DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is responsive to the communication received on 08/29/2023. The claims 1- 20 are pending, of which the claim(s) 1, 13, & 20 is/are in independent form.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to Judicial Exception (“abstract idea”) without significantly more.
As to claim 1
1. A method comprising:
receiving, by a batch analytic system, batch data of a batch generated in an industrial process, wherein the batch data includes K samples collected during the batch and each sample includes J values corresponding to J process variables of the industrial process;
applying, by the batch analytic system and for each process variable among the J process variables of the industrial process, a first function to K values of the process variable in the K samples of the batch to determine a first feature value of the process variable for the batch;
aggregating, by the batch analytic system, first feature values corresponding to the J process variables that are determined for the batch using the first function to form a batch representation of the batch; and
performing, by the batch analytic system, an operation using the batch representation of the batch.
1. Step 1: Yes. The claim is to a process, which is one of the four categories of patent eligible subject matter.
2. Step 2A, Prong 1: Yes. The claim(s) recite(s) limitations of “applying
aggregating,
performing,
In light of applicant’s specification, these limitations encompass an abstract idea based exception because they can be practically performed in human’s mind but for the recitation of the generic computer, namely a batch analytic system. See Spec, para. 10226 & fig. 9. Hence these limitations shown above without bold emphasis are Mental Processes that can be performed via evaluation and judgement to the received batch data in human’s mind. Put differently, other than reciting “by the batch analytic system” nothing in these limitations preclude the steps from practically being performed in the mind.
For example, applicant’s drawings in fig. 16 and associated text and dependent claims 3- 4 show that applying a first function, aggregating the first features obtained with applying of the first function covers performing any statistical measures such as calculating statistical average/median to the values from each of the rows of a matrix of the received batch data for samples of each variable and generating a column/feature vector. Here, the claim is broad enough to cover a small sample size such as “ten” samples and “two” process variables. Human mind can practically perform average or other statistical operations for such small batch size datasets of 10 samples and two variables thereby calculating two average values. The listing of both average values via concatenation the claimed “aggregating” can be implemented in light of applicant’s specification. These steps are simple mathematical procedures that can be easily performed in human’s mind at most with the aid of pen and paper.
The last limitation of “performing, is broad enough to cover every possible operations including performing mere some further calculation to the aggregated first feature values and hence can be practically performed in human’s mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components as in this case for “a batch analytic system” implemented by a generic computer, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
3. Step 2A, Prong 2: No. This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
(1) “receiving, by a batch analytic system, batch data of a batch generated in an industrial process, wherein the batch data includes K samples collected during the batch and each sample includes J values corresponding to J process variables of the industrial process” and
(2) using of “the batch analytic system” (a generic computer) to automate applying, aggregating, and performing steps.
The receiving step is recited at very high level of generality as part of data gathering step using a generic computer such that it amounts no more than adding a pre-solution insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). That is, 1st additional element is akin to mere adding an insignificant extra-solution activity.
The using of “by a batch analytic system” is also recited at very high level of generality such that it amounts no more than using a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). The 2nd additional element is akin to using computer as a tool to execute the abstract idea.
The individual or combination of additional elements when viewed the claim as a whole fail to integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the above abstract idea. That is, when viewed the claim as a whole, the claim continue to cover only using a generic computer as a tool to perform an abstract idea by using insignificant extra-solution activity. The claim is directed to an abstract idea.
4. Step 2B: No. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the 1st additional elements of receiving step is akin to adding an insignificant extra-solution activity. Furthermore, this additional element is well-understood, routine, conventional activity and examiner takes an Official notice to that effect by relying on the cited prior arts2 as evidence for Berkheimer memo. As stated above, the batch analytic system is merely utilized to automate the receiving step and other mental processes steps. Accordingly, the additional elements when considered separately and in the ordered combination still do not add significantly more (also known as an “inventive concept”) to the exception. The claim is not patent eligible under 101.
As to claim 13
13. A system comprising:
a memory storing instructions; and
a processor communicatively coupled to the memory and configured to execute the instructions to:
receive batch data of a batch generated in an industrial process, wherein the batch data includes K samples collected during the batch and each sample includes J values corresponding to J process variables of the industrial process;
apply, for each process variable among the J process variables of the industrial
process, a first function to K values of the process variable in the K
samples of the batch to determine a first feature value of the process
variable for the batch;
aggregate first feature values corresponding to the J process variables that are
determined for the batch using the first function to form a batch
representation of the batch; and
perform an operation using the batch representation of the batch.
1. Step 1: Yes. The claim is to a process/system with a processor and a memory, which is one of the four categories of patent eligible subject matter.
2. Step 2A, Prong 1: Yes. The claim recites the limitations of “apply, for each process variable among the J process variables of the industrial process, a first function to K values of the process variable in the K samples of the batch to determine a first feature value of the process variable for the batch;
aggregate first feature values corresponding to the J process variables that are
determined for the batch using the first function to form a batch representation of the batch; and perform an operation using the batch representation of the batch”.
These limitations, as drafted under BRI, are considered an abstract idea based exception because they can be practically performed in human’s mind (“mental processes”) but for the recitation of generic computer processor and a memory recited in lines 2- 3 of the claim for the similar reasons set forth above in claim 1. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
3. Step 2A, Prong 2: No. This judicial exception is not integrated into a practical application. In particular, the claim recites the additional element(s) of:
(1) a memory storing instructions; and a processor communicatively coupled to the memory and configured to execute the instructions to:
(2) receive batch data of a batch generated in an industrial process, wherein the batch data includes K samples collected during the batch and each sample includes J values corresponding to J process variables of the industrial process;
Here, both additional elements are recited at very high level of generality as part of using a computer to automate the method steps and data gathering step for the batch data. Thus, the first additional element is akin to merely using a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) and the 2nd additional element of receiving batch data is akin to adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). The individual/combination of additional elements fail to integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the above abstract idea. The claim is directed to an abstract idea.
4. Step 2B: No. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the 1st additional element of “a memory” and “a processor” is akin to using a computer as a tool to perform an abstract idea and the 2nd additional element (receiving step) amounts no more than adding insignificant extra-solution activity to the judicial exception. Furthermore, the receiving step is well-understood, routine, conventional activity and examiner takes an Official notice to that effect by relying on the cited prior arts for Berkheimer memo. Accordingly, the additional elements when considered separately and in combination do not add significantly more (also known as an “inventive concept”) to the exception. The claim is not patent eligible under 101.
As to claim 20, this claim is to “A non-transitory computer-readable medium” but recites the similar subject matter as that of the claims 1 & 13. Therefore, this claim also recites similar abstract idea and additional elements set forth above in claim 13. Therefore, this claim is also not patent eligible under 101 for the same rationale set forth above in claim 13.
Regarding claims 2-11, these claims directly or indirectly depend on claim 1, and therefore recite the similar abstract idea and additional elements as in claim 1. These claims 2- 11 require additional limitations. However, these additional limitations also can be practically performed in human’s mind via observation, evaluation, judgment, opinion at most with the aid of pen and paper hence still are “Mental Processes”. That is, in light of applicant’s specification under BRI, new limitations as drafted, in the claims 2- 11 cover performance of the limitations in the mind but for the recitation of the generic computer element, namely the batch analytic system. As to claim 5, the training of one or more machine learning models using the batch representation cover generating and modifying liner regression (trend line) based mathematical equation hence can be done in human’s mind via observation and using the judgement. Therefore, the claims 2- 11 also fail to provide a practical application in Step 2A, Prong 1 and an inventive concept in Step 2B. The claims 2- 11 are not patent eligible.
Regarding claims 12, the claim depends on claim 7 and recite the same abstract idea and additional elements of the claim 7 set forth above. The claim further recites “configuring, .”
However this limitation also can be practically performed in human’s mind and still abstract. The limitation of “implementing, by the batch analytic system, a machine learning model to perform a batch analytic operation for one or more batches generated in the industrial process” is an additional element. However, this limitation is also recited at very high level of generality of generating/implementing a machine learning model” for the batch data from batches generated in the industrial process. This limitation amounts to generally linking the use of the judicial exception to a particular technological environment or field of use (namely machine learning field for the collected data) – see MPEP 2106.05(h). The mere linking the abstract idea into a particular technological field itself is not sufficient to provide a practical application and an inventive concept. Upon considering additional elements together when viewed the claim as a whole, the claim continues to fail to provide a practical application and an inventive step. The claim is not patent eligible under 101.
Regarding claims 14-18, these claims, directly and indirectly depend on claim 13 and therefore recite the same abstract idea and additional elements discussed above in claim 13. The claims 14- 18 introduce new additional limitations. These newly added limitations, in light of applicant’s specification, also can be practically performed in human’s mind hence still abstract. That is, the claims 14- 18 do not provide new additional elements to the claim 13. Therefore, the claims 14- 18 also fail to provide a practical application and an inventive step. The claims 14- 18 are not patent eligible under 101.
Regarding claim 19, the claim depends on claim 15 and hence recites the same abstract idea and additional elements as that of the claim 15. The claim also recites the limitation of “configure the machine learning model to assign higher weight values to one or more elements that correspond to the one or more functions and the particular process variable as compared to other elements in a batch representation of each batch”. However, this limitation in light of applicant’s specification also can be performed in human’s mind hence still abstract. The limitation of “implement a machine learning model to perform a batch analytic operation for one or more batches generated in the industrial process” is an additional element. However, this limitation amounts no more than generally linking the use of the judicial exception to a particular technological environment or field of use (in the machine learning to the industrial batch data) – see MPEP 2106.05(h). Therefore, even when viewed the claim as a whole, the claim fail to provide a practical application and an inventive concept. The claim is not patent eligible under 101.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 5, 13, & 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yelchuru (US 20100017008 A1) in view of Shayovitz (US 20160313259 A1).
Regarding claim 1, Yelchuru teaches a method comprising:
receiving, by a batch analytic system [“control system 100 comprises a server 102, an operator computing system (OCS) 104”], batch data [“batch process data representing sets of samples s.sub.0, . . . , s.sub.S for each variable v.sub.0, . . . ,v.sub.J”, “archived data can include data obtained during actual runs of a batch process” wherein “the three way array of the archived data is scaled and unfolded into a two way array of scaled archived data”. The two way array can be understood as a matrix as shown in fig. 6B] of a batch generated in an industrial process [“industrial equipment 110”], wherein the batch data includes K samples collected during the batch and each sample includes J values [“archived data can further include a plurality of sets of samples S.sub.0, . . . , s.sub.S for each variable v.sub.0, . . . , v.sub.J that represent values”] corresponding to J process variables [variables like pressure, temperature for the industrial equipment] of the industrial process (Figs. 1, 6- 7, [027-030, 037-039, 041-044]);
…
forming a batch representation [“sets of samples s.sub.0,. . . , s.sub.S can be used to build multivariate statistical models” for FPR0] of the batch; and performing [using of the PCA or PLS models], by the batch analytic system, an operation using the batch representation of the batch ([034, 037, 056]).
Yelchuru’s analytic system populates batch data for each sample in a matrix form (two way arrows for each FPR as in Fig. 6B having variables V0 to Vj for time S0 to Ss) having pluralities of the rows and columns facilitated by timestamps (para. 030). However, Yelchuru does not teach applying a first function (e.g., an average or standard deviation to each row—see claim 3-4) to generate a first feature (e.g., average value) and aggregating the first feature (e.g., average, standard deviation) values of each rows to generate its batch representation3. That is, Yelchuru may not teach:
applying, by the batch analytic system and for each process variable among the J process variables of the industrial process, a first function to K values of the process variable in the K samples of the batch to determine a first feature value of the process variable for the batch;
aggregating, by the batch analytic system, first feature values corresponding to the J process variables that are determined for the batch using the first function to form a batch representation of the batch as claimed. That is, Yelchuru’s batch representation is not produced by aggregating first feature values generated by applying a first function.
Shayovitz teaches a system and method to analyze datasets (collected from sensors 130, 135, analogous to batch data of Yelchuru) of the monitored materials under test 140 in an industrial process [“the substance is milk”] ([060-061, 080], fig. 1). Shayovitz further teaches the system using a computer processor [Fig. 1, acquisition subsystem 150+ processing unit 160] for arranging the collected datasets [“the RF signals.. time domain signals”] in a matrix form [“in the form of matrix R of dimension M×N where M relates to the object's data points and N to the number of samples”] and converting the M x N matrix into a column vector (aggregating/concatenating or listing in a vector/list form) batch representation ([0110, 0112-013]). Specifically, Shayovitz teaches method steps comprising: (fig. 6);
applying [performing average to each row for the matrix], by the batch analytic system and for each process variable among the J process variables [different rows] of the industrial process, a first function [taking average of each row] to K values [values of each row] of the process variable in the K samples of the batch to determine a first feature value of the process variable for the batch; aggregating [generating column vector from each average of each row], by the batch analytic system, first feature values corresponding to the J process variables that are determined for the batch using the first function to form a batch representation [“average of each row of the matrix R may be denoted as column vector μ.”] of the batch; and performing, by the batch analytic system, an operation [“In step 640 each column of the matrix R is subtracted to provide a new matrix C. In step 650 transformation steps are initialized”] using the batch representation of the batch ([0113-0115], Fig. 6).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Shayovitz and Yelchuru because they both related to a batch analytic system performing multivariate statistical analysis to the pluralities of datasets formed in a M x N matrix to generate batch representation and (2) have the method/system of Yelchuru to include missing applying and aggregating steps as in Shayovitz and generate a column vector that includes values of averages of each of the rows. Doing so even if the batch data has very large data size would help to reduce the dimension of the parameter spaces (with K samples of J variables) of the large matrix of the batch data while extracting most of the information out of the matrix during generation of the batch representation thereby lowering the computational burdens (Shayovitz, [0113]). Accordingly, when the batch data of Yelchuru are converted to a column vector (representing average values of each variable) as in Shayovitz to lower the computational burden, the modified Yelchuru will teach each of the limitations of the claim and renders invention thereof obvious to PHOSITA.
Regarding claim 5, Yelchuru in view of Shayovitz teaches the method of claim 1, wherein performing the operation using the batch representation of the batch includes one or more of:
generating one or more principal component analysis (PCA) models [“a PCA (Principal Component analysis) transformation is used”] of the industrial process using the batch representation of the batch; or training one or more machine learning models using the batch representation of the batch (Yelchuru [05-056] & Shayovitz [0113]).
Regarding claim 13, Yelchuru in view of Shayovitz teaches/suggests invention of this system claim for the similar reasons set forth above in method claim 1.
Please note that the computer (server 102+ operator computing system 104 of Yelchuru’s fig. 1 or items 150-160 of Shayovitz) used by the Yelchuru is mapped with the claimed “A system comprising: a memory storing instructions; and a processor communicatively coupled to the memory”. Put differently, “a batch analytic system” of claim 1 is interpreted having similar patentable scope as claimed “A system comprising: a memory storing instructions; and a processor communicatively coupled to the memory”.
Regarding claim 20, Yelchuru in view of Shayovitz teaches/suggests invention of the claimed “A non-transitory computer-readable medium” for the similar reasons set forth above in claim 1.
Claim(s) 2- 4 & 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yelchuru in view of Shayovitz as applied to claim 1 above, and further in view of Glusman et al. (US 20200395095 A1). The combination of Yelchuru, Shayovitz, and Glusman is referred as YSG hereinafter.
Regarding claim 2, Yelchuru in view of Shayovitz teaches the method of claim 1 as outlined above.
While Yelchuru in view of Shayovitz teaches generating first feature values (averages of each row), it does not teach also using of the second feature values (e.g., variance). That is, Yelchuru in view of Shayovitz may not teach: applying, by the batch analytic system and for each process variable among the J process variables of the industrial process, a second function to the K values of the process variable in the K samples of the batch to determine a second feature value of the process variable for the batch, wherein the second function is different from the first function as claimed.
Glusman teaches a system and method comprising: receiving, by a batch analytic system, a matrix data having pluralities of rows and columns representing various variables obtained from pluralities of the sensors (). Specifically, Glusman teaches a method comprising:
applying, by the batch analytic system and for each process variable among the J process variables of the industrial process, a second function [“involves computing the average and standard deviation for each row in the matrix (blocks 408 and 410, respectively). Thereafter, the average value for each row is subtracted from each value in the row, and standard deviation for each row is divided into each value in the row (block 412)”] to the K values of the process variable in the K samples of the batch to determine a second feature value of the process variable for the batch, wherein the second function is different from the first function [“the average value for each row” is different to the “standard deviation”] ([069], Fig. 5).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Glusman and Yelchuru in view of Shayovitz because they both related to a computing device processing sensor datasets in the matrix form and (2) modify the aggregation step of Yelchuru in view of Shayovitz to additionally apply a second function (e.g., standard deviation) in addition to the first function (average) for each row of the matrix of the batch data. Doing so would allow to compute a dimensionless Z-score for the received batch data of Yelchuru to help understand how many standard deviations a specific data point of the batch data is away from the mean of that dataset as can be clear to PHOSITA (Glusman, [0069]).
Regarding claim 3, YSG teaches the method of claim 2, wherein aggregating the first feature values corresponding to the J process variables includes: aggregating the first feature values corresponding to the J process variables that are determined for the batch using [using both the average and standard deviation] the first function and second feature values corresponding to the J process variables that are determined for the batch using the second function to form the batch representation of the batch (Yelchuru [034], Shayovitz [0113], Glusman, [069]).
Regarding claim 4, YSG teaches the method of claim 2, wherein: the first function and the second function are configured to determine two of a mean value, a standard deviation value, a root mean square value, a median value, a length value, a frequency value, a maximum value, a minimum value, a variation coefficient value, a variance value, a skewness value, a kurtosis value, an absolute sum of changes, a longest strike below mean, a longest strike above mean, or a count above mean (Glusman [069] & Shayovitz [0113]).
Regarding claim 14, YSG teaches/suggests invention of this claim for the similar reasons set forth above in claim 2.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yelchuru in view of Shayovitz as applied to claim 1 above, and further in view of Blevins et al. (US 20110288837 A1).
Regarding claim 6, Yelchuru in view of Shayovitz teaches the method of claim 1 comprising performing, by the batch analytic system, an operation using the batch representation of the batch as discussed above.
However, Yelchuru in view of Shayovitz may not teach wherein performing the operation using the batch representation of the batch includes one or more of:
Claim 6: “determining an anomaly metric of the batch using the batch representation of the batch and a PCA model of the industrial process; or providing the batch representation of the batch to a machine learning model as an input.”
Blevins relates to a batch analytic system [“the OMS 102”] receiving batch data of a batch generated in an industrial process, forming a batch representation [e.g., “process operation by statistically and/or logically combining quality and/or process variables”, “the measured process and/or quality variables make possible create one or more calculated quality variables”. The combined quality variable or the processed batch data is mapped to the batch representation herein] of the batch, and performing an operation [“identify, and/or diagnose process operation faults”] using the batch representation of the batch ([051-052, 086-088]). Specifically, Blevins teaches the method of claim 1, wherein performing the operation using the batch representation of the batch includes one or more of:
determining an anomaly metric [“use of analytic tools such as PCA and PLS techniques for fault detection and prediction of quality parameters”] of the batch using the batch representation of the batch and a PCA model of the industrial process; or providing the batch representation of the batch to a machine learning model as an input ([011, 052]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Blevins and Yelchuru in view of Shayovitz because they both related to forming one or more batch representation to the received batch data generated in an industrial process and (2) modify the method/system of Yelchuru in view of Shayovitz to include determining an anomaly metric as in Blevins. Doing so would allow operators of the industrial facility to determine faults using the received batch data (Blevins [052]).
Allowable Subject Matter
Claims 7- 12 & 15- 19 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Regarding claims 7 & 14, Yelchuru in view of Shayovitz teaches the method of claim 1 and system of the claim 13.
Yelchuru in view of Shayovitz fails to teach remaining limitations of these claims.
Castillo et al. (US 20190332101 A1) teaches a method/system comprising: determining, by the batch analytic system, a plurality of variable trajectories of a particular process variable in a plurality of batches generated in the industrial process, wherein the plurality of batches have different batch lengths; determining, by the batch analytic system, that the plurality of variable trajectories of the particular process variable in the plurality of batches have a same shape [“identify average trajectory shape”]; determining, by the batch analytic system, a variable pattern [“step pattern”] of the particular process variable based on the same shape of the plurality of variable trajectories of the particular process variable ([0129-0159, 0256]).
However, Castillo or any other prior arts of the record do not teach or suggest the inclusion of
“selecting, by the batch analytic system, one or more functions that determine one or more attributes associated with the variable pattern of the particular process variable; and applying, by the batch analytic system, the one or more functions to the batch data of the batch when determining the batch representation of the batch” when viewed together with remaining limitations of the claim 7 & 15.
Regarding claims 8- 12 & 16- 19, they are also indicated allowable over prior arts of the record due to their direct and indirect dependency with claims 7 & 15.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
1) Li (US 5862054 A) teaches method steps comprising: applying, by a batch analytic system, a first function [computing average values for each parameter on a batch file and storing the average value in a historical data file 33] to generate a batch representation [“average value”] and using the batch representation to compute process limits (Fig. 3 & associated texts, Claim 8).
2) Yu et al (US 20140358478 A1) teaches Gaussian computer configured to compute a mean vector and a covariance matrix from multivariate performance characterizations having a multivariate distribution over a plurality of wafer ICs, wherein one performance characteristic would have a mean value and a standard deviation. (Abstract).
3) McCready (US 20130268238 A1) teaches performing multivariate analysis based on at least one unfolded data matrix established using observation-wise unfolding of a batch data array (Claim 3 [047]).
4) Ochi (US 5654896 A) a batch analytic system [computer 30] calculates and stores in the database library predetermined number n of measurements 79 average-value and a range R for each parameter of each chip 47 (Fig. 2-3, Col 7 lines 5- 30).
5) Wang et al. (US 20220044124 A1) data of Kat J times are collected in each batch to obtain a two-dimensional X(K×J) (Claim 1).
Contacts
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SANTOSH R. POUDEL whose telephone number is (571)272-2347. The examiner can normally be reached Monday - Friday (8:30 am - 5:00 pm).
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/SANTOSH R POUDEL/ Primary Examiner, Art Unit 2115
1 “the batch analytic system 900 may be implemented by computing resources such as servers, processors”.
2 See Yelchuru (US 20100017008 A1, fig. 6) & Blevins et al. (US 20110288837 A1), Castillo et al. (US 20190332101 A1).
3 See, Fig. 15 & Spec, paras. [0326, 0334], dependent claims 3-4.
Prosecution Insights