arguments in this respect are generalized and do not specifically explain why any individual element or combination of elements, as recited in the claims, may not be performed as mental processes. Furthermore, Examiner submits that what may constitute a large volume of manufacturing and state data is unclear, and furthermore is unspecified by the current claims. Examiner acknowledges that the use of computer resources may enable a more time-efficient manner of implementing the processing of the data but submits that such efficiency advantage is largely immaterial regarding whether, in a broadest reasonable interpretation, the functions may be performed via mental processes or constitute implementation of mathematical concepts.
On pages 13-14 of the response, Applicant contends that claim 1 does not merely recite mathematical relationships, formulas, or calculations in the abstract, instead reciting a specific and practical process for identifying and analyzing abnormality causes by acquiring condition data, generating index values, and determining similarities between different manufacturing scenarios using algorithms implemented by processing circuitry. Examiner notes that this argument is generalized and does not specifically explain why any individual element or combination of elements, as recited in the claims, do not constitute application of a mathematical concept.
Regarding the elements found to fall within the mathematical concepts exception, Applicant further contends on page 14 that the recited steps are not claimed as mathematical concepts per se but as part of a specific and applied technological solution that improves the reliability and speed of root cause analysis. Examiner submits that Applicant’s argument does not directly address whether the claims “recite” elements that fall within the mathematical concepts exception, but instead appear to address whether the elements found to fall within the mathematical concepts exception are in some manner integrated into a practical solution (part of an overall technical solution), which is a distinct issue from whether the specifically recited functions “compute… a first index value…, and compute … a second index value” and “compute a similarity between the first index value and the second index value,” fall within the exception such that the claim “recites” a mathematical concept such as a mathematical relation. The function of computing, based on the first factor data, a first index value relating to a degree by which each of the first manufacturing conditions contributes to an abnormality cause of the first product, and computing, based on the second factor data, a second index value relating to a degree by which each of the second manufacturing conditions contributes to an abnormality cause of the second product is determined to fall within the mathematical relations sub-category because as disclosed in Applicant’s specification (e.g., paragraph [0062]) the first and second indexes are computed using a statistical hypothesis test, which is fundamentally characterized by mathematical calculations/relations. Therefore, the function of computing the indexes do not merely involve a mathematical concept in some underlying manner, but are instead actually implemented by mathematical calculations/relation. Similarly, computing a similarity between the first index value and the second index value is determined to fall within the mathematical relations sub-category because as disclosed in Applicant’s specification (e.g., paragraph [0043]) the similarity between the first and second indexes is computed in terms of a mathematical distance, which is obtained via mathematical calculations/relations.
On pages 14-18 of the response, Applicant contends that additional elements integrate the judicial exception into a practical application. On pages 15-16, Applicant asserts that such integration is obtained due to Applicant’s claims reciting additional elements that amount to a technological improvement in computer functionality and the technical field of manufacturing processing analysis and abnormality cause estimation. In support, Applicant asserts on page 16 that the invention improves accuracy and speed of root cause analysis by enabling identification of past cases having similar manufacturing causes, and on page 17 that the invention improves ability to correct the first and second conditions so that only abnormal products are included in the analysis. In further support, on page 18 Applicant contends that the improvement is realized in terms of enabling faster and more accurate identification of abnormality causes and that considered as a whole claim 1 recites a particular manner of improving the technological field of manufacturing data analysis and abnormality cause estimation.
Examiner acknowledges that the method recited in Applicant’s claims may have utility as used in various manufacturing equipment monitoring applications. However, Examiner notes that Applicant’s arguments do not convey a manner in which one or more additional elements are combined with the elements falling within the judicial exception in some particularized manner that constitutes an improvement to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), or in a manner that applies the judicial exception with, or by use of, a particular machine (MPEP 2106.05(b)). The Examiner submits that the utility described by Applicant’s arguments is realized solely in terms of using conventional computer processing means for implementing a series of steps that fall within the mental processes and/or mathematical concepts exceptions such that the combination of additional elements (computer processing) with the elements falling within the exception does not constitute an improvement to any particular machine or technical field or to use of a particular machine by the functional elements.
On page 19, and regarding Step 2B, Applicant contends that claim 1 recites specific technical improvements that go beyond merely implementing an abstract idea on generic computer components. In support, Applicant asserts on page 19 that “the claimed invention introduces a hardware-implemented process” that implements the functions recited in claim 1.
Examiner submits that the hardware implementation recited in Applicant’s claims does not appear particularized in any significant functional manner with regard to either data collection or the processing of the collected data.
On pages 19-20, Applicant explains that differences between the claimed methodology and convention approaches results in more accurate results that has downstream manufacturing benefits in terms of improving yield.
Examiner submits that Applicant’s arguments in this regard are confined to speculative characterizations of potential processing results and do not explain the manner in which additional elements that are recited in the claim result in the claim as a whole amounting to significantly more than the abstract idea represented by the processor-implemented algorithm.
For the foregoing reasons, the rejections of claims under 101 are maintained.
Regarding the rejections of independent claims 1 and 13-14 under 103 as unpatentable over Cheng (US 2017/0123411 A1) in view of Harada (US 2020/0243204 A1), Harada ‘204, Examiner acknowledges that the amendments to claims 1 and 13-14 overcome the grounds of rejection. In view of further search and analysis, new grounds for rejecting claims 1 and 13-14 under 103 as unpatentable over Cheng in view of Harada ‘204, and in further view of Wang (US 2024/0193460 A1), Wang ‘460, and Watanabe (US 2021/0134032 A1) are set forth herein.
Claim Objections
Claims 6-7 are objected to because of the following informalities:
Each of claims 6 and 7 currently depends from claim 5, which has been cancelled. It appears, based on previous dependency of claim 5 to claim 1 that claims 6 and 7 should depend from claim 1.
Appropriate correction is required.
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 1-4 and 6-18 are rejected under 35 U.S.C. 101 because the claimed invention in each of these claims is directed to the abstract idea judicial exception without significantly more.
Claim 1, substantially representative also of independent claims 13-14 recites:
“[a] data analysis apparatus comprising processing circuitry configured to:
designate a first condition indicative of a first product of an analysis target;
designate a second condition indicative of a second product of a comparison target;
acquire first state data indicative of a state of the first product, based on the first condition, and acquire second state data indicative of a state of the second product, based on the second condition;
detect an abnormal state of the first product, based on the first state data, correct the first condition in such a manner as to indicate the first product in the detected abnormal state, detect an abnormal state of the second product, based on the second state data, and correct the second condition in such a manner as to indicate the second product in the detected abnormal state;
acquire, based on the corrected first condition, first factor data indicative of a plurality of first manufacturing conditions of the first product, and acquire, based on the corrected second condition, second factor data indicative of a plurality of second manufacturing conditions of the second product;
compute, based on the first factor data, a first index value relating to a degree by which each of the first manufacturing conditions contributes to an abnormality cause of the first product, and compute, based on the second factor data, a second index value relating to a degree by which each of the second manufacturing conditions contributes to an abnormality cause of the second product; and
compute a similarity between the first index value and the second index value, wherein
the first state data and the second state data include at least one of information relating to quality control (QC) of a product and/or information relating to an inspection result of the product,
the first index value indicates a first bias rate of a respective one of the first manufacturing conditions, and
the second index value indicates a second bias rate of a respective one of the second manufacturing conditions.”
The claim limitations considered to fall within in the abstract idea are highlighted in bold font above and the remaining features are “additional elements.”
Step 1 of the subject matter eligibility analysis entails determining whether the claimed subject matter falls within one of the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. Claims 1 and 14 each recite an apparatus and claim 13 recites a method, and therefore each falls within a statutory category.
Step 2A, Prong One of the analysis entails determining whether the claim recites a judicial exception such as an abstract idea. Under a broadest reasonable interpretation, the highlighted portions of claim 1 fall within the abstract idea judicial exception. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, the highlighted subject matter falls within the mental processes category (including an observation, evaluation, judgment, opinion) and the mathematical concepts category (mathematical relationships, mathematical formulas or equations, mathematical calculations). MPEP § 2106.04(a)(2).
The recited functions:
“designate a first condition indicative of a first product of an analysis target;
designate a second condition indicative of a second product of a comparison target,”
acquire first state data indicative of a state of the first product, “based on the first condition,” and acquire second state data indicative of a state of the second product, “based on the second condition;”
detect an abnormal state of the first product, based on the first state data,”
“detect an abnormal state of the second product, based on the second state data,”
acquire, “based on the corrected first condition,” first factor data indicative of a plurality of first manufacturing conditions of the first product, and acquire, “based on the corrected second condition,” second factor data indicative of a plurality of second manufacturing conditions of the second product,
“compute, based on the first factor data, a first index value relating to a degree by which each of the first manufacturing conditions contributes to an abnormality cause of the first product, and compute, based on the second factor data, a second index value relating to a degree by which each of the second manufacturing conditions contributes to an abnormality cause of the second product; and
compute a similarity between the first index value and the second index value, wherein
the first state data and the second state data include at least one of information relating to quality control (QC) of a product and/or information relating to an inspection result of the product,
the first index value indicates a first bias rate of a respective one of the first manufacturing conditions, and
the second index value indicates a second bias rate of a respective one of the second manufacturing conditions.”
may be performed as mental processes.
In a broadest reasonable interpretation in view of Applicant’s specification in which a condition designation may entail a product identifier itself, designating a first condition indicative of a first product and designating a second condition indicative of a second product, may be performed via mental processes (e.g., judgment or opinion in selectively ascertaining product categories such as by product ID, lot ID, or other information associated with the products (e.g., information categorizing the first and second products as abnormal) such as to be considered for further processing).
Acquiring first state data indicative of a state of the first product, “based on the first condition,” and acquire second state data indicative of a state of the second product, “based on the second condition,” acquiring, “based on the corrected first condition,” first factor data, and acquiring, “based on the corrected second condition,” second factor data (i.e., determining to acquire in accordance with a first or second condition), may be performed via mental processes (e.g., judgement of condition under which first/second factor data is to be collected). Detecting an abnormal state of the first product, based on the first state data and detecting an abnormal state of the second product, based on the second state data can be performed via mental processes (e.g., evaluation of state data and judgement).
Computing, based on the first factor data, a first index value relating to a degree by which each of the first manufacturing conditions contributes to an abnormality cause of the first product, and computing, based on the second factor data, a second index value relating to a degree by which each of the second manufacturing conditions contributes to an abnormality cause of the second product may be performed via mental processes (e.g., evaluation of first and second factor data to determine via judgement/opinion the first and second index values each of which relates to a degree by which the respective manufacturing conditions contributed to abnormality causes). Computing a similarity between the first index value and the second index value may also be performed via mental processes (e.g., evaluation of first and second indexes to determine via judgement/opinion a similarity/dissimilarity). Examiner notes the characterization of the first and second state data and the first and second indexes does not change the character of the functions that generate and/or process such data as falling within the judicial exception.
The recited functions:
“compute, based on the first factor data, a first index value relating to a degree by which each of the first manufacturing conditions contributes to an abnormality cause of the first product, and compute, based on the second factor data, a second index value relating to a degree by which each of the second manufacturing conditions contributes to an abnormality cause of the second product; and compute a similarity between the first index value and the second index value,” in claim 1 are further determined by the Examiner as falling within the mathematical relationships sub-category of mathematical concepts (MPEP 2106.04(a)(2)). Computing, based on the first factor data, a first index value relating to a degree by which each of the first manufacturing conditions contributes to an abnormality cause of the first product, and computing, based on the second factor data, a second index value relating to a degree by which each of the second manufacturing conditions contributes to an abnormality cause of the second product is determined to fall within the mathematical relations sub-category because as disclosed in Applicant’s specification (e.g., paragraph [0062]) the first and second indexes are computed using a statistical hypothesis test, which is fundamentally characterized by mathematical calculations/relations. Computing a similarity between the first index value and the second index value is determined to fall within the mathematical relations sub-category because as disclosed in Applicant’s specification (e.g., paragraph [0043]) the similarity between the first and second indexes is computed in terms of a mathematical distance, which is obtained via mathematical calculations/relations.
Step 2A, Prong Two of the analysis entails determining whether the claim includes additional elements that integrate the recited judicial exception into a practical application. “A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception” (MPEP § 2106.04(d)).
MPEP § 2106.04(d) sets forth considerations to be applied in Step 2A, Prong Two for determining whether or not a claim integrates a judicial exception into a practical application. Based on the individual and collective limitations of claim 1 and applying a broadest reasonable interpretation, the most applicable of such considerations appear to include: improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)); applying the judicial exception with, or by use of, a particular machine (MPEP 2106.05(b)); and effecting a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)).
Regarding improvements to the functioning of a computer or other technology, none of the “additional elements” including “processing circuitry configured to” implement the recited functions, “acquire first state data indicative of a state of the first product,” “acquire second state data indicative of a state of the second product,” “correct the first condition in such a manner as to indicate the first product in the detected abnormal state,” “correct the second condition in such a manner as to indicate the second product in the detected abnormal state,” “acquire,” “first factor data indicative of a plurality of first manufacturing conditions of the first product, and “acquire,” “second factor data indicative of a plurality of second manufacturing conditions of the second product,” in any combination appear to integrate the abstract idea in a manner that technologically improves any aspect of a device or system that may be used to implement the highlighted step or a device for implementing the highlighted step such as a generic computer. Instead, “processing circuitry configured to” implement the recited functions entails conventional data processing components (e.g., circuitry of a computer processor) configured to execute instructions for implementing the functions falling within the judicial exception, which constitutes insignificant extra solution activity. The functions , “acquire first state data indicative of a state of the first product,” “acquire second state data indicative of a state of the second product,” “acquire,” “first factor data indicative of a plurality of first manufacturing conditions of the first product, and acquire, based on the second condition, second factor data indicative of a plurality of second manufacturing conditions of the second product,” entails high-level data collection for providing the input data to the processing functions, that fall within the judicial exception, and therefore also constitute extra solution activity. Correcting the first and second conditions in a manner to indicate the first and second products are in the detected abnormal state entails routine, conventional data processing activity (updating data to reflect a change or new data), and therefore constitutes extra solution activity that fails to integrate the judicial exception into a practical application.
Regarding application of the judicial exception with, or by use of, a particular machine, the additional elements are configured and implemented in a general rather than a particularized manner of implementing monitoring and analyzing manufacturing products and processes. Claim 1 does not even establish a particular technological environment or field-of-use relevant to the data analysis, since it does not in any way identify any of the products, the analysis targets, the data, the states and conditions, etc., which are broadly recited in the claim. Instead, the claim seeks to monopolize the recited mental process itself over a wide range of technological environments and fields-of-use.
Regarding a transformation or reduction of a particular article to a different state or thing, claim 1 does not include any such transformation or reduction. Instead, claim 1 as a whole entails identifying sources of input information (designating conditions indicative of first and second products), acquiring the input information (factor data indicative of manufacturing conditions of the first and second products), and applying standard processing techniques (processing circuitry) to the information to implement the functions falling within the judicial exception (e.g., computing first and second indexes relating to a degree by which the manufacturing conditions contribute to a product abnormality and computing a similarity between the first and second index data) with the additional elements failing to provide a meaningful integration of the judicial exception in an application that transforms an article to a different state. Instead, the additional elements represent extra-solution activity that does not integrate the judicial exception into a practical application. In view of the various considerations encompassed by the Step 2A, Prong Two analysis, claim 1 does not include additional elements that integrate the recited abstract idea into a practical application.
Therefore, claim 1 is directed to a judicial exception and requires further analysis under Step 2B.
Regarding Step 2B, and as explained in the Step 2A Prong Two analysis, the additional elements in claim 1 constitute extra-solution activity such that the additional elements do not result in the claim as a whole amounting to significantly more than the judicial exception. Furthermore, the additional elements appear to be generic and well understood as evidenced by the disclosures of Cheng (US 2017/0123411 A1) and Harada (US 2020/0243204 A1). As set forth in the grounds for rejecting claim 1, Cheng teaches “processing circuitry configured to” implement the recited functions, and the function “acquire, based on the first condition, first factor data indicative of a plurality of first manufacturing conditions of the first product, and acquire, based on the second condition, second factor data indicative of a plurality of second manufacturing conditions of the second product.” Similarly, Harada teaches processing circuitry for implementing manufacturing process data collection and processing (FIG. 1 system 1; [0050], [0075] disclosed functions including data acquisition implemented via processor; [0018] functions include collecting manufacturing conditions data; FIG. 2B manufacture condition information collected in association with product ID).
Therefore, the additional elements are insufficient to amount to significantly more than the judicial exception.
Claim 1 is therefore not patent eligible.
Independent claims 13 and 14 include substantially the same elements falling within a judicial exception as claim 1 and neither includes additional elements that integrate the judicial exception into a practical application or result in the claim as a whole amounting to significantly more than the judicial exception.
Claims 13 and 14 are therefore also not patent eligible.
Claims 2-4, 6-12, and 15-18 depending from claim 1, provide additional features/steps which are part of an expanded algorithm that includes the abstract idea of claim 1 (Step 2A, Prong One). None of dependent claims 2-4, 6-12, and 15-18 recite additional elements that integrate the abstract idea into practical application (Step 2A, Prong Two), and all fail the “significantly more” test under the step 2B for substantially similar reasons as discussed with regards to claim 1.
In Claim 2, “designate one or more second conditions different from the first condition” falls within the mental processes exception for substantially the same reasons and the “designate” elements in claim 1. Regarding “acquire the second factor data in regard to each of the second conditions,” the acquiring itself entails high-level data collecting and therefore constitutes extra-solution that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception. The condition of acquisition (in regard to each of the second conditions) falls within the mental processes exception because determining to acquire the data in relation to one or more conditions may be performed via mental processes (e.g., judgement). The elements “compute the second index value in regard to each of the second factor data” and “compute the similarity in regard to each of the second index values” each falls within the mental processes exception and mathematical exception for the same reasons as set forth for the “compute” a first/second index value and “compute a similarity” elements in claim 1.
In claim 3, the element compute the first index value, based on the first factor data “and a statistical hypothesis test,” and to compute the second index value, based on the second factor data “and the statistical hypothesis test” falls within the mental processes exception and mathematical concepts exception for the reasons set forth in the grounds for rejecting claim 1.
Claim 4 further recites that the first and second index values are computed using “a trained model that is trained to output index values” based on input factor data, which entails routine, conventional computer processing in terms of instructions, prepared by training, to effectuate the judicial exception, and therefore constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
In claim 6, detecting the abnormal state of the first and second products by a statistical process falls within the mental processes exception because it may be performed via mental processes (e.g., via pen-and-paper calculation constituting a statistical process such as average, deviation, etc.). This function further falls within the mathematical relations sub-category of the mathematical relations exception because statistical processing is fundamentally characterized by mathematical relations and calculations.
In claim 7, using a trained machine learning model to detect the abnormal state of the first and second products entails conventional computer processing techniques (machine learning which is customarily trained) to produce instructions for implementing the judicial exception (detection of an abnormality), and therefore constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Claim 8 further recites “a memory” for storing and correlating the second condition and the second index value, and further that the processing circuitry is configured to acquire the second index value from the memory based on the second condition, which entails conventional, routine computer processing functions (storage and data associations enabling memory access to data), and therefore constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Claim 9 further recites “acquire the computed similarity and to output the similarity and the second condition,” which entails conventional, routine computer processing functions (acquiring and outputting data inherent to processing functions), and therefore constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Claim 10 further recites “acquire information relating to the second condition, and to output the acquired information, the similarity and the second condition,” which entails conventional, routine computer processing functions (acquiring and outputting data inherent to processing functions), and therefore constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
In claim 11, “wherein the statistical hypothesis test is a G-test” falls within the mathematical relations subcategory of the mathematical concepts exception because a G-test is fundamentally characterized by mathematical relations and calculations.
In claim 12, “wherein the statistical hypothesis test is a chi-square test” falls within the mathematical relations subcategory of the mathematical concepts exception because a chi-square test is fundamentally characterized by mathematical relations and calculations.
Claims 15-18 further characterize the data (first and second state data and first and second bias rates) generated and/or processed by the functions falling within the judicial exception and therefore also fall within the judicial exception.
Dependent claims 2-4, 6-12 and 15-18 therefore also constitute ineligible subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4, 8-10, and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Cheng (US 2017/0123411 A1) in view of Harada (US 2020/0243204 A1), Harada ‘204 and in further view of Wang (US 2024/0193460 A1), Wang ‘460, and Watanabe (US 2021/0134032 A1).
As to claims 1, 13, and 14 Cheng teaches “[a] data analysis apparatus ([0009] system for analyzing variation causes for manufacturing process; FIG. 2 depicting system 200) comprising processing circuitry ([0041] components of system 200 including evaluation module 220 and comparison module 240 implemented by circuitry such as ASIC and/or processor),” “a data analysis method ([0009] analyzing variation causes for manufacturing process; method implemented by FIG. 2 system 200),” and a “non-transitory computer readable storage medium (FIG. 2 storage module 260; [0041] components of system 200 implemented by conventional computer processor (i.e., processor executing software/firmware). Examiner notes that software processing via processor inherently entails storage devices for storing data and instructions for processing) including computer executable instructions ([0041] processor for implementing functions inherently entails instructions and software/firmware inherently entail instructions), wherein the instructions, when executed by a processor, cause the processor to perform a method including
“designate a first condition indicative of a first product of an analysis target ([0028] collecting module 210 collects manufacturing process data for multiple products in which the collection associates blocks for each product with manufacturing process parameters; [0029]-[0032] collection of manufacturing process data entails designating particular products i = 1 (identification of product for which to collect data is the “condition” as per broadest reasonable interpretation in view of Applicant’s specification) for which manufacturing processing data is collected; Table 1 PID (1,1) (1,2) (1,3) (1,4) for product 1 (Examiner notes that collection of data for a product inherently requires designation/selection of a product identifier in some manner); [0029]-[0032] additional condition designation in terms of product-associated manufacturing process data as having a particular quality check result, [0029] quality check result Z1 for product i=1 );
designate a second condition indicative of a second product of a” “target ([0028] collecting module 210 collects manufacturing process data for multiple products in which the collection associates blocks for each product with manufacturing process parameters; [0029]-[0032] collection of manufacturing process data entails designating particular products i = 2, 3, etc., (identification of product for which to collect data is the condition as per broadest reasonable interpretation in view of Applicant’s specification) for which manufacturing processing data is collected; Table 1 PID (2,1) (2,2) (2,3) (2,4) for product 2; [0029]-[0032] additional condition designation in terms of product-associated manufacturing process data as having a particular quality check result, [0029] any one of quality check result Z2, Z3 … Zn for product i=2, 3 … n);
acquire first state data indicative of a state of the first product (Cheng: [0028]-[0029] and [0031] collecting module acquires manufacture process data for a plurality of products that include a product quality parameter such as for product i = 1), based on the first condition (Cheng: [0029] manufacture process data acquired in association with (based on) a product identifier (i = 1). Examiner notes that collecting data from a particular product inherently requires identification of the product.), and acquire second state data indicative of a state of the second product (Cheng: [0028]-[0029] and [0031] collecting module acquires manufacture process data for a plurality of products that include a product quality parameter such as for any of products i = 2, 3, etc.), based on the second condition (Cheng: [0029] manufacture process data acquired in association with (based on) a product identifier (i = 2, 3, etc.). Examiner notes that collecting data from a particular product inherently requires identification of the product.);
detect an abnormal state of the first product, based on the first state data (Cheng: [0029] and Table 1 depicting manufacture process data corresponding a determined quality check result Z that may indicate normal or defective for products and/or sub-products (blocks) (Table 1 PID (2,1)), correct the first condition in such a manner as to indicate the first product in the detected abnormal state (Cheng: Table 1 “Defective” designator included in association with (as a “correction” to) PID (2,1)), detect an abnormal state of the second product, based on the second state data (Cheng: [0029] and Table 1 depicting manufacture process data corresponding a determined quality check result Z that may indicate normal or defective for products and/or sub-products (blocks) (Table 1 PID (2,2)), and correct the second condition in such a manner as to indicate the second product in the detected abnormal state (Cheng: Table 1 “Defective” designator included in association with (as a “correction” to) PID (2,2));
acquire, based on the” “first condition, first factor data ([0029] manufacture process data acquired in association with (based on) a product identifier (i = 1) and/or quality check result Z1) indicative of a plurality of first manufacturing conditions of the first product ([0028]-[0029] manufacture process data including manufacturing process parameters (conditions) acquired for product corresponding (i=1) to quality check result Z1), and acquire, based on the" “second condition, second factor data ([0029] manufacture process data including manufacturing parameters (conditions) are collected and are associated with (based on) a product identifier (i = 2, 3, etc.) and/or any one of quality check result Z2, Z3 … Zn) indicative of a plurality of second manufacturing conditions of the second product ([0028]-[0029] manufacture process data including manufacturing process parameters (conditions) acquired for product corresponding (i=2,3…n) to quality check result Z2, Z3 … Zn);
compute, based on the first factor data, a first index value ([0008] and [0034] compute/process manufacturing process data (e.g., manufacturing process data corresponding to Z1 in [0029]) to determine a contribution rate for each of the manufacturing process parameters) relating to a degree by which each of the first manufacturing conditions contributes to an abnormality cause of the first product (per [0021] and [0036] the contribution rates (rate is a form of “degree”) are utilized to determine cause of product defect), and compute, based on the second factor data, a second index value ([0008] and [0034] compute/process manufacturing process data (e.g., manufacturing process data corresponding to any one of Z2 to Zn in [0029]) to determine a contribution rate for each of the manufacturing process parameters) relating to a degree by which each of the second manufacturing conditions contributes to an abnormality cause of the second product (per [0021] and [0036] the contribution rates (rate is a form of “degree”) are utilized to determine cause of product defect)”
“wherein
the first state data and the second state data include at least one of information relating to quality control (QC) of a product ([0028]-[0029] manufacturing process data collected in accordance with product ID includes product quality data. Examiner notes that as applied in Cheng’s method for correlating with manufacturing processes to determine source of defective products the product quality data relates to quality control).”
Cheng further teaches a comparative analysis performed with respect to the contributions rates (indexes) ([0009] and [0036] comparison module deletes manufacturing process parameter having lowest contribution rate among the manufacturing process parameters), but does not appear to expressly teach a direct comparative analysis between a first part and a second product of “comparison target” or “compute a similarity between the first index value and the second index value.”
Harada ‘204 discloses a method/apparatus for analyzing manufacturing process information (Abstract; [0018]; FIG. 1) that includes collecting manufacturing condition data for multiple specified products ([0105]-[0107] environmental and processing data collected for different parts) and further includes comparing and computing a similarity between the collected data for the respective different parts (parts from different lots) ([0125]-[0126] in the comparison process, the characteristic/indicator data of the extracted lot (first product analysis target) of each production condition related to the lot of the part is compared against the characteristic data of other lots (second product comparison target) of each production condition and features of commonality (similarity) are extracted for each production condition lot and the commonality (similarity) in the extracted features is then determined).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Harada ‘204 teaching of comparing manufacturing condition data between different products or parts of products to compute a similarity to the apparatus/method taught by Cheng in which the manufacturing condition data includes defect contribution rate information, such that in combination the apparatus/method is configured to “compute a similarity between the first index value and the second index value.”
The motivation for such combination would have been to expand the scope of information relevant to potential causes of product defect/abnormality by deriving similarity patterns between manufacturing conditions including data indicating degree of abnormality contribution associated with the manufacturing condition data between different products/parts to account for variations and similarity patterns that may occur between different products/parts as suggested by Harada ‘204.
Cheng does not appear to teach acquiring the manufacture process data based on/in accordance with a data acquisition condition (e.g., a particular product ID and/or further condition data) indicating a state of the product (e.g., that the first and second products are abnormal) and therefore does not teach acquire, based on the “corrected” first condition, first factor data, and acquire, based on the “corrected” second condition, second factor data.
Wang ‘460 discloses a method/apparatus for collecting and processing product condition information to detect and diagnose product abnormalities (Abstract; FIGS. 1-2) that includes acquiring the manufacture process data ([0010] screening sample data based on filtering (i.e., selective acquisition of sample data from a larger set of sample data); per [0004] sample data includes characteristic data; per [0013] characteristic data includes production equipment, environmental parameters; [0054]-[0055] sample data acquired in a limited manner (screened) for further processing such as by machine learning) based on a data acquisition condition ([0010]-[0011] filtering threshold) indicating a state of the product abnormality ([0011] filtering threshold may be abnormal ratio threshold; [0054] explaining problem with machine learning processing of too many samples such as having low abnormality ratio).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Wang ‘460 teaching of acquiring the manufacture process data based on a data acquisition condition indicating a state of the product abnormality to the apparatus/method taught by Cheng as modified by Harada ‘204, in which manufacture condition information for first and second products are analyzed and in which product abnormalities are identified as associated with product ID information (“correction” to include the abnormality status), such that in combination the apparatus is configured to acquire the first factor data, based on the corrected first condition, and to acquire the second factor data, based on the corrected second condition.
The motivation would have been to optimize the manufacturing condition data sample set in accordance with levels of abnormality conditions of the products, resulting in a more efficient and effective diagnostic processing as disclosed by Wang ‘460.
None of Cheng, Harada ‘204, and Wang ‘460 appear to teach that a values indicating a degree to which a manufacturing condition causes an abnormality indicates a bias rate (e.g., rate of occurrence of a condition) and therefore does not teach “the first index value indicates a first bias rate of a respective one of the first manufacturing conditions, and the second index value indicates a second bias rate of a respective one of the second manufacturing conditions.”
Watanabe discloses a method/apparatus for analyzing manufacturing data for products (Abstract; FIG. 1) that uses bias rate information for monitoring manufacturing product abnormalities ([0079]-[0081] index value representing degree of abnormality is biased to a particular manufacturing condition in terms of probability the condition with cause an abnormality; FIGS. 5-6 depicting frequency relation between manufacturing data (product data) versus manufacturing conditions; [0068] the manufacturing conditions may be the number of products used in the analysis such that the data in FIG. 5 conveys a frequency of occurrence over a number of products).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Watanabe’s teaching of using indexing data that reflects/indicates a bias rate of manufacturing condition contribution to abnormalities to the apparatus/method taught by Cheng as modified by Harada ‘204 and Wang ‘460, which teaches using a parallel, comparative processing between two indexing values such that in combination the apparatus is configured such that “the first index value indicates a first bias rate of a respective one of the first manufacturing conditions, and the second index value indicates a second bias rate of a respective one of the second manufacturing conditions.”
The motivation would have been to generate and use an index value that incorporates probability in terms of number/rate of instances of conditions contributing to abnormality to identify and utilize manufacturing condition information that is more relevant to determining the source of abnormalities such that processing of such data provides more useful/accurate abnormality diagnostic results as suggested by Watanabe.
As to claim 2, the combination of Cheng, Harada ‘204, Wang ‘460, and Watanabe teaches “[t]he data analysis apparatus of Claim 1, wherein the processing circuitry is configured to:
designate one or more second conditions different from the first condition (Cheng: [0029]-[0032] collection of manufacturing process data entails designating particular products i = 2, 3, etc. different than first condition (product designation i = 1) for which manufacturing processing data is collected; Table 1 PID (2,1) (2,2) (2,3) (2,4) for product 2 different that PID (1,1) (1,2) (1,3) (1,4); [0029]-[0032] additional condition designation in terms of product-associated manufacturing process data as having a particular quality check result, [0029] any one of quality check result Z2, Z3 … Zn for product i=2, 3 … n different that condition designation Z1 for product i = 1);
acquire the second factor data in regard to each of the second conditions (Cheng: [0029]-[0032] including Table 1 - manufacture process data collected in association with product IDs as evidenced by collection);
compute the second index value in regard to each of the second factor data (Cheng: [0008] and [0034] compute/process manufacturing process data (e.g., manufacturing process data corresponding to any one of Z2 to Zn in [0029]) to determine a contribution rate for each of the manufacturing process parameters); and
compute the similarity in regard to each of the second index values (combination of Cheng and Harada ‘204 set forth in grounds for rejecting claim 1 results in the similarity value being computed using the second index values).”
As to claim 4, the combination of Cheng Harada ‘204, Wang ‘460, and Watanabe teaches “[t]he data analysis apparatus of Claim 1, wherein the processing circuitry is configured to compute the first index value and the second index value by using a trained model that is trained to output index values, based on factor data that is input (Cheng: [0034] evaluation module uses a classifier (model) to determine contribution rates based on manufacturing process parameter data; [0049] classifier is trained based on manufacture process data).”
As to claim 8, the combination of Cheng, Harada ‘204, Wang ‘460, and Watanabe teaches “[t]he data analysis apparatus of Claim 1, further comprising a memory in which the second condition and the second index value are correlated and stored (Cheng: [0036] describing processing by comparison module 240, which per [0041] may be implemented via computer processor that inherently entails memory for storing processing data/instructions, including a comparative determination of the contribution rates and deleting corresponding manufacturing process parameters that per Tables 1 and 2 correspond to the “first condition” and “second condition” (i.e., the product IDs and the Z values). In this manner, Examiner notes that the contribution rate (index value) is correlated via at least the manufacturing process parameters with the product ID.).”
Cheng does not teach the details of data access from memory and therefore does not explicitly teach that the processing circuity may use the second condition (product ID used in association with collected manufacturing condition data) to acquire the second index value (contribution rate corresponding to the manufacturing condition data associated with the product ID) from memory and therefore does not expressly teach “wherein the processing circuitry is configured to acquire the second index value from the memory, based on the second condition.”
As set forth in the grounds for rejecting claim 1, Cheng teaches that the process of obtaining manufacturing data for ID-specified products and determining contribution rates (indexes) based on the product ID-associated manufacturing data using computer processing in which the information is stored and therefore is inherently retrieved from memory (FIG. 2 storage module 260, [0041]). Cheng further teaches determining the contribution rates in association with respective manufacture process parameters (Table 5 Contribution rates associated with manufacture process parameters X1, X2, X3, X4, and X5 that per Tables 1-4 are associated with product ID) via processing that per [0041] may be implemented via conventional computer processing that would inherently entail memory storage and access for data retrieval.
In the foregoing manner, the system disclosed by Cheng includes a processing system that is configured to (capable of) accessing/acquiring the second index value (contribution rate corresponding to products identifiers PIDs (2,1) (2,2) (2,3) (2,4)) from the memory, based on the second condition (information indexing of the PIDs to manufacturing process parameters X1 X2 … X6 and indexing of the manufacturing process parameters to the contribution rates such as in Table 5).
Even if the evident capability of access/acquiring the contribution rates from memory based on the product and product block IDs were found not to actually disclose a system “configured to” perform such operation, it would have been obvious to one of ordinary skill in the art before the effective filing date, in view of Cheng’s teaching of multi-level indexing/associating of the product ID, the manufacture process parameters, and the corresponding contribution rates to have configured the system such that the product ID may be used to access/acquire the contribution rate data from memory.
The motivation would have been to leverage the specifically indexed/organized data structures disclosed by Cheng to enable fast and efficient access to data for consequently fast and efficient access to and processing of the contribution rate information such as for subsequent processing steps in which the contribution rates themselves as processed.
As to claim 9, the combination of Cheng, Harada ‘204, Wang ‘460, and Watanabe teaches “[t]he data analysis apparatus of Claim 1, wherein the processing circuitry is configured to acquire the computed similarity (Cheng as combined with Harada as set forth in the grounds for rejecting claim 1 in which similarity is computed (i.e., similarity acquired by processing functions)) and to output the similarity (Cheng as combined with Harada as set forth in the grounds for rejecting claim 1 in which similarity is computed and therefore inherently output as the processing result) and the second condition (Cheng: Tables 1 and 2 depicting processing results in terms of product ID (first and second conditions) associated with corresponding manufacturing process parameters that are generated/output).”
As to claim 10, the combination of Cheng, Harada ‘204, Wang ‘460, and Watanabe teaches “[t]he data analysis apparatus of Claim 9, wherein the processing circuitry is configured to acquire information relating to the second condition (Cheng: Tables 1 and 2 depicting manufacturing process parameter information (X1, X2, etc.) associated with respective product IDs (first and second conditions)), and to output the acquired information (Cheng: Tables 1 and 2 depicting manufacturing process parameter information (X1, X2, etc.) that has been generated/output via the processing function of acquisition of the information), the similarity (Cheng as combined with Harada as set forth in the grounds for rejecting claim 1 in which similarity is computed and therefore inherently output as the processing result) and the second condition (Cheng: Tables 1 and 2 depicting processing results in terms of product ID (first and second conditions) associated with corresponding manufacturing process parameters that are generated/output).”
As to claim 15, the combination of Cheng, Harada ‘204, Wang ‘460, and Watanabe teaches “[t]he data analysis apparatus of Claim 1,” and Watanabe further teaches that the determined states for products used for monitoring manufacturing conditions may include dimensions and electrical characteristics of the product ([0044] and [0112]),
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Watanabe’s teaching of using state data including dimensions and electrical characteristics of products for monitoring manufacturing conditions to the apparatus taught by Cheng as modified by Harada ‘204, Wang ‘460, and Watanabe, such that in combination the apparatus is configured such that “the first state data and the second state data include dimensions and electrical characteristics of the product .”
The motivation would have been to utilized information relevant to product quality and that may relate to corresponding manufacturing processes as disclosed by Watanabe to accurately identify abnormal products to be used for abnormality diagnostics.
As to claim 16, the combination of Cheng, Harada ‘204, Wang ‘460, and Watanabe teaches “[t]he data analysis apparatus of Claim 2, wherein the one or more second conditions are a plurality of second conditions (Cheng: [0029]-[0032] collection of manufacturing process data entails designating particular products i = 2, 3, etc. different than first condition (product designation i = 1) for which manufacturing processing data is collected; Table 1 PID (2,1) (2,2) (2,3) (2,4) for product 2 different that PID (1,1) (1,2) (1,3) (1,4); [0029]-[0032] additional condition designation in terms of product-associated manufacturing process data as having a particular quality check result, [0029] any one of quality check result Z2, Z3 … Zn for product i=2, 3 … n different that condition designation Z1 for product i = 1).”
As to clam 17, the combination of Cheng, Harada ‘204, Wang ‘460, and Watanabe teaches “[t]he data analysis apparatus of Claim 1,” and Watanabe further teaches that the bias rate may indicate a ratio of an element/factor of a number of the products evaluated (FIG. 6 G(Y) table indicating abnormality rate for 1000 products; FIG. 10 number of abnormalities per number of device-specific products determined as a form of index).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Watanabe’s teaching of an index value that indicates a degree of abnormality contribution that indicates/reflects a bias rate that indicates a ratio of an element/factor of a number of the products evaluated to the apparatus taught by Cheng as modified by Harada ‘204, Wang ‘460, and Watanabe which teaches using a parallel, comparative processing between two indexing values such that in combination the apparatus is configured such that “the first bias rate indicates a ratio of an element of the first manufacturing condition with respect to a number of the first product designated by the first condition, and the second bias rate indicates a ratio of an element of the second manufacturing condition with respect to a number of the second product designated by the second condition.”
The motivation would have been to utilize product defect frequency as a useful metric for diagnosing manufacturing abnormalities as suggested by Watanabe.
As to claim 18, the combination of Cheng, Harada ‘204, Wang ‘460, and Watanabe teaches “[t]he data analysis apparatus of Claim 1,” and Watanabe further teaches a bias rate that includes a maximum value of bias rates for respective elements of a manufacturing condition (FIG. 10 number of abnormalities per number of device-specific products determined as a form of indexing that includes a range to include a maximum (50 abnormal products per 1000 products); [0081] maximum bias rate for products produced by a particular manufacturing device is maximum number of products determined to be abnormal divided by total number of products).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Watanabe’s teaching of generating a bias rate that includes a maximum value of bias rates for respective elements of a manufacturing condition to the apparatus taught by Cheng as modified by Harada ‘204, Wang ‘460, and Watanabe which teaches using a parallel, comparative processing between two indexing values such that in combination the apparatus is configured such that “the first bias rate includes a maximum value of bias rates for respective elements of the first manufacturing condition, and the second bias rate includes a maximum value of bias rates for respective elements of the second manufacturing condition.”
The motivation would have been to utilize a relative product defect frequency as a useful metric for diagnosing manufacturing abnormalities as suggested by Watanabe.
Claims 3 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Cheng in view of Harada ‘204, Wang ‘460, and Watanabe as applied to claim 1 above, and further in view of Wang (US 2024/0004375 A1), Wang ‘375.
As to claim 3, the combination of Cheng, Harada ‘204, Wang ‘460, and Watanabe teaches “[t]he data analysis apparatus of Claim 1, wherein the processing circuitry is configured to compute the first index value, based on the first factor data (Cheng as set forth in the grounds for rejecting claim 1)” “and to compute the second index value, based on the second factor data (Cheng as set forth in the grounds for rejecting claim 1).”
Cheng, such as in [0034] teaches determining the contribution rate (index value by processing the manufacturing process parameters such as by using a statistical method and specifically an expectation maximation algorithm but does not expressly teach processing the manufacturing process parameters (first factor data) using a “statistical hypothesis test” to compute the first index value.
Wang ‘375 discloses a method/apparatus for processing manufacturing production data in relation to product defects (Abstract, Fig. 2) that includes processing production process data using a statistical hypothesis test to compute a value indicating influence degree (degree of contribution) on process steps on abnormality ([0013] chi-square test (statistical hypothetical test) performed on process step data to determine influence degree of the process step on index values that per [0004] represent a defect degree of the sample).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Wang ‘375 teaching of using a statistical hypotheses test to determine an indicator of a degree of influence of process information on an abnormality to the apparatus taught by Cheng as modified by Harada ‘204, Wang ‘460, and Watanabe such that in combination the apparatus is configured to use a statistical hypothetical test to process the manufacture process parameter data to generate each of the first and second indexes.
Such a combination would amount to selecting a known design option for determining a degree to which process-related information influences/contributes to an abnormality condition to achieve predictable results.
As to claim 11, the combination of Cheng, Harada ‘204, Wang ‘460, Watanabe, and Wang ‘375 teaches “[t]he data analysis apparatus of Claim 3,” and as set forth in the grounds for rejecting claim 3, Wang ‘375 further teaches that the statistical hypothesis test may be a chi-square test ([0013]), which is a type of statistical hypothesis test related and often functionally interchangeable with a G-test.
For example, Watanabe discloses a method/apparatus for analyzing manufacturing data for products (Abstract; FIG. 1) that uses a G-test for processing manufacturing data to determine causal likelihood data and further noting that G-test is widely used as a replacement for chi-square test ([0083]).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Watanabe’s teaching of using a G-test as a statistical test for deriving data relating to causal likelihood for manufacturing process data, and furthermore that G-test may be used as a replacement for chi-square test for statistical analysis, to the apparatus taught by Cheng as modified by Harada ‘204, Wang ‘460, Watanabe, and Wang ‘375, which teaches using statistical hypothesis testing such as in the form of chi-square test, such that in combination the apparatus is configured to apply a G-test in addition to or as an alternative to the chi-square test taught by Wang ‘375.
Such a combination would amount to selecting a known design option for implementing statistical hypothesis testing for determining causal likelihood to achieve predicable results.
As to claim 12, the combination of Cheng, Harada ‘204, Wang ‘460, Watanabe, and Wang ‘375 teaches “[t]he data analysis apparatus of Claim 3, wherein the statistical hypothesis test is a chi-square test (Wang ‘375: [0013]).”
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Cheng in view of Harada ‘204, Wang ‘460, and Watanabe as applied to claim 1, and in further view of Hashiguchi (JP2003114713A).
As to claim 6, as best understood in view of the grounds for objecting to claim 6, the combination of Cheng, Harada ‘204, Wang ‘460, and Watanabe teaches “[t]he data analysis apparatus of Claim [1],” but none of Cheng, Harada ‘204, Wang ‘460 and Watanabe appear to teach “wherein the processing circuitry is configured to detect the abnormal state of the first product by a statistical process based on the first state data, and to detect the abnormal state of the second product by a statistical process based on the second state data.”
Hashiguchi discloses a method/apparatus for analyzing quality defects in manufactured products (Abstract) that includes using a statistical process based on data indicating a state of a product (manufacturing condition data) to detect an abnormal state of a product (Abstract disclosing collection of process data and using principal component analysis (statistical process) to determine whether a residual value for a product is out of allowable range; page 1, Claims (4), paragraph beginning with “[Claims] 1. A method for analyzing”. Examiner notes that the collected process data is data “indicative of a state” of the product as evidenced by the subsequent processing of such data by principal component analysis to derive the abnormal condition.).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Hashiguchi’s teaching of using statistical processing on manufacturing condition data to determine a product abnormality to the apparatus taught by Cheng as modified by Harada ‘204, Wang ‘460, and Watanabe, such that the determination of product abnormality for each of the first and second products is performed by a statistical process performed on the manufacturing condition data, which itself intrinsically indicates product state (i.e., processing the manufacturing condition data to derive the quality parameter).
Such a combination would amount to selecting a known design option for determining product abnormality to achieve predictable results.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Cheng in view of Harada ‘204, Wang ‘460, and Watanabe as applied to claim 1, and in further view of Harada (US 2025/0200396 A1), Harada ‘396.
As to claim 7, as best understood in view of the grounds for objecting to claim 7, the combination of Cheng, Harada ‘204, Wang ‘460, and Watanabe teaches “[t]he data analysis apparatus of Claim [1],” and Cheng further teaches using a trained classifier (machine learning component that has been trained) for processing the manufacturing process parameters to determine the contribution rates (Abstract; [0034]; [0049]). None of Cheng, Harada ‘204, Wang ‘460, and Watanabe appear to teach using trained machine learning to detect an abnormal product state and therefore do not teach “wherein the processing circuitry is configured to detect, based on the first state data, the abnormal state of the first product by a machine learning model that is trained in advance, and to detect, based on the second state data, the abnormal state of the second product by the machine learning model.”
Harada ‘396 discloses in Background that it is known in the art to use machine learning for detecting product abnormality based on manufacturing conditions ([0003]. Examiner notes that the manufacturing conditions data is data “indicative of a state” of the product as evidenced by the subsequent processing of such data by machine learning to derive the abnormal condition).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Harada ‘396 teaching of applying machine learning processing to manufacturing condition data to determine a product abnormality to the apparatus taught by Cheng as modified by Harada ‘204, Wang ‘460, and Watanabe which teaches applicability of trained machine learning for analyzing multivariate data, such that in combination the determination of product abnormality for each of the first and second products is by applying machine learning model that has been trained on the manufacturing condition data, which itself intrinsically indicates product state (i.e., processing the manufacturing condition data to derive the quality parameter).
Such a combination would amount to selecting a known design option for determining product abnormality to achieve predictable results.
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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/MATTHEW W. BACA/Examiner, Art Unit 2857
/ANDREW SCHECHTER/Supervisory Patent Examiner, Art Unit 2857