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 statements (IDS) submitted on 18 December 2026 and 19 May 2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Response to Amendment
Claims 12, 14, 16-17, and 21-22 have been amended. No claims have been newly added nor canceled. Claims 12-28 remain pending in the present application. The previous claim objections to claims 12, 14, 16, 17, 21, and 22 have been withdrawn as a result of amendment.
Response to Arguments
Applicant’s arguments with respect to the rejection(s) of claims 12, 13, 16-18, 21, and 25-28 under 35 U.S.C. § 102(a)(1) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Liao (US 20200090319 A1).
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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Specifically, the “ means for … detecting process data and performing a model-based assessment” and “means configured to … perform a test assessment … and further train the machine learned model,” in claim 25 and the “means for … detecting further process data … performing a second model-based assessment … and monitoring the robot…,” in claim 26 are being interpreted as a processing unit, in line with at least Page 19 of the present specification.
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 12-13, 16-18, 20-21, and 25-28 are rejected under 35 U.S.C. 103 as being unpatentable over Liao (US 20200090319 A1), hereafter Liao, in view of Periaswamy (US 20190098035 A1), hereafter Periaswamy.
Regarding claim 12, Liao discloses a method for monitoring, wherein the following steps are performed by a controller:
(a.1) detecting process data (0012, At block S1, the AOI device detects a component and generates an image of the component. If the image of the component is determined to be qualified, block S8 is implemented. If the image of the component is determined to be unqualified, block S2 is implemented. 0013, At block S2, the image of the unqualified component is collected and processed. Block S2 may be performed by a processor (not shown) of the AOI device or other device having similar functions.);
(a.2) performing a first model-based assessment with the aid of a machine-learned model on the basis of the detected process data (0016, At block S4, the digital image information obtained in block S2 is sent to the machine learning model for training and testing the machine learning model, and letting the machine learning model determine whether the component is unqualified or the component was marked unqualified by error.);
In response to the first model-based assessment satisfying an examination criterion (0018, At block S5, a result of determination of the machine learning model is verified. If the accuracy of the machine learning model reaches a predetermined standard, block S6 is implemented. Otherwise, if the accuracy of the machine learning model does not reach the predetermined standard, block S8 is implemented, the machine learning model is optimized and adjusted, and then blocks S4, S5, and S8 are repeated until the accuracy of the machine learning model reaches the predetermined standard.), then:
(b.1) performing a test assessment with the aid of a testing authority (0019, As shown in FIG. 3, at block 551, the image of the component that the machine learning model determines to be unqualified is sent to a visual operation platform of the AOI device, and the image of the machine learning model is inspected by an operator.), and
(b.2) further training the machine-learned model on the basis of the test assessment (0019, The result of determination by the machine learning model is compared to a result of determination by the operator, and the accuracy of the machine learning model is calculated. If the accuracy of the machine learning model is lower than a predetermined value, such as 99.99%, it is determined that the result of determination by the machine learning model is inconsistent with the result of determination by the operator, and block S8 is implemented. See also Fig. 1, element S8);
(c.1) detecting further process data (0020, At block S6, the verified machine learning model is applied in the AOI device, an image of a next component to be inspected is obtained from the AOI device, and the image of the next component to be inspected is processed to obtain new digital image information.);
(c.2) performing a second model-based assessment with the aid of the further trained machine-learned model on the basis of the further detected process data (0020, The new digital image information is input to the machine learning model. The machine learning model determines whether the next component to be inspected is unqualified.); and
(c.3) monitoring for errors during the first process and based on the second model-based assessment (0020, If the result of determination is qualified, block S9 is implemented. If the result of determination is unqualified, block S7 is implemented, and the unqualified component is disposed. In an initial stage of application of the machine learning model in the AOI equipment, the accuracy of the machine learning model can be repeatedly confirmed by an operator. After the accuracy of the machine learning model is verified, the operator can be replaced by the machine learning model.);
Wherein the model-based assessment satisfies the examination criterion when at least one of:
The first model-based assessment identifies the occurrence in the process data of an error belonging to a group of error types (0016, At block S4, the digital image information obtained in block S2 is sent to the machine learning model for training and testing the machine learning model, and letting the machine learning model determine whether the component is unqualified or the component was marked unqualified by error. Examiner's note: the examiner is interpreting the "determin[ing] whether the component is unqualified or marked unqualified by error" as an identification, i.e., the determination, in the process data, i.e., the digital image information, the occurrence of an error belonging to a group of error types, i.e., the component is unqualified or erroneously marked unqualified), or
The first model-based assessment identifies an error has occurred a predefined number of times.
Liao fails to explicitly disclose, however, wherein the monitoring is for a robot-assisted first process;
Wherein the process data relates to the first process; and
Wherein operation of the robot is monitored for errors during the first process.
Periaswamy, however, in an analogous field of endeavor, does teach wherein the monitoring is for a robot-assisted first process (0071, Step 502 comprises receiving from a first set of field devices, at least one of image information, thermal image information, infrared image information, heat map information, and thermal array/thermal sensor information);
Wherein the process data relates to the first process (0074, At step 506, responsive to output from the machine learning ensemble representing detection of an anomaly event, information received from a second set of field devices is transmitted to a deep learning architecture implemented within a centralized server); and
Wherein operation of the robot is monitored for errors during the first process (0079, Step 508 comprises generating an anomaly event alert based on output from the deep learning architecture. In a preferred embodiment, the anomaly event alert is generated responsive to output from the deep learning architecture representing detection of an anomaly event or error state based on data received from the second set of field devices, 0043, Fig. 2 illustrates functional components of an edge node implementation of a system 200 for controlling or monitoring process environments within industrial plants).
Liao and Periaswamy are analogous because they are in a similar field of endeavor, e.g., anomaly detection systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the robot process monitoring of Periaswamy in order to provide a means of expanding the capabilities of the machine learning system. The motivation to combine is to increase the efficacy of the machine learning system in detecting anomalies.
Claim 28 is similar in scope to claim 12, and is similarly rejected.
Regarding claim 13, the combination of Liao and Periaswamy teaches the method of claim 12, Liao further teaches wherein the examination criterion depends on an external confirmation (0019, As shown in FIG. 3, at block 551, the image of the component that the machine learning model determines to be unqualified is sent to a visual operation platform of the AOI device, and the image of the machine learning model is inspected by an operator.).
Claim 27 is similar in scope to claim 13, and is similarly rejected.
Regarding claim 16, the combination of Liao and Periaswamy teaches the method of claim 12, and Liao further teaches wherein at least one of the process data or data used in the test assessment are at least one of:
Data of at least one robot by which the first process is performed;
Data of at least one process product of the first process (0012, At block S1, the AOI device detects a component and generates an image of the component. If the image of the component is determined to be qualified, block S8 is implemented. If the image of the component is determined to be unqualified, block S2 is implemented. 0013, At block S2, the image of the unqualified component is collected and processed. Block S2 may be performed by a processor (not shown) of the AOI device or other device having similar functions.); or
At least one of audio or video data of the first process (0012, At block S1, the AOI device detects a component and generates an image of the component. If the image of the component is determined to be qualified, block S8 is implemented. If the image of the component is determined to be unqualified, block S2 is implemented. 0013, At block S2, the image of the unqualified component is collected and processed. Block S2 may be performed by a processor (not shown) of the AOI device or other device having similar functions.).
Regarding claim 17, the combination of Liao and Periaswamy teaches the method of claim 16, and Liao further teaches wherein at least one of:
The data of at least one robot by which the first process is performed are time profiles; or
The data of at least one process product of the first process are image data (0012, At block S1, the AOI device detects a component and generates an image of the component. If the image of the component is determined to be qualified, block S8 is implemented. If the image of the component is determined to be unqualified, block S2 is implemented. 0013, At block S2, the image of the unqualified component is collected and processed. Block S2 may be performed by a processor (not shown) of the AOI device or other device having similar functions.).
Regarding claim 18, the combination of Liao and Periaswamy teaches the method of claim 12, and Periaswamy teaches it further comprising:
Monitoring the robot during the first process and based on the first model-based assessment (0071-0073 Step 502 comprises receiving from a first set of field devices, at least one of image information, thermal image information, infrared image information, heat map information, and thermal array/thermal sensor information, Step 504 comprises processing the received information using a first classifier model comprising a machine learning ensemble … the first classifier model may be configured to detect one or more anomaly events within a process environment).
Liao and Periaswamy are analogous because they are in a similar field of endeavor, e.g., anomaly detection systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the robot process monitoring of Periaswamy in order to provide a means of expanding the capabilities of the machine learning system. The motivation to combine is to increase the efficacy of the machine learning system in detecting anomalies.
Regarding claim 20, the combination of Liao and Periaswamy teaches the method of claim 12, and Liao further teaches wherein monitoring comprises at least one of:
Monitoring at least one robot by which the first process is performed;
Predictive maintenance monitoring of at least one robot by which the first process is performed;
Monitoring for errors in the first process; or
Monitoring for errors in process products of the first process (0012, At block S1, the AOI device detects a component and generates an image of the component. If the image of the component is determined to be qualified, block S8 is implemented. If the image of the component is determined to be unqualified, block S2 is implemented. 0013, At block S2, the image of the unqualified component is collected and processed. Block S2 may be performed by a processor (not shown) of the AOI device or other device having similar functions.).
Regarding claim 21, the combination of Liao and Periaswamy teaches the method of claim 12, and Liao further teaches wherein the examination criterion is predefined in such a way that the first model-based assessment satisfies the examination criterion when the first model-based assessment reveals a specific error (0018, At block S5, a result of determination of the machine learning model is verified. If the accuracy of the machine learning model reaches a predetermined standard, block S6 is implemented. Otherwise, if the accuracy of the machine learning model does not reach the predetermined standard, block S8 is implemented, the machine learning model is optimized and adjusted, and then blocks S4, S5, and S8 are repeated until the accuracy of the machine learning model reaches the predetermined standard.).
Regarding claim 25, Liao discloses a system for monitoring, the system comprising:
Means for:
(a.1) detecting process data (0012, At block S1, the AOI device detects a component and generates an image of the component. If the image of the component is determined to be qualified, block S8 is implemented. If the image of the component is determined to be unqualified, block S2 is implemented. 0013, At block S2, the image of the unqualified component is collected and processed. Block S2 may be performed by a processor (not shown) of the AOI device or other device having similar functions.); and
(a.2) performing a first model-based assessment with the aid of a machine-learned model on the basis of the detected process data (0016, At block S4, the digital image information obtained in block S2 is sent to the machine learning model for training and testing the machine learning model, and letting the machine learning model determine whether the component is unqualified or the component was marked unqualified by error.); and
Means configured to, in response to the first model-based assessment satisfying an examination criterion (0018, At block S5, a result of determination of the machine learning model is verified. If the accuracy of the machine learning model reaches a predetermined standard, block S6 is implemented. Otherwise, if the accuracy of the machine learning model does not reach the predetermined standard, block S8 is implemented, the machine learning model is optimized and adjusted, and then blocks S4, S5, and S8 are repeated until the accuracy of the machine learning model reaches the predetermined standard.), then:
(b.1) performing a test assessment with the aid of a testing authority (0019, As shown in FIG. 3, at block 551, the image of the component that the machine learning model determines to be unqualified is sent to a visual operation platform of the AOI device, and the image of the machine learning model is inspected by an operator.), and
(b.2) further training the machine-learned model on the basis of the test assessment (0019, The result of determination by the machine learning model is compared to a result of determination by the operator, and the accuracy of the machine learning model is calculated. If the accuracy of the machine learning model is lower than a predetermined value, such as 99.99%, it is determined that the result of determination by the machine learning model is inconsistent with the result of determination by the operator, and block S8 is implemented. See also Fig. 1, element S8);
Wherein the model-based assessment satisfies the examination criterion when at least one of:
The first model-based assessment identifies the occurrence in the process data of an error belonging to a group of error types (0016, At block S4, the digital image information obtained in block S2 is sent to the machine learning model for training and testing the machine learning model, and letting the machine learning model determine whether the component is unqualified or the component was marked unqualified by error. Examiner's note: the examiner is interpreting the "determin[ing] whether the component is unqualified or marked unqualified by error" as an identification, i.e., the determination, in the process data, i.e., the digital image information, the occurrence of an error belonging to a group of error types, i.e., the component is unqualified or erroneously marked unqualified), or
The first model-based assessment identifies an error has occurred a predefined number of times.
Liao fails to explicitly disclose, however, wherein the monitoring is for a robot-assisted first process; and
Wherein the process data relates to the first process.
Periaswamy, however, in an analogous field of endeavor, does teach wherein the monitoring is for a robot-assisted first process (0071, Step 502 comprises receiving from a first set of field devices, at least one of image information, thermal image information, infrared image information, heat map information, and thermal array/thermal sensor information); and
Wherein the process data relates to the first process (0074, At step 506, responsive to output from the machine learning ensemble representing detection of an anomaly event, information received from a second set of field devices is transmitted to a deep learning architecture implemented within a centralized server).
Liao and Periaswamy are analogous because they are in a similar field of endeavor, e.g., anomaly detection systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the robot process monitoring of Periaswamy in order to provide a means of expanding the capabilities of the machine learning system. The motivation to combine is to increase the efficacy of the machine learning system in detecting anomalies.
Regarding claim 26, the combination of Liao and Periaswamy teaches the system of claim 25, and Liao further teaches wherein the system further comprises means for:
(c.1) detecting further process data (0020, At block S6, the verified machine learning model is applied in the AOI device, an image of a next component to be inspected is obtained from the AOI device, and the image of the next component to be inspected is processed to obtain new digital image information.);
(c.2) performing a second model-based assessment with the aid of the further trained machine-learned model on the basis of the further detected process data (0020, The new digital image information is input to the machine learning model. The machine learning model determines whether the next component to be inspected is unqualified.); and
(c.3) monitoring for errors during the first process and based on the second model-based assessment (0020, If the result of determination is qualified, block S9 is implemented. If the result of determination is unqualified, block S7 is implemented, and the unqualified component is disposed. In an initial stage of application of the machine learning model in the AOI equipment, the accuracy of the machine learning model can be repeatedly confirmed by an operator. After the accuracy of the machine learning model is verified, the operator can be replaced by the machine learning model.);
Liao fails to teach, however, wherein operation of the robot is monitored for errors during the first process.
Periaswamy, however, in an analogous field of endeavor, does teach wherein operation of the robot is monitored for errors during the first process (0079, Step 508 comprises generating an anomaly event alert based on output from the deep learning architecture. In a preferred embodiment, the anomaly event alert is generated responsive to output from the deep learning architecture representing detection of an anomaly event or error state based on data received from the second set of field devices, 0043, Fig. 2 illustrates functional components of an edge node implementation of a system 200 for controlling or monitoring process environments within industrial plants).
Liao and Periaswamy are analogous because they are in a similar field of endeavor, e.g., anomaly detection systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the robot process monitoring of Periaswamy in order to provide a means of expanding the capabilities of the machine learning system. The motivation to combine is to increase the efficacy of the machine learning system in detecting anomalies.
Claim 19 is rejected under 35 U.S.C. 103 as being obvious over Liao in view of Periaswamy.
Regarding claim 19, the combination of Liao and Periaswamy teaches the method of claim 1, but fails to explicitly teach wherein:
Steps (a.1) and (a.2) are performed at least two times; and then
Steps (b.1) and (b.2) are performed.
The examiner asserts, however, that it would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have modified the combination of Liao and Periaswamy to have included a repetition of steps (a.1) and (a.2), as to do so amounts to mere routine optimization. Specifically, the examiner asserts that a person having ordinary skill in the art would have had a reasonable expectation of success in repeating steps (a.1) and (a.2) before performing steps (b.1) and (b.2), as there is both a design need (i.e., to have the best trained model possible) as well as a finite number of predictable solutions (i.e., a non-zero, positive number of repetitions of steps (a.1) and (a.2)).
Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Liao in view of Periaswamy, and further in view of Jalluri (US 20210011455 A1), hereafter Jalluri.
Regarding claim 14, the combination of Liao and Periaswamy teaches the method of claim 12, and Liao further teaches wherein at least one of:
The testing authority comprises at least one person (0019, As shown in FIG. 3, at block 551, the image of the component that the machine learning model determines to be unqualified is sent to a visual operation platform of the AOI device, and the image of the machine learning model is inspected by an operator.);
The testing authority comprises at least one further machine-learned model;
The testing authority determines at least one parameter.
The combination of Liao and Periaswamy fails to explicitly teach, however, wherein the test assessment comprises a test run of at least one robot by which the first process is performed, the test run being different than the first process.
Jalluri, however, in an analogous field of endeavor, does teach wherein the test assessment comprises a test run of at least one robot by which the first process is performed, the test run being different than the first process (0041, The test mode is a mode in which the autonomous system 110 performs movements without performing permanent operations on the workpiece 174. For example, the controller 134 controls the end effector assembly 118 to repeat the previous movements that had caused the first signals to meet the first criteria. These movements may be performed with or without the workpiece 174, provided the movement is not one that would permanently alter the workpiece 174.).
Liao, Periaswamy, and Jalluri are analogous because they are in a similar field of endeavor, e.g., abnormality detection systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the test mode of Jalluri in order to provide further means of isolating the cause of an abnormality. The motivation to combine is to allow the system to better learn to detect abnormalities.
Regarding claim 15, the combination of Liao, Periaswamy, and Jalluri teaches the method of claim 14, and Jalluri further teaches wherein the test run is different than a second process (0041, The test mode is a mode in which the autonomous system 110 performs movements without performing permanent operations on the workpiece 174. For example, the controller 134 controls the end effector assembly 118 to repeat the previous movements that had caused the first signals to meet the first criteria. These movements may be performed with or without the workpiece 174, provided the movement is not one that would permanently alter the workpiece 174.).
Liao, Periaswamy, and Jalluri are analogous because they are in a similar field of endeavor, e.g., abnormality detection systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the test mode of Jalluri in order to provide further means of isolating the cause of an abnormality. The motivation to combine is to allow the system to better learn to detect abnormalities.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Liao in view of Periaswamy, and further in view of Nowozin et al.("Improved Information Gain Estimates for Decision Tree Induction"), hereafter Nowozin.
Regarding claim 22, the combination of Liao and Periaswamy teaches the method of claim 12, and Periaswamy further teaches wherein:
The examination criterion is predefined in such a way that:
The first model-based assessment satisfies the examination criterion with the aid of the model on the basis of the first detected process data (0083, Step 604 of Fig. 6 thereafter comprises responding to detection of an anomaly event by a first anomaly detector based on a first set of data received from a corresponding first set of field devices, by initializing the process of updating or modifying the configuration of a second anomaly detector implemented at a second edge node), and
The first model-based assessment does not satisfy the examination criterion with the aid of the same model on the basis of second detected process data (0083, Step 604 of Fig. 6 thereafter comprises responding to detection of an anomaly event by a first anomaly detector based on a first set of data received from a corresponding first set of field devices, by initializing the process of updating or modifying the configuration of a second anomaly detector implemented at a second edge node, Examiner's note, in the instance that the first anomaly detector did not detect an abnormality, the process of initializing the updating the second anomaly detector would never be performed, i.e., the examination criteria would not be met).
Liao and Periaswamy are analogous because they are in a similar field of endeavor, e.g., abnormality detection systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the examination criterion of Periaswamy in order to provide further means of refining the machine learning model. The motivation to combine is to ensure that the machine learning model becomes as accurate as possible.
The combination of Liao and Periaswamy fails to teach, however, wherein an expected gain in information during further training of the model on the basis of the first process data is greater than during further training of the model on the basis of the second process data.
Nowozin, however, in an analogous field of endeavor, does teach wherein an expected gain in information during further training of the model on the basis of the first process data is greater than during further training of the model on the basis of the second process data (Page 2, Col. 1, Paragraphs 1-2, information is a popular criterion used to determine the quality of a split, when the information gain criterion is used for recursive tree growing, Algorithm 1 is executed at each node, and the one that achieves the highest estimated information gain is kept.).
Liao, Periaswamy, and Nowozin are analogous because they are in a similar field of endeavor, e.g., machine learning systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the information gain of Nowozin in order to provide a means of better training the machine learned model. The motivation to combine is to ensure that the information gain of the model is maximized.
Claims 23 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Liao in view of Periaswamy, and further in view of Yoshida (US 20190354080 A1), hereafter Yoshida.
Regarding claim 23, the combination of Liao and Periaswamy teaches the method of claim 12, but fails to explicitly teach wherein the model is further trained before step (c.1) additionally on the basis of detected process data without a test assessment being taken into account.
Yoshida, however, in an analogous field of endeavor, does teach wherein the model is further trained before step (c.1) additionally on the basis of detected process data without a test assessment being taken into account (0035, error calculation unit calculates an error between the correlation model M based on the physical quantity and the correlation feature identified from teacher data T, Examiner's note: in the case of supervised learning without direct input from a user/operator, no label acquisition for the data would be done, see also [0028], label data acquisition is used at the time of learning, and need not be an integral component of the device after the learning is completed.).
Liao, Periaswamy, and Yoshida are analogous because they are in a similar field of endeavor, e.g., abnormality detection systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the further teaching data of Yoshida in order to provide further means of classifying a detected error. The motivation to combine is to provide additional information to more accurately perform machine learning.
Regarding claim 24, the combination of Liao, Periaswamy, and Yoshida teaches the method of claim 23, and Yoshida further teaches wherein the model is further trained with the aid of the testing authority (0028, state of the manufacturing machine is confirmed by the operator or maintenance personnel).
Liao, Periaswamy, and Yoshida are analogous because they are in a similar field of endeavor, e.g., abnormality detection systems. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention, with a reasonable expectation of success, to have included the further operator confirmation of Yoshida in order to provide further means of classifying a detected error. The motivation to combine is to provide additional information to more accurately perform machine learning.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BLAKE A WOOD whose telephone number is (571)272-6830. The examiner can normally be reached M-F, 8:00 AM to 4:30 PM Eastern.
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/BLAKE A WOOD/ Examiner, Art Unit 3658
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