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 .
This action is responsive to the Amendment filed on 9/29/2025.
Claims 1-18 are pending in the case.
Response to Arguments
Applicant’s arguments and amendments with regards to the interpretation of claim(s) 1 under 35 U.S.C. § 112(f) have been fully considered and are not persuasive.
Therefore, the interpretation of claim(s) 1 under 35 U.S.C. § 112(f) is respectfully maintained.
Regarding claim(s) 1, applicant argues
“amendments eliminate any ambiguity that the elements are mere functional placeholders. Each unit now corresponds to a tangible component in the specification (units 102, 104, 106, 108, 112). In particular, the spec teaches a complete quality data analysis system 100 having these units: "the analysis system 100 includes all or some of an input unit 102... a data pre-processing unit 104... a determination unit 106... and a data visualizing unit 108". Thus, the claim as amended recites concrete hardware (processors executing software modules) rather than a purely functional means, overcoming the 112(f) rejection”.
Examiner respectfully disagrees.
Applicant’s specification does not provide definitions for input unit, data pre-processing unit, determination unit and data visualizing unit.
Examiner further notes that “unit” is a generic placeholder, and the input unit, data pre-processing unit, determination unit and data visualizing unit and their accompanying limitations do not recite sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Input unit, data pre-processing unit, determination unit and data visualizing unit and their accompanying limitations invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph based on the Three-Prong Test, and therefore have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
Applicant’s arguments and amendments with regards to the 35 U.S.C. § 112(b) rejection of claim(s) 3 and 5 have been fully considered and are persuasive.
Therefore, the 35 U.S.C. § 112(b) rejection of claim(s) 3 and 5 is respectfully withdrawn.
Applicant's arguments and amendments with regards to the 35 U.S.C. § 101 rejection of claim(s) 1-18 have been fully considered and are not persuasive. The 35 U.S.C. § 101 rejection of claim(s) 1-18 is respectfully maintained.
Regarding claim(s) 1:
Regarding Step 2A, Prong 1,
Applicant’s arguments with respect to claim(s) 1-18 have been considered but are moot because the new ground of rejection has been made that restructures the Prong analysis of the claims.
Examiner further reiterates that Applicant’s specification does not provide definitions for input unit, data pre-processing unit, determination unit and data visualizing unit.
Regarding Step 2A, Prong 2,
Applicant is reminded that Applicant’s specification does not provide definitions, and therefore arguments directed towards limitations in the specification but absent in the claims are not persuasive. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant further argues
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Examiner respectfully disagrees. Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the 35 U.S.C. § 101 rejection is erroneous.
Applicant further argues
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Examiner respectfully disagrees. The broad meaning of “encodes”, and imputing missing values with median/mode, appear to be practically implementable in the human mind and are understood to be a recitation of a mental process and math. There is no indication of specific data-engineering steps in the claims.
Applicant further argues
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Examiner respectfully disagrees. Revising quality control criteria based on the results of the abstract idea is a field of use limitation, and does not constitute a technological improvement.
Applicant further argues
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Examiner respectfully disagrees. Applicant’s claims do not include limitations that use the analysis report to generate new quality control criteria and to guide factor selection/adjustment in the simulator UI.
Applicant further argues
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Examiner respectfully disagrees. The above statement ignores the 35 USC Prong analysis of operations performed by the units, and addresses the “generic computer” analysis in a vacuum. One cannot show patent eligibility by attacking patent eligibility analysis of individual limitations, where limitations have been addresses under multiple considerations.
Applicant further argues
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Examiner respectfully disagrees. Revising quality control criteria based on the results of the abstract idea is a field of use limitation, and does not constitute a technological improvement. Further the claims do not recite limitations that actually reduce defects.
Regarding Step 2B,
Applicant further argues
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Examiner respectfully disagrees.
Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a non-conventional ordered combination without specifically pointing out how the ordered combination is non-conventional.
Applicant further argues
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Examiner respectfully disagrees. Applicant’s claims do not include limitations reduce defects, rather the criteria merely revised. As such, the claims do not recite limitations where the revised criteria reduce defects,
Applicant further argues
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Examiner respectfully disagrees. Applicant’s arguments with respect to claim(s) 1-18 have been considered but are moot because the new ground of rejection has been made that restructures the Prong analysis of the claims and does not address any limitations as well-understood, routine or conventional.
Traceablity
Applicant is reminded that Applicant’s specification does not provide definitions, and therefore arguments directed towards limitations in the specification but absent in the claims are not persuasive. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant’s arguments and remarks in the Traceability section are directed towards the specification, rather than claim limitations.
Applicant’s following arguments with respect to the 35 U.S.C. § 103 rejection of claim(s) 1-18 have been considered but are moot because a Non-Final Rejection with new ground(s) of rejection is being issued in the current Office Action.
The new ground(s) of rejection do not rely on any rejections specifically challenged in the arguments.
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Applicant's following arguments with respect to the rejection of claims 1-18 under 35 U.S.C. § 103 have been considered, but are not persuasive.
Applicant argues
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Examiner respectfully disagrees. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Applicant argues
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Examiner respectfully disagrees. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., field claim) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Traceablity
Applicant is reminded that Applicant’s specification does not provide definitions, and therefore arguments directed towards limitations in the specification but absent in the claims are not persuasive. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant’s arguments and remarks in the Traceability section are directed towards the specification, rather than claim limitations.
Claim Objections
Claim 10 is objected to because of the following informalities:
“wherein user interface presents” should be “wherein the user interface presents”.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Claim limitations
“input unit configured to obtain quality data on a product, the quality data being collected for process factors occurring in a production process of the product”,
“data pre-processing unit configured to pre-process the quality data by encoding the process factors for each data types and setting the process factors that are lost while the quality data is collected, to a preset value, including a median value for numerical process factors and a mode value for categorical process factors”,
“determination unit configured to determine whether the product is acceptable based on the process factors using an inference model that is based on machine learning”,
“data visualization unit configured to generate an analysis report on a quality of the product based on the process factors and the determination”, and
“training unit configured to train the inference model using the quality data for learning and a first label relevant to the quality data for learning”," in claim 1;
have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because they use a generic placeholder
“input unit" coupled with functional language "configured to obtain quality data on a product, the quality data being collected for process factors occurring in a production process of the product”,
“data pre-processing unit" coupled with functional language "configured to pre-process the quality data by encoding the process factors for each data types and setting the process factors that are lost while the quality data is collected, to a preset value, including a median value for numerical process factors and a mode value for categorical process factors”,
“determination unit" coupled with functional language "configured to determine whether the product is acceptable based on the process factors using an inference model that is based on machine learning”,
“data visualization unit" coupled with functional language “configured to generate an analysis report on a quality of the product based on the process factors and the determination”, and
“training unit" coupled with functional language "configured to train the inference model using the quality data for learning and a first label relevant to the quality data for learning”
in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation: according to [290,291], "input", “data pre-processor”, “determiner”, “data visualizer” and “trainer” are implemented as hardware/ processor(s) executing software modules to perform the instructions of the modules
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
According to the first part of the analysis, in the instant case, claim(s) 1-10 is/are directed to a system, claim(s) 11-17 are directed to a method, claim(s) 18 is/are directed to a product. Thus, each of the claim(s) falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Independent claims:
Step 2A, Prong 1
Regarding claim(s) 1, 11 and 18, this/these claim(s) recite(s)
pre-process the ... data by encoding the ... factors for each data types and setting the process factors that are lost while the ... data is collected, to a preset value, including a median value for numerical ... factors and a mode value for categorical ... factors;
determine whether the product is acceptable based on the ... factors;
process factors set to random adjusted values to generate the determination and to use the random adjusted values and a relevant determination to revise ... criteria ....
generate the determination and to use the random adjusted values and a relevant determination to revise .... criteria on the ... factors.
These steps for pre-processing, determining, setting and generating appear to be practically implementable in the human mind and are understood to be a recitation of a mental process and math (user can encode data and set missing data to default values, determine product acceptability, set random adjusted values, determine quality determination and revise quality control criteria, calculation of median and mode are mathematical processes).
Step 2A, Prong 2
Regarding claim(s) 1, 11 and 18, this judicial exception is not integrated into a practical application.
In particular, the claim(s) recite a memory, a processor, for implementing input unit, data pre-processing unit, determination unit and data visualizing unit.
The memory, processor, are recited at a high level of generality and recited so generically that they represent no more than mere instructions to apply the judicial exception on a computer (see MPEP 2106.05(f). These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(h)).
Regarding claim(s) 1, 11 and 18, this/these claim(s) further recite(s)
obtain data and obtain the process factors (These limitations appear to be directed to receiving information, which is understood to be insignificant extra-solution activity),
quality data on a product, the quality data being collected for process factors occurring in a production process of the product; quality control criteria on the process factors; quality of the product (These limitations appear to be directed to the specification of information and is understood to be a field of use limitation),
generate an analysis report on a quality ... based on the ... factors and the determination (These limitations appear to represents insignificant extrasolution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output),
train the inference model using the ... data for learning and a first label relevant to the ... data for learning (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data);
using an inference model that is based on machine learning; wherein the inference model is a simulator ... to generate the determination (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level application of a previously trained inference machine model that is a simulator to make a prediction and revise quality control criteria),
The additional element(s) as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Therefore, the claim(s) is/are directed to an abstract idea.
Step 2B
Regarding claim(s) 1, 11 and 18, this/these claim(s) do/does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In particular, the claim(s) recite a memory, a processor, for implementing input unit, data pre-processing unit, determination unit and data visualizing unit.
The memory, processor, are recited at a high level of generality and recited so generically that they represent no more than mere instructions to apply the judicial exception on a computer (see MPEP 2106.05(f). These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(h)).
Regarding claim(s) 1, 11 and 18, this/these claim(s) further recite(s)
obtain data and obtain the process factors (These limitations appear to be directed to receiving information, which is understood to be insignificant extra-solution activity such as data gathering., see MPEP 2106.05(g)),
quality data on a product, the quality data being collected for process factors occurring in a production process of the product; quality control criteria on the process factors (These limitations appear to be directed to the specification of information and is understood to be a field of use limitation, See MPEP 2106.05(h)),
generate an analysis report on a quality ... based on the ... factors and the determination (These limitations appear to represents extrasolution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output, see MPEP 2106.05(g), the data visualizer and analysis report are recited at a high level of generality and amount to using generic computer components to generate the output information of the abstract idea, see MPEP 2106.05(g))
train the inference model using the ... data for learning and a first label relevant to the ... data for learning (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data);
using an inference model that is based on machine learning; wherein the inference model is a simulator ... to generate the determination (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level application of a previously trained inference machine model that is a simulator to make a prediction and revise quality control criteria).
The additional element(s) as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Therefore, the claim(s) is/are not patent eligible.
Step 2A, Prong 1 Dependent Claims
Regarding Claim(s) 2, this/ these claims recite generate a second label by encoding, among the process factors, a target factor,
Regarding Claim(s) 3, this/ these claims recite wherein the determination of the product being acceptable ... is expressed as a probability value of the product,
Regarding Claim(s) 4 and 12, this/ these claims recite wherein the analysis report comprises: any one or any combination of an analysis data summary, process factor importance, a data distribution, and an analysis result for the process factors,
Regarding Claim(s) 5, this/ these claims recite wherein the analysis result comprises: an accuracy, a precision, a recall, and an F1 score, each based on the second label and the determination,
Regarding Claim(s) 7 and 14, this/ these claims recite perform a T-test on the process factors or to perform a comparison between information gains of the process factors in response to the process factors constituting the quality data for learning having a count exceeding a threshold, and to sort out main process factors so that the process factors have a count less than or equal to the threshold,
These steps appear to be practically implementable in the human mind and are understood to be a recitation of a mental process and math.
Regarding Claim(s) 6, this/ these claims recite wherein the trainer is further ... implemented based on a tree, ... toward maximizing information gains in respective branches constituting the tree.
These steps appear to be practically implementable in the human mind and are understood to be a recitation of a mental process and math. Because the recited “training” explicitly recites performing mathematical concepts, the limitation falls within the mathematical concepts grouping of abstract ideas.
Regarding Claim(s) 8 and 15, this/ these claims recite select, from among the four machine learning models, a model that is best in trained performance ..., wherein the trained performance comprises an accuracy, a precision, a recall, and an F1 score that are based on the first label and determinations ... by the four machine learning models.
These steps appear to be practically implementable in the human mind and are understood to be a recitation of a mental process and math. Because the recited “training” explicitly recites performing mathematical concepts, the limitation falls within the mathematical concepts grouping of abstract ideas.
Step 2A, Prong 2 Dependent Claims
Regarding claim(s) 2, this/these claims recite a target factor indicating whether a field claim has occurred against the product,
Regarding Claim(s) 3, this/ these claims recite wherein the determination of the product being acceptable indicates whether a field claim has occurred against the product
These limitations appear to be directed to the specification of information and is understood to be a field of use limitation.
Regarding Claim(s) 6 and 13, this/ these claims recite train four machine learning models that are algorithms of a decision tree, a random forest, an Extreme Gradient Boosting (XGBoost), and a Light Gradient Boosting Model (LightGBM) which are implemented based on a tree, and to train each of the four machine learning models based on the first label.
This limitation recites training a decision tree, a random forest, an Extreme Gradient Boosting (XGBoost), and a Light Gradient Boosting Model (LightGBM) which are implemented based on a tree, the training is recited at a high level of generality and amounts to no more than using a decision tree, a random forest, an Extreme Gradient Boosting (XGBoost), and a Light Gradient Boosting Model (LightGBM) which are implemented based on a tree as a tool to perform an abstract idea, which is not indicative of integration into a practical application.
Regarding Claim(s) 8 and 15, this/ these claims recite select a model ... as the inference model, wherin the trained performance comprises ... determinations generated respectively by the four machine learning models.
This limitation recites using an artificial neural network as a tool to perform an abstract idea, which is not indicative of integration into a practical application, and is generally linking the use of the judicial exception to a particular technological environment or field of use - see MPEP 2106.05(h).
Regarding Claim(s) 9 and 16, this/ these claims recite a user interface (UI) configured to receive a factor selection for input to the inference model, wherein the user interface provides an input field for random adjusted process-factor values to the simulator and displays the corresponding revised quality control criteria.
These limitations appear to represents extrasolution activity because it is a mere nominal or tangential addition to the claim, amounting to receiving information and displaying, which is understood to be insignificant extra-solution activity such as data gathering and data output, see MPEP 2106.05(g), the user interface are recited at a high level of generality and amount to using generic computer components to generate the output information of the abstract idea.
Regarding Claim(s) 10 and 17this/ these claims recite wherein user interface presents, as output, the revised quality control criteria and associated determinations generated by the simulator based on the random adjusted process-factor values
These limitations appear to represents extrasolution activity because it is a mere nominal or tangential addition to the claim, amounting to receiving information and displaying, which is understood to be insignificant extra-solution activity such as data gathering and data output, see MPEP 2106.05(g), the user interface are recited at a high level of generality and amount to using generic computer components to generate the output information of the abstract idea.
Step 2B Dependent Claims
Regarding claim(s) 2, this/these claims recite a target factor indicating whether a field claim has occurred against the product,
Regarding Claim(s) 3, this/ these claims recite wherein the determination of the product being acceptable indicates whether a field claim has occurred against the product.
These limitations appear to be directed to the specification of information and is understood to be a field of use limitation, See MPEP 2106.05(h).
Regarding Claim(s) 6 and 13, this/ these claims recite train four machine learning models that are algorithms of a decision tree, a random forest, an Extreme Gradient Boosting (XGBoost), and a Light Gradient Boosting Model (LightGBM) which are implemented based on a tree, and to train each of the four machine learning models based on the first label
This limitation recites training a decision tree, a random forest, an Extreme Gradient Boosting (XGBoost), and a Light Gradient Boosting Model (LightGBM) which are implemented based on a tree, the training is recited at a high level of generality and amounts to no more than using a decision tree, a random forest, an Extreme Gradient Boosting (XGBoost), and a Light Gradient Boosting Model (LightGBM) which are implemented based on a tree as a tool to perform an abstract idea, which is not indicative of integration into a practical application, see MPEP 2106.05(f),
Regarding Claim(s) 8 and 15, this/ these claims recite select a model ... as the inference model, wherin the trained performance comprises ... determinations generated respectively by the four machine learning models.
This limitation recites using an inference model as a tool to perform an abstract idea, which is not indicative of integration into a practical application, and is generally linking the use of the judicial exception to a particular technological environment or field of use - see MPEP 2106.05(h).
Regarding Claim(s) 9 and 16, this/ these claims recite a user interface (UI) configured to receive a factor selection for input to the inference model, wherein the user interface provides an input field for random adjusted process-factor values to the simulator and displays the corresponding revised quality control criteria.
These limitations appear to represents extrasolution activity because it is a mere nominal or tangential addition to the claim, amounting to receiving information and displaying, which is understood to be insignificant extra-solution activity such as data gathering and data output, see MPEP 2106.05(g), the user interface are recited at a high level of generality and amount to using generic computer components to generate the output information of the abstract idea.
Regarding Claim(s) 10 and 17this/ these claims recite wherein user interface presents, as output, the revised quality control criteria and associated determinations generated by the simulator based on the random adjusted process-factor values
These limitations appear to represents extrasolution activity because it is a mere nominal or tangential addition to the claim, amounting to receiving information and displaying, which is understood to be insignificant extra-solution activity such as data gathering and data output, see MPEP 2106.05(g), the user interface are recited at a high level of generality and amount to using generic computer components to generate the output information of the abstract idea.
Accordingly, these additional elements in the dependent claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Therefore the claim limitations, taken either alone or in combination, fail to provide an inventive concept. Thus the claims are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, 9, 10, 11, 12, 16, 17, 18, are rejected under 35 U.S.C. 103 as being unpatentable over Phillips (US 20140358825 A1), in view of Cay (US 11055639 B1) and Maier (US 11694775 B1).
Regarding claim 1, Phillips teaches a system for analyzing quality data, comprising: an input unit..., a data pre-processing unit..., a determination unit..., a data visualization unit... a training unit ... (Phillips [28-31, 37, 55] system to determine whether goals are met, system modules stored in one or more storage media may be implemented using processor(s), may be separate and/or combined):
an input unit configured to obtain quality data on a product, the quality data being collected for process factors occurring in a ... process of the product (Phillips [73-76, 78, 114, 174] data related to quality for a business product in process may be received for input features (process factors) with attributes related to the process, Phillips [28-31, 37, 55] module(s) may be used);
a data pre-processing unit configured to pre-process the quality data by encoding the process factors for each data types and setting the process factors that are lost while the quality data is collected, to a preset value (Phillips [74, 104] data may be pre-computed to fill-in, pad, or simulate missing values based on existing information, Phillips [28-31, 37, 55] module(s) may be used);
a determination unit configured to determine whether the product is acceptable based on the process factors using an inference model that is based on machine learning (Phillips [60, 61, 80, 92] determination is made based on the received features and attributes, as to whether the product meets goals (acceptable), inferred function and model(s) may be used for determination, Phillips [28-31, 37, 55] module(s) may be used);
a data visualization unit configured to generate an analysis report on a quality of the product based on the process factors and the determination (Phillips [79, 80, 95, 96] results (analysis report) of whether goals are met may be displayed, Phillips [28-31, 37, 55] module(s) may be used); and
a training unit configured to train the inference model using the quality data for learning and a first label relevant to the quality data for learning (Phillips [114-116] features and attributes may be used to train model, features may be labels, Phillips [60, 61, 80, 92] determination is made based on the received features and attributes, as to whether the product meets goals (acceptable), inferred function and model(s) may be used for determination, Phillips [28-31, 37, 55] module(s) may be used).
Phillips does not specifically teach .... production process of the product;
including a median value for numerical process factors and a mode value for categorical process factors;
wherein the inference model is a simulator configured to obtain the process factors set to random adjusted values to generate the determination and to use the random adjusted values and a relevant determination to revise quality control criteria on the process factors.
However Cay teaches process factors occurring in a production process of the product; wherein the inference model is a simulator configured to obtain the process factors set to random adjusted values to generate the determination and to use the random adjusted values and a relevant determination to revise quality control criteria on the process factors (Cay Col 21, lines 35-49, Col 26, lines 7-38, Col 31, line 24- Col 32, line 24, model may be a simulator and may be used to process new data as available, process factors may be for manufacturing process of product, process factor values may be randomized and then used to generate determination of whether quality is acceptable, quality criterion (configurable settings) may be adjusted based on determination(s)).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Cay of process factors occurring in a production process of the product; wherein the inference model is a simulator configured to obtain the process factors set to random adjusted values to generate the determination and to use the random adjusted values and a relevant determination to revise quality control criteria on the process factors, into the invention suggested by Phillips; since both inventions are directed towards determining product quality based on process factors, and incorporating the teaching of Cay into the invention suggested by Phillips would provide the added advantage of allowing simulated data (that is not relying solely on input data) to be used to adjust configurable settings used to determine product quality, and the combination would perform with a reasonable expectation of success (Cay Col 21, lines 35-49, Col 26, lines 7-38, Col 31, line 24- Col 32, line 24).
Phillips and Cay do not specifically teach including a median value for numerical process factors and a mode value for categorical process factors.
However Maier teaches setting ...data values... that are lost ... to a preset value, including a median value for numerical ... data values... and a mode value for categorical ... data values... (Maier Col 11, line 55- Col 12, line 7, missing data values are set to median for continuous (numerical) and mode for category data types respectively, avoids adverse impact to any individual results without knowledge of an observed value and results should not be beneficial or detrimental to a given aspect with respect to similar aspects- if an unobserved value is passed as a model input).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Maier of setting ...data values... that are lost ... to a preset value, including a median value for numerical ... data values... and a mode value for categorical ... data values..., into the invention suggested by Phillips and Cay; since both inventions are directed towards preprocessing data that is lost while the data is collected, to a preset value, and incorporating the teaching of Maier into the invention suggested by Phillips and Cay would provide the added advantage of avoiding adverse impact to any individual results without knowledge of an observed value and ensuring thatresults should not be beneficial or detrimental to a given aspect with respect to similar aspects- if an unobserved value is passed as a model input, and the combination would perform with a reasonable expectation of success (Maier Col 11, line 55- Col 12, line 7).
Regarding claim 3, Phillips, Cay and Maier teach the invention as claimed in claim 1 above. Phillips further teaches wherein the determination of the product being acceptable indicates whether a field claim has occurred against the product, and wherein the determination is expressed as a probability value of the product (Phillips [51, 56, 73, 154] product quality may be based on customer datasets which may include complaints for products, product quality may be expressed as a confidence metric for a prediction, metric may be expressed as a percentage or ratio).
Regarding claim 9, Phillips, Cay and Maier teach the invention as claimed in claim 1 above. Phillips does not specifically teach a user interface (UI) configured to receive a factor selection for input to the inference model, wherein the user interface provides an input field for random adjusted process-factor values to the simulator and displays the corresponding revised quality control criteria,
However Cay teaches a user interface (UI) configured to receive a factor selection for input to the inference model, wherein the user interface provides an input field for random adjusted process-factor values to the simulator and displays the corresponding revised quality control criteria (Cay Col 21, lines 35-49, Col 26, lines 7-38, Col 31, line 24- Col 32, line 24, model may be a simulator and may be used to process new data as available, process factors may be for manufacturing process of product, process factor values may be randomized and then used to generate determination of whether quality is acceptable, quality criterion (configurable settings) may be adjusted based on determination(s), Cay Fig. 13, Col 32, lines 47-50, Col 31, lines 14-17, Col 22, lines 37-45, user interface may display adjusted quality control criteria, operator can fine tune factors using user interface, user interface may have text boxes, menus etc, Cay Col 32, lines 1-9, 18-24, 43-51, operator input may be used to determine factor values).
Regarding claim 10, Phillips, Cay and Maier teach the invention as claimed in claim 9 above. Phillips does not specifically teach wherein user interface presents, as output, the revised quality control criteria and associated determinations generated by the simulator based on the random adjusted process-factor values
However Cay teaches wherein user interface presents, as output, the revised quality control criteria and associated determinations generated by the simulator based on the random adjusted process-factor values (Cay Col 4, lines 41-67, Col 22, lines 37-47, Cay Col 21, lines 35-49, Col 26, lines 7-38, Col 31, line 24- Col 32, line 24, Figs. 17 and 18, model input and output values may be represented in the UI, model may be a simulator and may be used to process new data as available, process factors may be for manufacturing process of product, process factor values may be randomized and then used to generate determination of whether quality is acceptable, quality criterion (configurable settings) may be adjusted based on determination(s), Col 31, lines 7-11, Cay Fig. 13, Col 32, lines 47-50, Col 31, lines 14-17, Col 22, lines 37-45, Col 33, lines 43-67, user interface may display adjusted quality control criteria and other determined values, determined values can include prediction whether quality is acceptable).
Claim 11 is directed towards a method performing instructions similar in scope to the instructions executed by the system of claim 1 and is rejected under the same rationale.
Regarding claim 12, Phillips, Cay and Maier teach the invention as claimed in claim 11 above. Phillips further teaches wherein the generating of the analysis report comprises generating... process factor importance, an analysis result for the process factors (Phillips [84, 95, 96] results (analysis report) based on attributes of whether goals are met and feature impact (importance) may be displayed in a UI).
Regarding claim 16, Phillips, Cay and Maier teach the invention as claimed in claim 11 above. Phillips further teaches presenting, a user interface (UI), an analysis result including ...a factor importance... (Phillips [84, 95, 96] results (analysis report) based on attributes of whether goals are met and feature impact (importance) may be displayed in a UI).
Phillips does not specifically teach wherein the user interface further receives random adjusted process-factor values as inputs to the simulator and outputs corresponding revised quality control criteria
However Cay teaches wherein the user interface further receives random adjusted process-factor values as inputs to the simulator and outputs corresponding revised quality control criteria Cay Col 21, lines 35-49, Col 26, lines 7-38, Col 31, line 24- Col 32, line 24, model may be a simulator and may be used to process new data as available, process factors may be for manufacturing process of product, process factor values may be randomized and then used to generate determination of whether quality is acceptable, quality criterion (configurable settings) may be adjusted based on determination(s), Cay Fig. 13, Col 32, lines 47-50, Col 31, lines 14-17, Col 22, lines 37-45, user interface may display adjusted quality control criteria, operator can fine tune factors using user interface, user interface may have text boxes, menus etc, Cay Col 32, lines 1-9, 18-24, 43-51, operator input may be used to determine factor values).
Regarding claim 17, Phillips, Cay and Maier teach the invention as claimed in claim 16 above. Phillips does not specifically teach wherein the user interface presents, as output, the revised quality control criteria and associated determinations generated by the simulator based on the random adjusted process-factor values
However Cay teaches wherein the user interface presents, as output, the revised quality control criteria and associated determinations generated by the simulator based on the random adjusted process-factor values (Cay Col 4, lines 41-67, Col 22, lines 37-47, Cay Col 21, lines 35-49, Col 26, lines 7-38, Col 31, line 24- Col 32, line 24, Figs. 17 and 18, model input and output values may be represented in the UI, model may be a simulator and may be used to process new data as available, process factors may be for manufacturing process of product, process factor values may be randomized and then used to generate determination of whether quality is acceptable, quality criterion (configurable settings) may be adjusted based on determination(s), Col 31, lines 7-11, Cay Fig. 13, Col 32, lines 47-50, Col 31, lines 14-17, Col 22, lines 37-45, Col 33, lines 43-67, user interface may display adjusted quality control criteria and other determined values, determined values can include prediction whether quality is acceptable).
Claim 18 is directed towards a medium storing instructions similar in scope to the instructions executed by the system of claim 1 and is rejected under the same rationale. Phillips further teaches a non-transitory computer-readable recording medium storing instructions that, when executed by a processor, cause a processor to perform (Phillips [28, 30-33, 37, 55] system to determine whether goals are met, system modules may be implemented using processor(s) executing instructions stored on a medium).
Claims 2, 4, are rejected under 35 U.S.C. 103 as being unpatentable over Phillips (US 20140358825 A1) in view of Cay (US 11055639 B1) and Maier (US 11694775 B1), and further in view of Szeto (US 10713594 B2).
Regarding claim 2, Phillips, Cay and Maier teach the invention as claimed in claim 1 above. Phillips further teaches a target factor indicating whether a field claim has occurred against the product (Phillips [51, 56, 73, 154] product quality may be based on customer datasets which may include complaints for products)
Phillips does not specifically teach wherein the data pre-processor is further configured to generate a second label by encoding, among the process factors, a target factor indicating whether a field claim has occurred against the product
However Szeto teaches wherein the data pre-processor is further configured to generate a second label by encoding, among the process factors, a target factor indicating whether a field claim has occurred against the product (Szeto Col 51, line 35- Col 52, line 16, customer interaction may may be labelled as suggestion, compliment or complaint for easy processing).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Szeto of wherein the data pre-processor is further configured to generate a second label by encoding, among the process factors, a target factor indicating whether a field claim has occurred against the product, into the invention suggested by Phillips, Cay and Maier; since both inventions are directed towards predicting quality of a product and using customer complaints, and incorporating the teaching of Szeto into the invention suggested by Phillips, Cay and Maier would provide the added advantage of labelling customer interaction as a suggestion, compliment or complaint to allow the interaction to be processed accordingly, and the combination would perform with a reasonable expectation of success (Szeto Col 51, line 35- Col 52, line 16).
Regarding claim 4, Phillips, Cay, Maier and Szeto teach the invention as claimed in claim 2 above. Phillips further teaches wherein the analysis report comprises...an analysis result for the process factors (Phillips [84, 95, 96] results (analysis report) based on attributes of whether goals are met may be displayed in a UI).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Phillips (US 20140358825 A1) in view of Cay (US 11055639 B1), Maier (US 11694775 B1) and Szeto (US 10713594 B2), and further in view of Orhan (US 20240028830 A1).
Regarding claim 5, Phillips, Cay, Maier and Szeto teach the invention as claimed in claim 4 above. Phillips does not specifically wherein the analysis result comprises: an accuracy, a precision, a recall, and an F1 score, each based on the second label and the determination
However Orhan teaches wherein the analysis result comprises: an accuracy, a precision, a recall, and an F1 score, each based on ...a label... and ...a determination... (Orhan [19, 48, 56-58] models may be based on labelled data, model may provide a prediction for anomalies (not satisfying quality criteria), model (based on labelled data) results may be analyzed using accuracy, precision, recall or F1 scores, this allows selecting of best model and gradually increasing the quality of the predictions of the model).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated
the concept taught by Orhan of wherein the analysis result comprises: an accuracy, a precision, a recall, and an F1 score, each based on ...a label... and ...a determination...,
into the invention suggested by Phillips, Cay, Maier and Szeto,
to result in wherein the analysis result comprises: an accuracy, a precision, a recall, and an F1 score, each based on the second label and the determination;
since both inventions are directed towards determining quality, and incorporating the teaching of Orhan into the invention suggested by Phillips, Cay, Maier and Szeto would provide the added advantage of selecting of best model and gradually increasing the quality of the predictions of the model, and the combination would perform with a reasonable expectation of success (Orhan [19, 48, 56-58]).
Claims 6, 7, 13, 14, are rejected under 35 U.S.C. 103 as being unpatentable over Phillips (US 20140358825 A1) in view of Cay (US 11055639 B1) and Maier (US 11694775 B1), and further in view of Corredor (US 20200286227 A1).
Regarding claim 6, Phillips, Cay and Maier teach the invention as claimed in claim 1 above. Phillips further teaches wherein the trainer is further configured to train four machine learning models that are algorithms of a decision tree, a random forest, ..., which are implemented based on a tree, and to train each of the four machine learning models based on the first label ... (Phillips [119, 170, 171, 125, 116] learned functions may be generated using different machine learning algorithms including decision tree and random forest, models may be trained based on feature subsets).
Phillips does not specifically teach an Extreme Gradient Boosting (XGBoost), and a Light Gradient Boosting Model (LightGBM), ... toward maximizing information gains in respective branches constituting the tree.
However Corredor teaches four machine learning models that are algorithms of a decision tree, a random forest, an Extreme Gradient Boosting (XGBoost), and a Light Gradient Boosting Model (LightGBM) which are implemented based on a tree, and to train each of the four machine learning models based on the first label toward maximizing information gains in respective branches constituting the tree (Corredor [108, 117] machine learning algorithm may be include boosted algorithm(s) such as XGBoost and LightGBM, decision tree may be built based on parameters with greatest information gain at the top nodes of the tree, decision tree may be built by searching for the most informative nodes (e.g., parameters) for a given dataset).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Phillips, Cay and Maier’s existing machine learning algorithms with the additional machine learning algorithms taught by Corredor, according to known methods, to yield predictable results. Per the guidance at MPEP § 2143 (subsection (I.)(A.)), the relevant factual findings are as follows:
(A) The prior art included each element claimed, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. The evidence for this finding is provided above, where each claim element is mapped to a respective citation from the prior art references.
(B) One of ordinary skill in the art could have combined the elements as claimed by known methods, and in combination, each element merely performs the same function as it does separately. Evidence for this finding includes paragraphs 121, 122, and 127 of Phillipps’ disclosure, which instruct the person of ordinary skill in the art to combine known machine learning models into one or more ensembles.
(C) One of ordinary skill in the art would have recognized that the results of the combination were predictable, based on all three references’ discussion of the different models and how they may work together using known ensemble methods.
Regarding claim 7, Phillips Cay, Maier and Corredor teach the invention as claimed in claim 6 above. Phillips further teaches wherein the trainer is further configured to perform a T-test on the process factors or to perform a comparison between information gains of the process factors in response to the process factors constituting the quality data for learning having a count exceeding a threshold, and to sort out main process factors so that the process factors have a count less than or equal to the threshold (Phillips [133] ensemble may be generated using minimal features and iteratively increasing number of features used to generate ensemble until increasing features stops being effective, thus minimum effective set of features for use in a machine learning ensemble may be determined (both effective and efficient), feature effectiveness threshold may be predetermined or hard coded, may be selected by a client as part of a new ensemble request or the like, may be based on one or more parameters or limitations).
Claim(s) 13 and 14 is/are dependent on claim 11 above, is/are directed towards a method performing instructions similar in scope to the instructions executed by the system of claim(s) 6 and 7 respectively, and is/are rejected under the same rationale.
Claims 8, 15, are rejected under 35 U.S.C. 103 as being unpatentable over Phillips (US 20140358825 A1) in view of Cay (US 11055639 B1), Maier (US 11694775 B1) and Corredor (US 20200286227 A1), and further in view of Orhan (US 20240028830 A1).
Regarding claim 8, Phillips Cay, Maier and Corredor teach the invention as claimed in claim 6 above. Phillips further teaches wherein the trainer is further configured to select, from among the four machine learning models, a model that is best in trained performance as the inference model, wherein the trained performance comprises an accuracy, ...that are based on the first label and determinations generated respectively by the four machine learning models
(Phillips [156] models are selected based on trained performance for ensemble, selection may be based on metrics which may include accuracy, Phillips [61, 80, 92] determination is made based on the received features and attributes, as to whether the product meets goals (acceptable), inferred function and model(s) may be used for determination).
Phillips does not specifically teach a precision, a recall, and an F1 score
However Orhan teaches select, from among the four machine learning models, a model that is best in trained performance as the inference model, wherein the trained performance comprises an accuracy, a precision, a recall, and an F1 score that are based on the first label and determinations generated respectively by the four machine learning models (Orhan [58] models may be selected for ensemble based on accuracy, a precision, a recall, and an F1 score, in order to determine best model).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Orhan of select, from among the four machine learning models, a model that is best in trained performance as the inference model, wherein the trained performance comprises an accuracy, a precision, a recall, and an F1 score that are based on the first label and determinations generated respectively by the four machine learning models, into the invention suggested by Phillips Cay, Maier and Corredor; since both inventions are directed towards determining ensemble models, and incorporating the teaching of Orhan into the invention suggested by Phillips Cay, Maier and Corredor would provide the added advantage of using a comprehensive measure to determine model performance by using additional metrics to determine the best model, and the combination would perform with a reasonable expectation of success (Orhan [58]).
Claim(s) 15 is/are dependent on claim 11 above, is/are directed towards a method performing instructions similar in scope to the instructions executed by the system of claim(s) 8 respectively, and is/are rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Noone (US 20200166909 A1) discloses determining product quality and adjusting process factors.
Chen (US 20200202235 A1) discloses determining product quality and adjusting process factors.
Kimura (US 20230017042 A1) discloses determining degree of influence of process factors on product quality characteristics.
Kuhn (US 20220121183 A1) discloses simulating product manufacture based on process factors.
Deverakonda (US 20220067622 A1) discloses predicting product quality implementing causal analysis.
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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SANCHITA . ROY
Primary Examiner
Art Unit 2146
/SANCHITA ROY/Primary Examiner, Art Unit 2146