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 .
Status of the Claims
Claims 1-3, 5-12 and 14-20 are pending for examination.
Claims 1-3, 5-12 and 14-20 are rejected under 35 U.S.C. §§101, 103.
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 1-3, 5-12 and 14-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As per Claim 1 and 10:
Step 1: Are the Claims to a process, machine, manufacture or composition of matter? Yes.
Step 2A prong 1: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. See the analysis below.
Claim 1, similarly to Claim 10, recites:
A wafer testing machine, used for testing a wafer containing a plurality of dies, and using an artificial intelligent (AI) model to facilitate determination of whether a target die is faulty, the wafer testing machine comprising:
measurement equipment, measuring the dies to generate a measured value of the target die and a plurality of measured values of a plurality of reference dies;
a database, used for storing the measured value of the target die and the measured values of the reference dies;
a storage circuit, used for storing a plurality of program instructions or program codes and storing the Al model configured to test the wafer; and
a computing circuit, coupled to the storage circuit and the database and configured to execute the program instructions or program codes to perform following steps to train the Al model:
determining the target die from the dies;
selecting, based on the target die and a predetermined range, a plurality of reference dies neighboring the target die;
generating a main training data including the measured value of the target die and the measured values of the reference dies;
generating an auxiliary training data indicating whether each reference die is a passed die or a failed die; and
training the Al model using the main training data and the auxiliary training data; wherein the measured value is a Supply Current Quiescent (IDDQ) value.
wherein the AI model, after training, predicts a range of a threshold for the measured value of the target die based on the measured values of the reference dies.
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Regarding the non-emphasized limitations:
Limitations “ determining the target die from the dies;
selecting, based on the target die and a predetermined range, a plurality of reference dies neighboring the target die; ” are directed to Mental Process(es) group of Abstract Idea.
Limitations “generating a main training data including the measured value of the target die and the measured values of the reference dies;
generating an auxiliary training data indicating whether each reference die is a passed die or a failed die; and” are directed to organizing of information and manipulating information through mathematical correlations under Mathematical Relationships group of Abstract Ideas.
Limitations “wherein the AI model, after training, predicts a range of a threshold for the measured value of the target die based on the measured values of the reference dies” are directed to mathematical Calculation group of Abstract Idea.
The Claim is directed to Mathematical Concept(s) group of Abstract Idea.
Step 2A prong 2:
Limitations “measurement equipment, measuring the dies to generate a measured value of the target die and a plurality of measured values of a plurality of reference dies;
a database, used for storing the measured value of the target die and the measured values of the reference dies;
a storage circuit, used for storing a plurality of program instructions or program codes and storing the Al model configured to test the wafer; and
a computing circuit, coupled to the storage circuit and the database and configured to execute the program instructions or program codes to perform following steps to train the Al model:
wherein the measured value is a Supply Current Quiescent (IDDQ) value.” are directed extra solution activity and/or applying the abstract idea using generic computer component. See MPEP 2106.05(g).
Limitation “training the Al model using the main training data and the auxiliary training data;” are directed to Mere Instructions to Apply an Exception. See MPEP 2106.05(f).
The Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that integrate the Judicial Exception into a practical application? No.
The emphasized limitations, “measurement equipment, measuring the dies to generate a measured value of the target die and a plurality of measured values of a plurality of reference dies;
a database, used for storing the measured value of the target die and the measured values of the reference dies;
a storage circuit, used for storing a plurality of program instructions or program codes and storing the Al model configured to test the wafer; and
a computing circuit, coupled to the storage circuit and the database and configured to execute the program instructions or program codes to perform following steps to train the Al model:” are directed to well-understood, routine and conventional activity. See MPEP 2106.05(d).
Limitations “wherein the measured value is a Supply Current Quiescent (IDDQ) value” are directed to well-understood, routine and conventional activity. See MPEP 2106.05(d). Search query L48 reveals 106 results related to common or well-known quiescent current supply testing.
The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
As per Claims 2 and 11:
Step 1: Are the Claims to a process, machine, manufacture or composition of matter? Yes.
Step 2A prong 1: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. See the analysis below.
Claim 2, similarly to Claim 11, recites:
wherein the Al model comprises a feature extraction algorithm and a machine learning algorithm model.
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Regarding the non-emphasized limitations:
“wherein the Al model comprises a feature extraction algorithm and a machine learning algorithm model”.
These steps are directed to organizing of information and manipulating information through mathematical correlations under Mathematical Relationships group of Abstract Ideas.
Step 2A prong 2: the claim does not have any additional elements.
Step 2B: Does the Claim recite additional elements that integrate the Judicial Exception into a practical application? No.
There are no additional elements.
The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
As per Claims 3 and 12:
Step 1: Are the Claims to a process, machine, manufacture or composition of matter? Yes.
Step 2A prong 1: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. See the analysis below.
Claim 3, similarly to Claim 12, recites:
wherein the machine learning algorithm model is selected from a group consisting of Bayesian Ridge Regression algorithm, Gaussian Process Regression algorithm and scalable variational Gaussian process algorithm
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Regarding the non-emphasized limitations:
“wherein the machine learning algorithm model is selected from a group consisting of Bayesian Ridge Regression algorithm, Gaussian Process Regression algorithm and scalable variational Gaussian process algorithm.”
These steps are directed to organizing of information and manipulating information through mathematical correlations under Mathematical Relationships group of Abstract Ideas.
Step 2A prong 2: the claim does not have any additional elements.
Step 2B: Does the Claim recite additional elements that integrate the Judicial Exception into a practical application? No.
There are no additional elements.
The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
As per Claims 5 and 14:
Step 1: Are the Claims to a process, machine, manufacture or composition of matter? Yes.
Step 2A prong 1: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. See the analysis below.
Claim 5, similarly to Claim 14, recites:
wherein the auxiliary training data is a first auxiliary training data, and the computing circuit further performs following steps:
generating a second auxiliary training data indicating whether at least one of the target die and the reference dies exists; and
training the Al model using the second auxiliary training data together with the main training data and the first auxiliary training data.
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Regarding the non-emphasized limitations:
“wherein the auxiliary training data is a first auxiliary training data, and the computing circuit further performs following steps:
generating a second auxiliary training data indicating whether at least one of the target die and the reference dies exists; and
training the Al model using the second auxiliary training data together with the main training data and the first auxiliary training data.”
These steps are directed to organizing of information and manipulating information through mathematical correlations under Mathematical Relationships group of Abstract Ideas.
Step 2A prong 2: the claim does not have any additional elements.
Step 2B: Does the Claim recite additional elements that integrate the Judicial Exception into a practical application? No.
There are no additional elements.
The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
As per Claims 6 and 15:
Step 1: Are the Claims to a process, machine, manufacture or composition of matter? Yes.
Step 2A prong 1: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. See the analysis below.
Claim 6, similarly to Claim 15, recites:
wherein the auxiliary training data further indicates whether the reference dies exist
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Regarding the non-emphasized limitations:
wherein the auxiliary training data further indicates whether the reference dies exist
These steps are directed to organizing of information and manipulating information through mathematical correlations under Mathematical Relationships group of Abstract Ideas.
Step 2A prong 2: the claim does not have any additional elements.
Step 2B: Does the Claim recite additional elements that integrate the Judicial Exception into a practical application? No.
There are no additional elements.
The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
As per Claims 7 and 16:
Step 1: Are the Claims to a process, machine, manufacture or composition of matter? Yes.
Step 2A prong 1: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. See the analysis below.
Claim 7, similarly to Claim 16, recites:
wherein the main training data and the auxiliary training data correspond to a combination of a temperature and a voltage
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Regarding the non-emphasized limitations:
wherein the main training data and the auxiliary training data correspond to a combination of a temperature and a voltage
These steps are directed to organizing of information and manipulating information through mathematical correlations under Mathematical Relationships group of Abstract Ideas.
Step 2A prong 2: the claim does not have any additional elements.
Step 2B: Does the Claim recite additional elements that integrate the Judicial Exception into a practical application? No.
There are no additional elements.
The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
As per Claims 8 and 17:
Step 1: Are the Claims to a process, machine, manufacture or composition of matter? Yes.
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. See the analysis below.
Claim 8, similarly to Claim 17, recites:
wherein the main training data and the auxiliary training data correspond to a plurality of combinations of a plurality of temperatures and a plurality of voltages
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Regarding the non-emphasized limitations:
wherein the main training data and the auxiliary training data correspond to a plurality of combinations of a plurality of temperatures and a plurality of voltages
These steps are directed to organizing of information and manipulating information through mathematical correlations under Mathematical Relationships group of Abstract Ideas.
Step 2A prong 2: the claim does not have any additional elements.
Step 2B: Does the Claim recite additional elements that integrate the Judicial Exception into a practical application? No.
There are no additional elements.
The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
As per Claims 9 and 18:
Step 1: Are the Claims to a process, machine, manufacture or composition of matter? Yes.
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. See the analysis below.
Claim 9, similarly to Claim 18, recites:
wherein the main training data and the auxiliary training data are a matrix or an array, and relative positions of a plurality of elements in the matrix or the array correspond to relative positions on the wafer of the target die and the reference dies
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Regarding the non-emphasized limitations:
There are no non-emphasized limitations
These steps are directed to organizing of information and manipulating information through mathematical correlations under Mathematical Relationships group of Abstract Ideas.
Step 2A prong 2: “wherein the main training data and the auxiliary training data are a matrix or an array, and relative positions of a plurality of elements in the matrix or the array correspond to relative positions on the wafer of the target die and the reference dies” is extra solution activity. The additional limitation(s) does not integrate the judicial exception into a practical application .
Step 2B: Does the Claim recite additional elements that integrate the Judicial Exception into a practical application? No.
wherein the main training data and the auxiliary training data are a matrix or an array, and relative positions of a plurality of elements in the matrix or the array correspond to relative positions on the wafer of the target die and the reference dies
These limitations are insignificant extra-solution activities. Therefore, they do not make the Claims significantly more than the Abstract Idea. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
As per Claims 19 and 20:
Step 1: Are the Claims to a process, machine, manufacture or composition of matter? Yes.
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. See the analysis below.
Claim 19, similarly to Claim 20, recites:
“wherein the AI model is a deep learning algorithm model, and the deep learning algorithm model comprises a Convolutional Neural Network (CNN) algorithm model and a Mixture Density Neural Network (MDNN) algorithm model.”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Regarding the non-emphasized limitations:
There are no non-emphasized limitations
These steps are directed to organizing of information and manipulating information through mathematical correlations under Mathematical Relationships group of Abstract Ideas.
Step 2A prong 2: “wherein the AI model is a deep learning algorithm model, and the deep learning algorithm model comprises a Convolutional Neural Network (CNN) algorithm model and a Mixture Density Neural Network (MDNN) algorithm model” is Mere Instruction to Apply an Exception. See MPEP 2106.05(f). The additional limitation(s) does not integrate the judicial exception into a practical application .
Step 2B: Does the Claim recite additional elements that integrate the Judicial Exception into a practical application? No.
There is no additional limitations to consider in this step.
The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 5-6, 9-12, 14-15 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rosa (U.S. 2014/0244548 hereinafter Rosa) in view of Chan et al. (U.S. 2008/0206903 hereinafter Chan).
As Claim 1, Rosa teaches a wafer testing machine, used for testing a wafer containing a plurality of dies, and using an artificial intelligent (AI) model to facilitate determination of whether a target die is faulty, the wafer testing machine comprising:
measurement equipment, measuring the dies to generate a measured value of the target die and a plurality of measured values of a plurality of reference dies; (Rosa (¶0019 line 9-17, ¶0031 last 7 lines), testing equipment inserts probes into each die for testing. );
a database, used for storing the measured value of the target die and the measured values of the reference dies; (Rosa (¶0029 line 1-8), training data is stored with corresponding classification);
a storage circuit, used for storing a plurality of program instructions or program codes and storing the AI model configured to test the wafer (Rosa (¶0049 last 4 lines), main memory); and
a computing circuit, coupled to the storage circuit and the database and configured to execute the program instructions or program codes to perform following steps to train the Al model (Rosa (¶0049 line 4-5), central processor):
determining the target die from the dies (Rosa (¶0045 line 1-5), target die such as die 310 is selected);
selecting, based on the target die and a predetermined range, a plurality of reference dies neighboring the target die (Rosa (¶0045), system selects neighboring dies with target die 210);
generating a main training data including the measured value of the target die (Rosa (¶0029 line 1-8), training data is stored with corresponding classification) and the measured values of the reference dies (Rosa (¶0020 line 1-11, ¶0045 lines 2-11), the measured values are generated for test dies. Feature vectors include a pass/fail/missing indication for one or more nearest neighbor of the dies. The classification of the neighboring die is provided during the initial testing of the die by wafer probe);
generating an auxiliary training data indicating whether each reference die is a passed die or a failed die (Rosa (¶0045), feature vector x(i) includes pass/fail status of adjacent dies. The classification (pass/fail) of the neighboring die is provided during the initial testing of the die by wafer probe); and
training the Al model using the main training data and the auxiliary training data (Rosa (¶0045 line 1-3), system trains prediction model with nearest neighbor elimination method);
Rosa may not explicitly disclose:
wherein the measured value is a Supply Current Quiescent (IDDQ) value.
wherein the AI model, after training, predicts a range of a threshold for the measured value of the target die based on the measured values of the reference dies.
Chan teaches:
wherein the measured value is a Supply Current Quiescent (IDDQ) value (Chan (¶0042, fig. 6 item 606), if the selected parameter is quiescent current, the adaptive thresholds will be quiescent current thresholds).
wherein the AI model, after training (Chan (¶0036 line 1-3), method for testing a semiconductor wafer. The testing is construed as a step after AI model training), predicts a range of a threshold for the measured value of the target die based on the measured values of the reference dies (Chan (¶0036 line 1-6, fig. 5 item 504, 506, ¶0038 last 5 lines fig. 5 item 508), a parameter is measured for each die in the selected region. An adaptive threshold is generated for the selected region. A threshold is a range because it defines a range of number which decides pass/fail status of the die).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify testing parameters of Rosa instead be testing parameters taught by Chan, with a reasonable expectation of success. The motivation would be to allow “semiconductor dies of the water are qualified based on an adaptive threshold that varies according to the wafer region under test”, solve the problem of “process variations in the formation of the wafer” and provide “an improved technique for detecting failed dies” (Chan (¶0002 last 5 lines, ¶0009 line 7-10)).
As Claim 2, besides Claim 1, Rosa in view of Djuric teaches wherein the Al model comprises a feature extraction algorithm (Rosa (¶0039 last 6 lines), feture vectos x(i) is extracted) and a machine learning algorithm model (Rosa (¶0029 line 11-13), machine learning model is trained with training data).
As Claim 3, besides Claim 2, Rosa in view of Djuric teaches wherein the machine learning algorithm model is selected from a group consisting of Bayesian Ridge Regression algorithm, Gaussian Process Regression algorithm and scalable variational Gaussian process algorithm (Rosa (¶0041), machine learning model implement Gaussian function).
As Claim 5, besides Claim 1, Rosa in view of Djuric teaches wherein the auxiliary training data is a first auxiliary training data (Rosa (¶0045), feature vector x(i) includes pass/fail status of adjacent dies), and the computing circuit further performs following steps:
generating a second auxiliary training data indicating whether at least one of the target die and the reference dies exists (Rosa (¶0046 line 1-7), dies on the edge of wafer is categorized as unknown); and
training the Al model using the second auxiliary training data together with the main training data and the first auxiliary training data (Rosa (¶0045 line 1-3), system trains prediction model with nearest neighbor elimination method).
As Claim 6, besides Claim 1, Rosa in view of Djuric teaches wherein the auxiliary training data further indicates whether the reference dies exist (Rosa (¶0046 line 1-7), dies on the edge of wafer is categorized as unknown).
As Claim 9, besides Claim 1, Rosa in view of Djuric teaches wherein the main training data and the auxiliary training data are a matrix or an array, and relative positions of a plurality of elements in the matrix or the array correspond to relative positions on the wafer of the target die and the reference dies (Rosa (¶0018, fig. 2A), dies are arranged into array).
As Claim 10, the Claim is rejected for the same reasons as Claim 1.
As Claim 11, the Claim is rejected for the same reasons as Claim 2.
As Claim 12, the Claim is rejected for the same reasons as Claim 3.
As Claim 14, the Claim is rejected for the same reasons as Claim 5.
As Claim 15, the Claim is rejected for the same reasons as Claim 6.
As Claim 18, the Claim is rejected for the same reasons as Claim 9.
Claim(s) 7-8 and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rosa in view of Chan in further view of Itoyama (U.S. 5,510,724 hereinafter Itoyama).
As Claim 7, besides Claim 1, Rosa in view of Chan does not explicitly disclose:
wherein the main training data and the auxiliary training data correspond to a combination of a temperature and a voltage.
Itoyama teaches:
wherein the main training data and the auxiliary training data correspond to a combination of a temperature and a voltage (Itoyama (col. 14 line 45-51), burn-in tests are used for testing both voltage and temperature).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify testing probes of Rosa in view of Chan instead be a voltage and/or temperature method taught by Itoyama, with a reasonable expectation of success. The motivation would be to detect in advance the intrinsic defects of semiconductor chips (Itoyama (col. 14 lines 45-51)).
As Claim 8, besides Claim 1, Rosa does not explicitly disclose:
wherein the main training data and the auxiliary training data correspond to a plurality of combinations of a plurality of temperatures and a plurality of voltages.
Itoyama teaches:
wherein the main training data and the auxiliary training data correspond to a plurality of combinations of a plurality of temperatures and a plurality of voltages (Itoyama (col. 14 line 45-51), burn-in tests are used for testing both voltages and temperatures).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify testing probes of Rosa in view of Djuric instead be a voltage and/or temperature method taught by Itoyama, with a reasonable expectation of success. The motivation would be to detect in advance the intrinsic defects of semiconductor chips (Itoyama (col. 14 lines 45-51)).
As Claim 16, the Claim is rejected for the same reasons as Claim 7.
As Claim 17, the Claim is rejected for the same reasons as Claim 8.
Claim(s) 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rosa in view of Chan in further view of Djuric et al. (U.S. 2019/0049970 hereinafter Djuric).
As Claim 19, besides Claim 1, Rosa in view of Chan does not explicitly disclose:
wherein the Al model is a deep learning algorithm model, and the deep learning algorithm model comprises a Convolutional Neural Network (CNN) algorithm model and a Mixture Density Neural Network (MDNN) algorithm model
Djuric teaches:
wherein the Al model is a deep learning algorithm model, and the deep learning algorithm model comprises a Convolutional Neural Network (CNN) algorithm model (Djuric (¶0072), convolution neural network) and a Mixture Density Neural Network (MDNN) algorithm model (Djuric (¶0092), Mixture Density Network)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify machine learning model of Rosa in view of Chan instead be a training model taught by Djuric, with a reasonable expectation of success. The motivation would be to leverage already developed prediction model (Djuric (¶0053)).
As Claim 20, the Claim is rejected for the same reasons as Claim 19.
Response to Arguments
Response to 35 U.S.C. §101 Rejections:
Applicants argue that “this application does not use fixed IDDQ threshold to determine whether a die is faulty; therefore, the claimed invention are not well-understood, routine and conventional application” (second paragraph of page 7 in the remarks).
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Applicants’ arguments are not persuasive because the limitation(s) do not improve and/or integrate the abstract idea into a practical application. Measuring IDDQ value of a die is well-understood, routine and conventional activity based on search query L48 of the search notes.
Response to 35 U.S.C. §112 Rejection:
Applicants amended the Claims; therefore, current 35 U.S.C. §112 rejection(s) are respectfully withdrawn.
Response to 35 U.S.C. §§102-103(b) Rejection:
As Claim 1 and 10, Applicants argue that Rosa does not disclose “Supply Current Quiescent (IDDQ) value” (last paragraph of page 12 in the remarks).
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Applicants’ arguments are moot because new reference Chan teaches the limitation(s). See the current rejections for details.
As Claim 7 and 11, Applicants argue that current application relates to “a range of a threshold” (first and third paragraph of page 16 in the remarks).
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Applicants’ arguments are not persuasive because new reference Chan teaches the limitation(s). Further clarification of “a range of a threshold” might advance the prosecution. See the current rejections for details.
Other independent/dependent Claims are not allowable for the same reasons above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NHAT HUY T NGUYEN whose telephone number is (571)270-7333. The examiner can normally be reached M-F: 12:00-8:00 EST.
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/NHAT HUY T NGUYEN/Primary Examiner, Art Unit 2147