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 .
1. Claims 1-20 are presented for examination.
Specification
2. The disclosure is objected to because of the following informalities: "cross reference to related application" section (page 1) needs to be updated, to reflect the current status of the cited applications. Appropriate correction is required in response to this Office action. Appropriate correction is required.
Claim Rejections - 35 USC § 112
3. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
2.1 Claim 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “Major KPIs” in claim 1, 9 and 16 is a relative term which renders the claim indefinite. The term “major” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. the clause controlling the plurality of the tool groups according to the impact of the set of major KPIs of each tool group in order to reduce a total WIP, wherein the total WIP is a summation of the WIP of each tool group, wherein such set of major KPIs of each tool group has previously been presented as being selected merely according to the impact of each tool group, is deemed to be a mere statement of desired result, without more, which has no clear support within the instant claim language. In this regard, this claim language provides no metes and bounds for how controlling the tool groups based upon the selected set of major KPIs necessarily accomplishes a reduction in total WIP.
The term “critical stage” in claim 1 and 9 is a relative term which renders the claim indefinite. The term “critical” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
As per dependent claims 2-8, 10-15 and 17-20, these claims are at least rejected for their dependencies, directly or indirectly, on the rejected claims 1, 9 and 16. They are therefore rejected as set forth above.
Double Patenting
4. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,140,937, and claims 1-20 of US Patent No. 11,726,462 in view of Bruce (US 2014/02000696).
Although the claims at issue are not identical, they are not patentably distinct from each other because it is directed to the same claimed subject matter. For the instant claim 1 and claim 9 introduces that “wherein tools running a main process step of at least critical stages are grouped into one of the plurality of tool groups, and tools running a metrology step of the at least critical stages are grouped into another one of the plurality of tool groups”, however, the limitations of the claims are obvious variant of one of ordinary skill in the art at the time of inventor, since wafer fabrication is a very complex manufacturing process including several dozens of stages Bruce (US 2014/02000696) teaches [0014-0017, 0514], that it was known in the manufacturing management art to re-allocate system resources by grouping to improve the cycle time of the whole process performance. It would have been obvious to one having ordinary skill in the art, at the time the instant invention was filed, to include such re-allocation by grouping in Batrin to improve system performance of the wafer manufacturing process.
For example, the mapping of independent claims 1, 9 and 16 of the instant US Application 18/788,108 with the independent claims 1, 9 and 16 of Parent US Patent 12,140,937, as show below:
As the instant US Application of the dependent claims 2-8, 10-15 and 17-20 are unpatentable over the Parents US Patent claims 2-8, 10-15 and 17-20, respectively, since the same respective subject matter.
Instant US Patent Application 18/788,108
Parent US Patent 12,140,937
1. A method for reducing a work-in-progress (WIP) in a process of a product, the method comprising:
collecting process profile data from a plurality of tool groups running the process, and calculating a standard deviation of an output of a stage of a bottleneck tool group of the plurality of tool groups according to the process profile data to serve as values of at least one of a plurality of key-performance-indicators (KPIs) of each tool group,
wherein tools running a main process step of at least critical stages are grouped into one of the plurality of tool groups, and tools running a metrology step of the at least critical stages are grouped into another one of the plurality of tool groups;
feeding the values of the KPIs and the WIP of each tool group into a neural network model in order to output an impact on the WIP for each KPI of each tool group by the neural network model, wherein the neural network model is trained by feeding the values of the KPIs and the WIP of each tool group in the process profile data of a plurality of days collected in advance, and performing sensitivity analysis on the values of the KPIs and the WIP so as to output the impact showing a rate of change of the WIP resulting from a predetermined change of each KPI; selecting a predetermined percentage of the KPIs for each tool group as a set of major KPIs according to the impact on the WIP for each KPI of each tool group output by the neural network model; and
controlling the plurality of the tool groups according to the impact of the set of major KPIs of each tool group in order to reduce a total WIP, wherein the total WIP is a summation of the WIP of each tool group.
1. A method for reducing a work-in-progress (WIP) in a process of a product, the method comprising:
collecting process profile data from a plurality of tool groups running the process, and calculating a standard deviation of an output of a stage of a bottleneck tool group of the plurality of tool groups according to the collected process profile data to serve as values of at least one of a plurality of key-performance-indicators (KPIs) of each tool group;
feeding the values of the KPIs and the WIP of each tool group into a neural network model in order to output an impact on the WIP for each KPI of each tool group by the neural network model, wherein the neural network model is trained by feeding the values of the KPIs and the WIP of each tool group in the process profile data of a plurality of days collected in advance, and performing sensitivity analysis on the values of the KPIs and the WIP so as to output the impact showing a rate of change of the WIP resulting from a predetermined change of each KPI; selecting a predetermined percentage of the KPIs for each tool group as a set of major KPIs according to the impact on the WIP for each KPI of each tool group output by the neural network model,
wherein the standard deviation of the output of the stage of the bottleneck tool group is selected into the set of major KPIs of the bottleneck tool group; and
controlling the plurality of the tool groups according to the impact of the set of major KPIs of each tool group in order to reduce a total WIP, wherein the controlling comprises adjusting a dispatching priority of the bottleneck tool group to reduce the standard deviation of the output of the stage of the bottleneck tool group and the total WIP is a summation of the WIP of each tool group.
9. A system for reducing a work-in-progress (WIP) in a process of a product, comprising:
a plurality of tool groups configured to run the process; and
a controlling computer coupled to the plurality of tool groups, and configured to:
collect process profile data from the plurality of tool groups, and calculate a standard deviation of an output of a stage of a bottleneck tool group of the plurality of tool groups according to the process profile data to serve as values of at least one of a plurality of key-performance-indicators (KPIs) of each tool group,
wherein tools running a main process step of at least critical stages are grouped into one of the plurality of tool groups, and tools running a metrology step of the at least critical stages are grouped into another one of the plurality of tool groups;
feed the values of the KPIs and the WIP of each tool group into a neural network model in order to output an impact on the WIP for each KPI of each tool group by the neural network model, wherein the neural network model is trained by feeding the values of the KPIs and the WIP of each tool group in the process profile data of a plurality of days collected in advance, and performing sensitivity analysis on the values of the KPIs and the WIP so as to output the impact showing a rate of change of the WIP resulting from a predetermined change of each KPI model;
select a predetermined percentage of the KPIs for each tool group as a set of major KPIs according to the impact on the WIP for each KPI of each tool group output by the neural network model; and
control the plurality of tool groups according to the impact of the set of major KPIs of each tool group in order to reduce a total WIP, wherein the total WIP is a summation of the WIP of each tool group.
9. A system for reducing a work-in-progress (WIP) in a process of a product, comprising:
a plurality of tool groups configured to run the process; and
a controlling computer coupled to the plurality of tool groups, and configured to:
collect process profile data from the plurality of tool groups, and calculate a standard deviation of an output of a stage of a bottleneck tool group of the plurality of tool groups according to the collected process profile data to serve as values of at least one of a plurality of key-performance-indicators (KPIs) of each tool group;
feed the values of the KPIs and the WIP of each tool group into a neural network model in order to output an impact on the WIP for each KPI of each tool group by the neural network model, wherein the neural network model is trained by feeding the values of the KPIs, an initial WIP of each tool group that is a WIP at a starting time for collecting the process profile data and the WIP of each tool group in the process profile data of a plurality of days collected in advance, and performing sensitivity analysis on the values of the KPIs and the WIP so as to output the impact showing a rate of change of the WIP resulting from a predetermined change of each KPI;
select a predetermined percentage of the KPIs for each tool group as a set of major KPIs according to the impact on the WIP for each KPI of each tool group output by the neural network model; and
control the plurality of tool groups according to the impact of the set of major KPIs of each tool group in order to reduce a total WIP, wherein the total WIP is a summation of the WIP of each tool group.
16. A method for reducing a work-in-progress (WIP) in a process of a product, the method comprising:
collecting process profile data from a plurality of tool groups running the process, wherein the process profile data comprises an output from each of a plurality of stages run by each tool group, and calculating a standard deviation of an output of a stage of a bottleneck tool group of the plurality of tool groups every a predetermined number of days according to the process profile data to serve as values of at least one of a plurality of key-performance-indicators (KPIs) of each tool group;
feeding the values of the KPIs and the WIP of each tool group into a neural network model in order to output an impact on the WIP for each KPI of each tool group by the neural network model, wherein the neural network model is trained by feeding the values of the KPIs and the WIP of each tool group in the process profile data of a plurality of days collected in advance, and performing sensitivity analysis on the values of the KPIs and the WIP so as to output the impact showing a rate of change of the WIP resulting from a predetermined change of each KPI;
selecting a predetermined percentage of the KPIs for each tool group as a set of major KPIs according to the impact on the WIP for each KPI of each tool group output by the neural network model; and
controlling the plurality of the tool groups according to the impact of the set of major KPIs of each tool group in order to reduce a total WIP, wherein the total WIP is a summation of the WIP of each tool group
16. A method for reducing a work-in-progress (WIP) in a process of a product, the method comprising:
collecting process profile data from a plurality of tool groups running the process, and calculating a standard deviation of an output of a stage of a bottleneck tool group of the plurality of tool groups according to the collected process profile data to serve as values of at least one of a plurality of key-performance-indicators (KPIs) of each tool group;
feeding the values of the KPIs and the WIP of each tool group into a neural network model in order to output an impact on the WIP for each KPI of each tool group by the neural network model, wherein the neural network model is trained by feeding the values of the KPIs and the WIP of each tool group in the process profile data of a plurality of days collected in advance, and performing sensitivity analysis on the values of the KPIs and the WIP so as to output the impact showing a rate of change of the WIP resulting from a predetermined change of each KPI;
selecting a predetermined percentage of the KPIs having greatest impacts on the WIP for each tool group as a set of major KPIs based on a 80/20 rule according to the impact on the WIP for each KPI of each tool group output by the neural network model; and
controlling the plurality of the tool groups according to the impact of the set of major KPIs of each tool group in order to reduce a total WIP, wherein the total WIP is a summation of the WIP of each tool group.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 5-12, 14,16-19 of U.S. Patent No. 11,402,828 in view of Bruce (US 2014/02000696).
Although the claims at issue are not identical, they are not patentably distinct from each other because it is directed to the same claimed subject matter. For the instant claim 1 and claim 9 introduces that “wherein tools running a main process step of at least critical stages are grouped into one of the plurality of tool groups, and tools running a metrology step of the at least critical stages are grouped into another one of the plurality of tool groups”, however, the limitations of the claims are obvious variant of one of ordinary skill in the art at the time of inventor, since wafer fabrication is a very complex manufacturing process including several dozens of stages Bruce (US 2014/02000696) teaches [0014-0017, 0514], that it was known in the manufacturing management art to re-allocate system resources by grouping to improve the cycle time of the whole process performance. It would have been obvious to one having ordinary skill in the art, at the time the instant invention was filed, to include such re-allocation by grouping in Batrin to improve system performance of the wafer manufacturing process.
For example, the mapping of independent claims 1, 9 and 16 of the instant US Application 18/788,108 with the independent claims 1, 10 and 18 of Parent US Patent 11, 402,828, as show below:
As the instant US Application of the dependent claims 1-20 are unpatentable over the Parents US Patent claims 2-3, 5-9, 11-12 and 14-16 and 19, respectively, since the same respective subject matter.
Instant US Patent Application 18/788,108
Parent US Patent 11,402,828
1. A method for reducing a work-in-progress (WIP) in a process of a product, the method comprising:
collecting process profile data from a plurality of tool groups running the process, and calculating a standard deviation of an output of a stage of a bottleneck tool group of the plurality of tool groups according to the process profile data to serve as values of at least one of a plurality of key-performance-indicators (KPIs) of each tool group, wherein tools running a main process step of at least critical stages are grouped into one of the plurality of tool groups, and tools running a metrology step of the at least critical stages are grouped into another one of the plurality of tool groups;
feeding the values of the KPIs and the WIP of each tool group into a neural network model in order to output an impact on the WIP for each KPI of each tool group by the neural network model, wherein the neural network model is trained by feeding the values of the KPIs and the WIP of each tool group in the process profile data of a plurality of days collected in advance, and performing sensitivity analysis on the values of the KPIs and the WIP so as to output the impact showing a rate of change of the WIP resulting from a predetermined change of each KPI;
selecting a predetermined percentage of the KPIs for each tool group as a set of major KPIs according to the impact on the WIP for each KPI of each tool group output by the neural network model; and
controlling the plurality of the tool groups according to the impact of the set of major KPIs of each tool group in order to reduce a total WIP, wherein the total WIP is a summation of the WIP of each tool group.
1.A method for reducing a work-in-progress (WIP) in a process of a product, the method comprising:
collecting process profile data from a plurality of tool groups running the process, and calculating a standard deviation of an output of a stage of a bottleneck tool group of the plurality of tool groups according to the process profile data to serve as values of at least one of a plurality of key-performance-indicators (KPIs) of each tool group;
feeding the values of the KPIs and the WIP of each tool group into a neural network model in order to output an impact on the WIP for each KPI of each tool group by the neural network model, wherein the neural network model is trained by feeding the values of the KPIs and the WIP of each tool group in the process profile data of a plurality of days collected in advance, and performing sensitivity analysis on the values of the KPIs and the WIP so as to output the impact showing a rate of change of the WIP resulting from a predetermined change of each KPI;
selecting a predetermined percentage of the KPIs for each tool group as a set of major KPIs according to the impact on the WIP for each KPI of each tool group output by the neural network model; and
controlling the plurality of the tool groups according to the impact of the set of major KPIs of each tool group in order to reduce a total WIP, wherein the total WIP is a summation of the WIP of each tool group.
9. A system for reducing a work-in-progress (WIP) in a process of a product, comprising:
a plurality of tool groups configured to run the process; and
a controlling computer coupled to the plurality of tool groups, and configured to: collect process profile data from the plurality of tool groups, and calculate a standard deviation of an output of a stage of a bottleneck tool group of the plurality of tool groups according to the process profile data to serve as values of at least one of a plurality of key-performance-indicators (KPIs) of each tool group, wherein tools running a main process step of at least critical stages are grouped into one of the plurality of tool groups, and tools running a metrology step of the at least critical stages are grouped into another one of the plurality of tool groups;
feed the values of the KPIs and the WIP of each tool group into a neural network model in order to output an impact on the WIP for each KPI of each tool group by the neural network model, wherein the neural network model is trained by feeding the values of the KPIs and the WIP of each tool group in the process profile data of a plurality of days collected in advance, and performing sensitivity analysis on the values of the KPIs and the WIP so as to output the impact showing a rate of change of the WIP resulting from a predetermined change of each KPI model; select a predetermined percentage of the KPIs for each tool group as a set of major KPIs according to the impact on the WIP for each KPI of each tool group output by the neural network model; and
control the plurality of tool groups according to the impact of the set of major KPIs of each tool group in order to reduce a total WIP, wherein the total WIP is a summation of the WIP of each tool group.
10. A system for reducing a work-in-progress (WIP) in a process of a product, comprising:
a plurality of tool groups configured to run the process; and
a controlling computer coupled to the plurality of tool groups, and configured to: collect process profile data from the plurality of tool groups, and calculate a standard deviation of an output of a stage of a bottleneck tool group of the plurality of tool groups according to the process profile data to serve as values of at least one of a plurality of key-performance-indicators (KPIs) of each tool group;
feed the values of the KPIs and the WIP of each tool group into a neural network model in order to output an impact on the WIP for each KPI of each tool group by the neural network model, wherein the neural network model is trained by feeding the values of the KPIs and the WIP of each tool group in the process profile data of a plurality of days collected in advance, and performing sensitivity analysis on the values of the KPIs and the WIP so as to output the impact showing a rate of change of the WIP resulting from a predetermined change of each KPI;
select a predetermined percentage of the KPIs for each tool group as a set of major KPIs according to the impact on the WIP for each KPI of each tool group output by the neural network model; and
control the plurality of tool groups according to the impact of the set of major KPIs of each tool group in order to reduce a total WIP, wherein the total WIP is a summation of the WIP of each tool group.
16. A method for reducing a work-in-progress (WIP) in a process of a product, the method comprising:
collecting process profile data from a plurality of tool groups running the process, wherein the process profile data comprises an output from each of a plurality of stages run by each tool group, and calculating a standard deviation of an output of a stage of a bottleneck tool group of the plurality of tool groups every a predetermined number of days according to the process profile data to serve as values of at least one of a plurality of key-performance-indicators (KPIs) of each tool group;
feeding the values of the KPIs and the WIP of each tool group into a neural network model in order to output an impact on the WIP for each KPI of each tool group by the neural network model, wherein the neural network model is trained by feeding the values of the KPIs and the WIP of each tool group in the process profile data of a plurality of days collected in advance, and performing sensitivity analysis on the values of the KPIs and the WIP so as to output the impact showing a rate of change of the WIP resulting from a predetermined change of each KPI;
selecting a predetermined percentage of the KPIs for each tool group as a set of major KPIs according to the impact on the WIP for each KPI of each tool group output by the neural network model; and
controlling the plurality of the tool groups according to the impact of the set of major KPIs of each tool group in order to reduce a total WIP, wherein the total WIP is a summation of the WIP of each tool group.
18. A non-transitory computer-readable medium comprising processor executable instructions that when executed perform a method for reducing a work-in-progress (WIP) in a process of a product, the method comprising:
collecting process profile data from a plurality of tool groups running the process, and calculating a standard deviation of an output of a stage of a bottleneck tool group of the plurality of tool groups according to the process profile data to serve as values of at least one of a plurality of key-performance-indicators (KPIs) of each tool group;
feeding the values of the KPIs and the WIP of each tool group into a neural network model in order to output an impact on the WIP for each KPI of each tool group by the neural network model, wherein the neural network model is trained by feeding the values of the KPIs and the WIP of each tool group in the process profile data of a plurality of days collected in advance, and performing sensitivity analysis on the values of the KPIs and the WIP so as to output the impact showing a rate of change of the WIP resulting from a predetermined change of each KPI;
selecting a predetermined percentage of the KPIs for each tool group as a set of major KPIs according to the impact on the WIP for each KPI of each tool group output by the neural network model; and
controlling the plurality of the tool groups according to the impact of the set of major KPIs of each tool group in order to reduce a total WIP, wherein the total WIP is a summation of the WIP of each tool group.
Allowable Subject Matter
5. Claims 1-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, and the Double Patenting rejection in set forth this Office action.
The following is an examiner’s statement of reasons for allowance:
The allowability of the independent claims 1, 9 and 16 resides, at least in part, in that closest prior art of Batrin et al. (US 2014/0200696 A1) collecting process profile data from a plurality of tool groups running the process (para[0007] – analyses performance indicators of a set of machines; para[0019] - plurality of tools), and calculating values of a plurality of key-performance-indicators (KPIs) of each tool group (para[0007, 0025] - each element contributes data to be interpreted as key performance indicators) comprising calculating an output of a stage of a bottleneck tool group of the plurality of tool groups according to the process profile data (para[0026, 0037] - determine bottlenecks); feeding the values of the KPIs and a work-in-progress (WIP) (para[0027-0028] - KPIs and other items of data, including identification of which item is currently being processed (i.e.; WIP); para[0050-0051] - KPIs and other empirical data) of each tool group into a model (Fig 2, 240 - Intelligent Productivity System) in order to output an impact on the WIP for each KPI of each tool group by the model; selecting a set of major KPIs of each tool group from the KPIs according to the impact of each tool group output by the model (Fig 3, 326-350 - various processing of KPI values; para[0036-0037] - determine which element is a bottleneck or otherwise a limiting factor; para[0050-0051] - determine limiting factors in response to KPIs and other empirical data); and controlling the plurality of the tool groups according to the impact of the set of major KPIs of each tool group in order to reduce a total WIP, wherein the total WIP is a summation of the WIP of each tool group (para[0007] - provide control parameters; para[0031] - generate control variables; para[0036] - optimize parameters, such as best production throughput); however, the prior art does not disclose or suggest, alone or in combination, feeding the values of the KPIs and the WIP of each tool group into a neural network model in order to output an impact on the WIP for each KPI of each tool group by the neural network model, wherein the neural network model is trained by feeding the values of the KPIs and the WIP of each tool group in the process profile data of a plurality of days collected in advance, and performing sensitivity analysis on the values of the KPIs and the WIP so as to output the impact showing a rate of change of the WIP resulting from a predetermined change of each KPI; selecting a predetermined percentage of the KPIs for each tool group as a set of major KPIs according to the impact on the WIP for each KPI of each tool group output by the neural network model; wherein the total WIP is a summation of the WIP of each tool group, in combination with the other elements and features of the claimed invention.
As claims 2-8, 10-15 and 17-20 are directly or indirectly dependent on claims 1, 9 and 16, those claims are also allowable at least by virtue of their dependency.
Citation Pertinent prior art
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ghosh (US 2011/0282475 A1) discloses Cycle time and throughput of a manufacturing facility is effectively manages by a control system that employs a combination of a long-term horizon model.
Bruce (US 2002/0049621 A1) discloses analyzing a process in terms of the elements that are the drivers of the process. The invention evaluates a process in terms of one or more scheduling drivers and process drivers, measures the metrics around the driver.
Vazquez (US 20110166683 A1) discloses identifies a real time downstream to processing capability within a production environment using a computerized device. The processing sequences perform operations utilizing one or more tools.
ABRAMOVIC (US 20180060830 A1) discloses “Key Performance Indicator” or “KPI”, is a measure of the performance of an agent or group of agents, also known as a score.
Conclusion
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed Kidest Worku whose telephone number is 571-272-3737. The examiner can be reached on Mon-Fri 9am to 5pm (ET).
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/KIDEST WORKU/Primary Examiner, Art Unit 2119
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