DETAILED ACTION
This action is in response the communications filed on 03/31/2026 in which claims 1-17 are amended, and claims 1-17 are pending.
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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 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim 1 recites
“a history information module, storing various history data relevant to tools, recipe groups and lots to be processed in said semiconductor fabrication plant;
a basic information module, storing various basic data relevant to said tools, said recipe groups and said lots;
an algorithm module, working out predicted runtimes and predicted switching times of specific said recipe groups when specific said lots are processed in specific said tools based on said history data and said basic data through machine learning;
a robot module, working out an optimized schedule result with maximum output for every said tool and reduced overall cycle time of said lots based on said history data, said basic data, said predicted runtimes and said predicted switching times of specific said recipe groups; and
a dispatching module, dispatching said lots to multiple said tools in said semiconductor fabrication plant for performing predetermined said recipe groups according to said optimized schedule result to obtain an actual production result with improved production efficiency and capacity of said lot scheduling and dispatching and feed said actual production result back to said robot module”
that use the word “module”) are being interpreted under 35 U.S.C. 112(f). The specification identified the corresponding structure for all the “module” elements in [0017] “Preferably, one or more of such embodiments of the present invention are embodied in a computer implemented program or control system. The computer for executing this kind of program or control system may be a general purpose computer architecture, wherein user may enter commands for executing the computer-implemented methods of the present invention… The processor in the computer may access… program code or data relevant to the computer-implemented methods of the present invention and load them into the memory…”
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-17 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1 and 9 recite “an optimized schedule result with maximum output for every said tool and reduced overall cycle time of said lots” and “an actual production result with improved production efficiency and capacity of said lot scheduling and dispatching.” The claims recite those features as specific desired result for the claimed elements “an optimized schedule result” and “an actual production result.” However, the specification may lack written description support. The specification only states those features as an overall goal or aspirational level for the whole claimed invention ([0019] “in order to ensure maximum output for every tool”; [0003] “in hope of achieving the goals of intelligent manufacture, improving production capacity and reducing cycle time.”; [0020] “therefore the production capacity and efficiency may be improved.”), but the claims recite those features as specific outputs along with the claimed “result” elements. Accordingly, for examination purposes, the examiner has not given those features patentable weight, i.e. “with maximum output for every said tool and reduced overall cycle time of said lots” and “with improved production efficiency and capacity of said lot scheduling and dispatching” are not given weight.
Claims 2-8 and 10-17 are also rejected due to their dependency on a rejected claim.
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.
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Claims 1-17 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more
Step 1: Claims 1-8 recite a machine learning intelligent dispatching system. Claims 9-17 recite an intelligent dispatching method. Therefore, claims 1-8 are directed to a machine, and claims 9-17 are directed to a process.
With respect to claim 1:
2A Prong 1: The claim recites a judicial exception.
an algorithm module, working out predicted runtimes and predicted switching times of specific said recipe groups when specific said lots are processed in specific said tools based on said history data and said basic data… (mental process – evaluation or judgement, determining runtimes and switching times)
a robot module, working out an optimized schedule result with maximum output for every said tool and reduced overall cycle time of said lots based on said history data, said basic data, said predicted runtimes and said predicted switching times of specific said recipe groups; and (mental process – evaluation or judgement, determining an optimized schedule result based on the given data)
2A Prong 2: The judicial exception is not integrated into a practical application.
a history information module, storing various history data relevant to tools, recipe groups and lots to be processed in said semiconductor fabrication plant; (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting: acquiring history data)
a basic information module, storing various basic data relevant to said tools, said recipe groups and said lots (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting: acquiring basic data)
through machine learning (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; using machine learning model to process the given data)
a dispatching module, dispatching said lots to multiple said tools in said semiconductor fabrication plant (a particular technological environment or field of use – MPEP 2106.05(h))
for performing predetermined said recipe groups according to said optimized schedule result to obtain an actual production result with improved production efficiency and capacity of said lot scheduling and dispatching and feed said actual production result back to said robot module (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
a history information module, storing various history data relevant to tools, recipe groups and lots to be processed in said semiconductor fabrication plant; (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting, and WURC: receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 - MPEP 2106.05(d)(II)(i))
a basic information module, storing various basic data relevant to said tools, said recipe groups and said lots (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting, and WURC: receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 - MPEP 2106.05(d)(II)(i))
through machine learning (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; using machine learning model to process the given data)
a dispatching module, dispatching said lots to multiple said tools in said semiconductor fabrication plant (a particular technological environment or field of use – MPEP 2106.05(h))
for performing predetermined said recipe groups according to said optimized schedule result to obtain an actual production result with improved production efficiency and capacity of said lot scheduling and dispatching and feed said actual production result back to said robot module (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claim 2:
2A Prong 1: The claim recites a judicial exception.
wherein said robot module compares said actual production result and said optimized schedule result and (mental process – evaluation or judgement, comparing the actual result and the optimized result)
2A Prong 2: The judicial exception is not integrated into a practical application.
feeds a comparison result back to said algorithm module as a basis for said machine learning (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
feeds a comparison result back to said algorithm module as a basis for said machine learning (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting, and WURC: receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 - MPEP 2106.05(d)(II)(i))
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claim 3:
2A Prong 2: The judicial exception is not integrated into a practical application.
wherein said robot module performs data labeling and memory simulation to said history data and said basic data based on said comparison result (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception, performing data labeling and memory simulation)
said algorithm module uses information from said data labeling and said memory simulation as a basis for said machine learning (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; the machine learning model using the information)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein said robot module performs data labeling and memory simulation to said history data and said basic data based on said comparison result (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception, performing data labeling and memory simulation)
said algorithm module uses information from said data labeling and said memory simulation as a basis for said machine learning (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; the machine learning model using the information)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claim 4:
2A Prong 2: The judicial exception is not integrated into a practical application.
wherein algorithm models adopted in said machine learning comprises decision tree, random forest, artificial neural network or Bayesian network (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; using the machine learning model)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein algorithm models adopted in said machine learning comprises decision tree, random forest, artificial neural network or Bayesian network (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; using the machine learning model)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claim 5:
2A Prong 2: The judicial exception is not integrated into a practical application.
wherein said history data comprises tool information, production capacity information and lot information (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting; claim 1 recites “storing(acquiring) history data,” which is insignificant extra-solution activity.)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein said history data comprises tool information, production capacity information and lot information (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting, and WURC: receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 - MPEP 2106.05(d)(II)(i)); claim 1 recites “storing(acquiring) history data,” which is insignificant extra-solution activity.)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claim 6:
2A Prong 2: The judicial exception is not integrated into a practical application.
wherein said basic data comprises tool constraint information, flow information and lot schedule information (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting: Claim 1 recites “storing(acquiring) various basic data,” which is insignificant extra-solution activity.)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein said basic data comprises tool constraint information, flow information and lot schedule information (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting, and WURC: receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 - MPEP 2106.05(d)(II)(i)). Claim 1 recites “storing(acquiring) various basic data,” which is insignificant extra-solution activity.)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claim 7:
2A Prong 2: The judicial exception is not integrated into a practical application.
wherein said dispatching module comprises active management system and real-time dispatching system, and said active management system executes actions of dispatching said lots and said real-time dispatching system feeds said actual production result back to said robot module in real time (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein said dispatching module comprises active management system and real-time dispatching system, and said active management system executes actions of dispatching said lots and said real-time dispatching system feeds said actual production result back to said robot module in real time (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claim 8:
2A Prong 1: The claim recites a judicial exception.
wherein said optimized schedule result comprises predicted optimized runtimes of multiple said lots processed in multiple said tools using multiple said recipe groups (mental process – evaluation or judgement: claim 1 recites “working out an optimized schedule result” which is an abstract idea. The specifics (predicted optimized runtimes) do not change the scope of the claim.)
2A Prong 2: The judicial exception is not integrated into a practical application.
said actual production result comprises actual runtime of said lots dispatched according to said optimized schedule result (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception: claim 1 recites “performing… to obtain an actual production result” which is mere instructions to apply an exception.)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
said actual production result comprises actual runtime of said lots dispatched according to said optimized schedule result (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception: claim 1 recites “performing… to obtain an actual production result” which is mere instructions to apply an exception.)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claim 9:
2A Prong 1: The claim recites a judicial exception.
working out predicted runtimes and predicted switching times of specific said recipe groups when specific said lots are processed in specific said tools based on said history data and said basic data (mental process – evaluation or judgement, determining runtimes and switching times)
working out an optimized schedule result with maximum output for every said tool and reduced overall cycle time of said lots based on said history data, said basic data, said predicted runtimes and said predicted switching times of specific said recipe groups (mental process – evaluation or judgement, determining an optimized result based on the given data)
comparing said actual production result and said optimized schedule result and feeding back a comparison result as a basis for said machine learning algorithms (mental process – evaluation or judgement, comparing the actual result and the optimized result to provide feedback)
2A Prong 2: The judicial exception is not integrated into a practical application.
acquiring various history data and basic data relevant to tools, recipe groups and lots to be processed in said semiconductor fabrication plant from database (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting: receiving history data)
through different machining learning algorithms (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; using machine learning model to process the given data)
dispatching said lots to multiple said tools in said semiconductor fabrication plant (a particular technological environment or field of use – MPEP 2106.05(h))
for performing predetermined said recipe groups according to said optimized schedule result to obtain an actual production result with improved production efficiency and capacity of said lot scheduling and dispatching; (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
acquiring various history data and basic data relevant to tools, recipe groups and lots to be processed in said semiconductor fabrication plant from database (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting: receiving history data, and WURC: receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 - MPEP 2106.05(d)(II)(i))
through different machining learning algorithms (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; using machine learning model to process the given data)
dispatching said lots to multiple said tools in said semiconductor fabrication plant (a particular technological environment or field of use – MPEP 2106.05(h))
for performing predetermined said recipe groups according to said optimized schedule result to obtain an actual production result with improved production efficiency and capacity of said lot scheduling and dispatching; (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claim 10:
2A Prong 1: The claim recites a judicial exception.
wherein said optimized schedule result comprises predicted optimized runtimes of multiple said lots processed in multiple said tools using multiple said recipe groups (mental process – evaluation or judgement: claim 9 recites “working out an optimized schedule result” which is an abstract idea. The specifics (predicted optimized runtimes) do not change the scope of the claim.)
2A Prong 2: The judicial exception is not integrated into a practical application.
said actual production result comprises actual runtime of said lots dispatched according to said optimized schedule result (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception: claim 9 recites “performing… to obtain an actual production result” which is mere instructions to apply an exception.)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
said actual production result comprises actual runtime of said lots dispatched according to said optimized schedule result (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception: claim 9 recites “performing… to obtain an actual production result” which is mere instructions to apply an exception.)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claim 11:
2A Prong 1: The claim recites a judicial exception.
wherein comparing said actual production result and said optimized schedule result comprises working out a difference between said predicted optimized runtimes and said actual runtime, and (mental process – evaluation or judgement)
if said difference exceeds a set value, determining there is a disparity between said optimized schedule result and said actual production result, and (mental process – evaluation or judgement)
if said difference is smaller than said set value, determining there is no disparity between said optimized schedule result and said actual production result (mental process – evaluation or judgement)
With respect to claim 12:
2A Prong 1: The claim recites a judicial exception.
wherein when it is determined that there is a disparity between said optimized schedule result and said actual production result, feedback said comparison result as a basis for said machine learning algorithms (mental process – evaluation or judgement, determining a disparity to provide feedback)
With respect to claim 13:
2A Prong 2: The judicial exception is not integrated into a practical application.
further comprising performing data labeling and memory simulation to said history data and said basic data based on said comparison result (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; performing data labeling and memory simulation)
information from said data labeling and said memory simulation are used as a basis for said machine learning algorithms (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; the machine learning model using the information)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
further comprising performing data labeling and memory simulation to said history data and said basic data based on said comparison result (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; performing data labeling and memory simulation)
information from said data labeling and said memory simulation are used as a basis for said machine learning algorithms (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; the machine learning model using the information)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claim 14:
2A Prong 1: The claim recites a judicial exception.
further comprising using or excluding specific said machine learning algorithms based on said comparison result (mental process – evaluation or judgement,--- using or excluding algorithms based on the result)
With respect to claim 15:
2A Prong 2: The judicial exception is not integrated into a practical application.
wherein said machine learning algorithms comprise decision tree, random forest, artificial neural network or Bayesian network (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; using machine learning models)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein said machine learning algorithms comprise decision tree, random forest, artificial neural network or Bayesian network (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; using machine learning models)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claim 16:
2A Prong 2: The judicial exception is not integrated into a practical application.
wherein acquiring said history data and said basic data comprises said machine learning algorithm decides whether to process said history data and said basic data with average, median or weighted treatment (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception, in light of specification [0022] “algorithm module may self-determine”)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein acquiring said history data and said basic data comprises said machine learning algorithm decides whether to process said history data and said basic data with average, median or weighted treatment (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception, in light of specification [0022] “algorithm module may self-determine”)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claim 17:
2A Prong 2: The judicial exception is not integrated into a practical application.
wherein comparing said actual production result and said optimized schedule result comprises auto-regulating algorithms and variation factors in information cross-validation based on said recipe groups or products (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein comparing said actual production result and said optimized schedule result comprises auto-regulating algorithms and variation factors in information cross-validation based on said recipe groups or products (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 1-10 and 13-15 rejected under 35 U.S.C. 103 as being unpatentable over Schelthoff (US 20230260056 A1, filed on 20230216) in view of Kim ("On Scheduling a Photolithograhy Toolset Based on a Deep Reinforcement Learning Approach with Action Filter" 20211212) in further view of Park ("A Reinforcement Learning Approach to Robust Scheduling of Semiconductor Manufacturing Facilities" 20191231)
In regard to claim 1, Schelthoff teaches: A machine Learning intelligent dispatching system for lot scheduling and dispatching in a semiconductor fabrication plant, comprising: (Schelthoff, Abstract "A method predicts an expected waiting time for a route having a plurality of production operations in manufacturing. The method includes receiving a sorted list of production operations characterizing a route for manufacturing a lot, and defining a starting time point of a lot production start time. [lot scheduling and dispatching]"; [0048] "we present a framework for waiting time estimation of operations in semiconductor wafer fabs [a semiconductor fabrication plant]")
a history information module… a basic information module… an algorithm module… a robot module… a dispatching module… (Schelthoff, [0101] "The procedures executed by the training device 500 may be implemented as a computer program stored on a machine-readable storage medium 54 and executed by a processor 55.")
storing various history data relevant to tools, recipe groups and lots to be processed in said semiconductor fabrication plant; storing various basic data relevant to said tools, said recipe groups and said lots; (Schelthoff, Abstract "The method further includes, for each production operation in the sorted list, (i) sampling feature values for a plurality of features by sampling from a database of collected feature values... "; [0016] "the machine learning system take as inputs different feature sets. [acquiring (storing) history/basic data]"; [0054] "In FIG. 1, the table shows the feature set of our approach."; [0064] "G. Availability of machines [tools] (a): the availability is defined by the number of available machines which are able to execute the operation."; [0072] "O. Amount of similar operations in the queue (ql_sim): Similar operations are of the same operation type (independent of its product) and can be therefore produced in batches, [recipe groups (batches with similar operations or the same type of operation)] if the equipments are capable of processing batches."; [0056]-[0057] "A. Lot priority [lots] (P): Each lot is assigned a priority at fab entry…"; a database storing feature set, which include data of tools, recipe groups and lots)
working out predicted runtimes and predicted switching times of specific said recipe groups when specific said lots are processed in specific said tools based on said history data and said basic data through machine learning; (Schelthoff, [0018] "the trained machine learning system [machine learning] or the plurality of trained machine learning systems are configured to additionally output the expected processing times. [predicted runtimes]"; [0100] "The training system 500 comprises a provider system 51, which provides input features from a training data base. Input features are fed to the machine learning system 52 to be trained, which determines expected waiting time from them."; [0005] "There are approaches on waiting time predictions by forecasting models, wherein the forecasting models can be neural networks or data mining models to forecast cycle times. [predicted runtimes and predicted switching times]"; [0049] "Cycle time can be defined as elapsed time between starting and completing a task, which is composed of transport time, waiting time, processing time, and time for additional steps."; [0058] "It is of relevance for batch-building (group of lots to be processed together) operations"; [0072] "Hence, a lot could be preferred if a lot of similar operations is waiting for execution to create full batches. [recipe groups (full batches with similar operations or the same type of operation)]"; using a machine learning model to determine cycle times (which includes run time, waiting time, switch time, transport time, etc.), which is associated with groups of lots of the same/similar type of processing, is based on all the input features)
Schelthoff does not teach, but Kim teaches: working out an optimized schedule result (Kim, p. 3, PHOTOLITHOGRAHY SCHEDULER BY REINFORCEMENT LEARNING" In this section, we introduce the structure of the photolithography scheduler by reinforcement learning. [RL working out an optimized schedule result] Figure 2 shows the overall structure of proposed model. Arrival is generated from historical production data [said history data] of fab... The RL agent interacts with the integrated system simulation and observes system state and selects a lot to dispatch. The proposed model incorporates the machine eligibility constraints and reticle resource constraints."; p. 5, 3.2.1 State description "Feature 1 [e.g. said basic data] Let Nwj represents number of waiting jobs of lot type LTj... Feature 2 s2l represents the status of the mask. [by analyzing reticle/mask avaiability, which identifies if reticle swap and setup time (switching times) is necessary] For l∈{1,2,…,r}, s2l is defined as follows... if mask l is available... if mask l is occupied by machine... Feature 6, s6j represent the urgency of waiting jobs of LTj. pj,k denote the processing time of lot type j [said predicted runtimes of the lot type/recipe group] at machine k."; p. 3, 2 PROBLEM DESCRIPTION "If prior LTi and following LTj requires different reticle resources (reti,l≠retj,l), the system need setup time (st) [said predicted switching times of the lot type/recipe group] for change reticle."; dispatching or scheduling is based on the features of lot type [specific said recipe groups])
dispatching said lots to multiple said tools in said semiconductor fabrication plant for performing predetermined said recipe groups according to said optimized schedule result (Kim, p. 3, PHOTOLITHOGRAHY SCHEDULER BY REINFORCEMENT LEARNING "In this section, we introduce the structure of the photolithography scheduler by reinforcement learning. [RL working out an optimized schedule result ] Figure 2 shows the overall structure of proposed model... The RL agent interacts with the integrated system simulation and observes system state and selects a lot to dispatch.")
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Schelthoff to incorporate the teachings of Kim by including deep reinforcement learning with reticle and qualification constraints. Doing would yield improved performance compared to typical rule-based strategies. (Kim, p. 1 Abstract "The proposed model was evaluated in a simulation environment inspired by practical photolithography scheduling problems across various settings with reticle and qualification constraints. Our experiments demonstrated improved performance compared to typical rule-based strategies.")
Schelthoff and Kim do not teach, but Park teaches: to obtain an actual production result (Park, p. 1424, D. Training and Test Phases "L = ... f(yu, qu) (3) where f(yu [an actual production result], qu) is the loss function given by f(yu, qu) = ...yu-qu... (4)"; p. 1424, D. Training and Test Phases "In line 22, the learning algorithm performs a gradient descent step with respect to θ on the Huber loss L [42], denoted as L…"; the loss L is provided back to the RL model [feeds the result back])
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Schelthoff and Kim to incorporate the teachings of Park by including reinforcement learning (RL) and a centralized policy by sharing a neural network. Doing so would minimize the makespan for an MCP scheduling problem. (Park, p. , Abstract "To minimize the makespan for an MCP scheduling problem, we propose a setup change scheduling method using reinforcement learning (RL) in which each agent determines setup decisions in a decentralized manner and learns a centralized policy by sharing a neural network among the agents to deal with the changes in the number of machines.")
In regard to claim 2, Schelthoff and Kim do not teach, but Park teaches: wherein said robot module compares said actual production result and said optimized schedule result and (Park, p. 1422, III. Scheduling by RL [a robot module] "The proposed scheduling method based on the Q -network is presented in this section."; p. 1424, D. Training and Test Phases "L = ... f(yu, qu) (3) where f(yu [said actual production result], qu [said optimized schedule result]) is the loss function given by f(yu, qu) = ...yu-qu [said comparison result, a difference] ... (4)")
feeds a comparison result back to said algorithm module as a basis for said machine learning. (Park, p. 1424, D. Training and Test Phases "In line 22, the learning algorithm performs a gradient descent step with respect to θ on the Huber loss L [42], denoted as L…"; the loss L is provided back to the RL model [feeds a comparison result back])
The rationale for combining the teachings of Schelthoff, Kim and Park is the same as set forth in the rejection of claim 1.
In regard to claim 3, Schelthoff and Kim do not teach, but Park teaches: wherein said robot module performs data labeling and (Park, p. 1422, III. Scheduling by RL [a robot module] "The proposed scheduling method based on the Q -network is presented in this section."; p. 1424, D. Training and Test Phases "Given the sampled transitions, a loss is calculated from a Q-value and target value [35] [data labeling] (lines 19–21)."; p. 1424, Algorithm 1 "19: Set qu=Q(su,au;θ) 20: Set yu = ru+γ1F(su+1)maxa′Q^(su+1,a′;θ^) 21: Calculate loss L from (3)–(4)"; yu is the "synthetic label" for the specific state-action input)
memory simulation (Park, p. 1422, III. Scheduling by RL "we adopt the replay buffer [e.g. memory simulation, sample is memory] that contains the set of transitions each of which consists of state, action, reward, and next state... The weights of the Q -network [e.g. memory simulation, weights is memory] are periodically replicated to the target Q -network, which improves the convergence stability of the Q -network [35]."; in light of [0024] "The memory simulation may include static memory like parametric model (i.e. model parameter is memory), non-parametric model (i.e. sample is memory)... or neural network model (i.e. weights in network connectivity is memory), etc.";) to said history data and said basic data based on said comparison result, and said algorithm module uses information from said data labeling and said memory simulation as a basis for said machine learning. (Park, p. 1424, Algorithm 1 Scheduling with Q-Network "1:… replay buffer B [history/basic data] to size NB… Calculate loss L from (3)-(4) 22: Perform a gradient descent step on L [said comparison result] w.r.t. θ "; RL uses the yu [said data labeling] and stored samples in the replay buffer or weights θ [memory simulation] for learning)
The rationale for combining the teachings of Schelthoff, Kim and Park is the same as set forth in the rejection of claim 1.
In regard to claim 4, Schelthoff teaches: wherein algorithm models adopted in said machine learning comprises decision tree, random forest, artificial neural network or Bayesian network. (Schelthoff, [0005] "There are approaches on waiting time predictions by forecasting models, wherein the forecasting models can be neural networks or data mining models to forecast cycle times.")
In regard to claim 5, Schelthoff teaches: wherein said history data comprises tool information, (Schelthoff, [0016] "the machine learning system take as inputs different feature sets. [said history data]"; [0054] "In FIG. 1, the table shows the feature set of our approach."; [0064] "G. Availability of machines [e.g. tool information] (a): the availability is defined by the number of available machines which are able to execute the operation. Preferably, we obtain the number of machines in each equipment state ('available', 'repair', 'maintenance', 'setup', and 'shutdown') as features...")
production capacity information and (Schelthoff, [0062]-[0063] "F. The utilization (upreH)... The utilization of the equipment's indicates the available capacity for the process execution [e.g. production capacity information]...") lot information. (Schelthoff, [0056]-[0057] "A. Lot priority [e.g. lot information] (P): Each lot is assigned a priority at fab entry… B. Work-in-progress (WIP): The WIP is defined as the number of lots currently in operation in a machine group and the number of lots currently waiting"; [0071]-[0074])
In regard to claim 6, Schelthoff teaches: wherein said basic data comprises tool constraint information, (Schelthoff, [0016] "the machine learning system take as inputs different feature sets. [said basic data]"; [0054] "In FIG. 1, the table shows the feature set of our approach."; [0064] "G. Availability of machines [e.g. tool constraint information] (a): the availability is defined by the number of available machines which are able to execute the operation. Preferably, we obtain the number of machines in each equipment state ('available', 'repair', 'maintenance', 'setup', and 'shutdown') as features...")
flow information and (Schelthoff, [0066]-[0068]"I. Product mix in the fab (pmfab)… The complexity of a product can be measured by the amount of layers [e.g. flow information] necessary for its completion... J. Number of tool loops (l): This feature indicates whether an operation is executed for the first time, or is repeated as a rework step..."; [0078] "U. Number of total stages necessary (Sttotal) for completion: This feature shall indicate how complex the respective lot is..."; in light of psecification [0023] "Flow information may include... specific process steps to be performed for various recipe groups")
lot schedule information. (Schelthoff, [0056]-[0057] "A. Lot priority [e.g. lot schedult information] (P): Each lot is assigned a priority at fab entry… B. Work-in-progress (WIP): The WIP is defined as the number of lots currently in operation in a machine group and the number of lots currently waiting"; [0071]-[0074])
In regard to claim 7, Schelthoff does not teach, but Kim teaches: wherein said dispatching module comprises active management system and real-time dispatching system, and (Kim, p. 2, 1.1 Contribution and Organization "an efficient Deep Q-Networks(DQN) algorithm for real-time sequential decision making in photolithography, and")
The rationale for combining the teachings of Schelthoff and Kim is the same as set forth in the rejection of claim 1.
Schelthoff and Kim do not teach, but Park teaches: said active management system executes actions of dispatching said lots and said real-time dispatching system feeds said actual production result back to said robot module in real time. (Park, p. 1424, D. Training and Test Phases "L = ... f(yu, qu) (3) where f(yu [an actual production result], qu) is the loss function given by f(yu, qu) = ...yu-qu... (4)"; p. 1424, D. Training and Test Phases "In line 22, the learning algorithm performs a gradient descent step with respect to θ on the Huber loss L [42], denoted as L…"; the loss L is provided back to the RL model [feeds the result back])
The rationale for combining the teachings of Schelthoff, Kim and Park is the same as set forth in the rejection of claim 1.
In regard to claim 8, Schelthoff teaches: of multiple said lots processed in multiple said tools using multiple said recipe groups, and (Schelthoff, [0072] "O. Amount of similar operations in the queue (ql_sim): Similar operations are of the same operation type (independent of its product) and can be therefore produced in batches, [multiple recipe groups (batches with similar operations)] if the equipments are capable of processing batches. Hence, a lot could be preferred if a lot of similar operations is waiting for execution to create full batches.")
Schelthoff does not teach, but Kim teaches: of said lots dispatched according to said optimized schedule result. (Kim, p. 3, PHOTOLITHOGRAHY SCHEDULER BY REINFORCEMENT LEARNING "In this section, we introduce the structure of the photolithography scheduler by reinforcement learning. [RL working out an optimized schedule result ] Figure 2 shows the overall structure of proposed model... The RL agent interacts with the integrated system simulation and observes system state and selects a lot to dispatch.")
The rationale for combining the teachings of Schelthoff and Kim is the same as set forth in the rejection of claim 1.
Schelthoff and Kim do not teach, but Park teaches: wherein said optimized schedule result comprises predicted optimized runtimes… said actual production result comprises actual runtime (Park, p. 1421, II. Problem Definition "Oj,k can be processed on any machine among its alternatives, and the processing time of Oj,k is denoted as pj,k."; p. 1423, B. State, Action, and Reward "If the previous setup type is Oj′,k′ and ai is Oj,k, τ(si+1) is calculated as the sum of τ(si), pj,k, and σj′,k′,j,k... As a state transition takes place from si to si+1 , ri is observed... ri is defined as follows: ri = ... τ(si+1)… (1) [reward is based on τ(si+1), which is based on the processing time (said runtime)]"; p. 1424, Algorithm 1 "19: Set qu=Q(su,au;θ) 20: Set yu = ru+γ...max...Q^(su+1,a′;θ^) [yu is based on ru, which is based on the processing time (said runtime)]"; yu is actual accumulated rewards and processing time at time i+1 [actual runtime] , qu is predicted accumulated rewards and processing time at time i [predicted optimized runtimes])
The rationale for combining the teachings of Schelthoff, Kim and Park is the same as set forth in the rejection of claim 1.
In regard to claim 9, Schelthoff teaches: An intelligent dispatching method through machine learning for lot scheduling and dispatching in a semiconductor fabrication plant, comprising: (Schelthoff, Abstract "A method predicts an expected waiting time for a route having a plurality of production operations in manufacturing. The method includes receiving a sorted list of production operations characterizing a route for manufacturing a lot, and defining a starting time point of a lot production start time. [lot scheduling and dispatching]"; [0048] "we present a framework for waiting time estimation of operations in semiconductor wafer fabs [a semiconductor fabrication plant]")
acquiring various history data and basic data relevant to tools, recipe groups and lots to be processed in said semiconductor fabrication plant from database; (Schelthoff, Abstract "The method further includes, for each production operation in the sorted list, (i) sampling feature values for a plurality of features by sampling from a database of collected feature values... "; [0016] "the machine learning system take as inputs different feature sets. [acquiring (storing) history/basic data]"; [0054] "In FIG. 1, the table shows the feature set of our approach."; [0064] "G. Availability of machines [tools] (a): the availability is defined by the number of available machines which are able to execute the operation."; [0072] "O. Amount of similar operations in the queue (ql_sim): Similar operations are of the same operation type (independent of its product) and can be therefore produced in batches, [recipe groups (batches with similar operations or the same type of operation)] if the equipments are capable of processing batches."; [0056]-[0057] "A. Lot priority [lots] (P): Each lot is assigned a priority at fab entry…"; a database storing feature set, which include data of tools, recipe groups and lots)
working out predicted runtimes and predicted switching times of specific said recipe groups when specific said lots are processed in specific said tools based on said history data and said basic data through different machining learning algorithms; (Schelthoff, [0018] "the trained machine learning system [machine learning] or the plurality of trained machine learning systems are configured to additionally output the expected processing times. [predicted runtimes]"; [0100] "The training system 500 comprises a provider system 51, which provides input features from a training data base. Input features are fed to the machine learning system 52 to be trained, which determines expected waiting time from them."; [0005] "There are approaches on waiting time predictions by forecasting models, wherein the forecasting models can be neural networks or data mining models to forecast cycle times. [predicted runtimes and predicted switching times]"; [0049] "Cycle time can be defined as elapsed time between starting and completing a task, which is composed of transport time, waiting time, processing time, and time for additional steps."; [0058] "It is of relevance for batch-building (group of lots to be processed together) operations"; [0072] "Hence, a lot could be preferred if a lot of similar operations is waiting for execution to create full batches. [recipe groups (full batches with similar operations or the same type of operation)]"; using a machine learning model to determine cycle times (which includes run time, waiting time, switch time, transport time, etc.), which is associated with groups of lots of the same/similar type of processing, is based on all the input features)
Schelthoff does not teach, but Kim teaches: working out an optimized schedule result with maximum output for every said tool and reduced overall cycle time of said lots based on said history data, said basic data, said predicted runtimes and said predicted switching times of specific said recipe groups; (Kim, p. 3, PHOTOLITHOGRAHY SCHEDULER BY REINFORCEMENT LEARNING" In this section, we introduce the structure of the photolithography scheduler by reinforcement learning. [RL working out an optimized schedule result] Figure 2 shows the overall structure of proposed model. Arrival is generated from historical production data [said history data] of fab... The RL agent interacts with the integrated system simulation and observes system state and selects a lot to dispatch. The proposed model incorporates the machine eligibility constraints and reticle resource constraints."; p. 5, 3.2.1 State description "Feature 1 [e.g. said basic data] Let Nwj represents number of waiting jobs of lot type LTj... Feature 2 s2l represents the status of the mask. [by analyzing reticle/mask avaiability, which identifies if reticle swap and setup time (switching times) is necessary] For l∈{1,2,…,r}, s2l is defined as follows... if mask l is available... if mask l is occupied by machine... Feature 6, s6j represent the urgency of waiting jobs of LTj. pj,k denote the processing time of lot type j [said predicted runtimes of the lot type/recipe group] at machine k."; p. 3, 2 PROBLEM DESCRIPTION "If prior LTi and following LTj requires different reticle resources (reti,l≠retj,l), the system need setup time (st) [said predicted switching times of the lot type/recipe group] for change reticle."; dispatching or scheduling is based on the features of lot type [specific said recipe groups])
dispatching said lots to multiple said tools in said semiconductor fabrication plant for performing predetermined said recipe groups according to said optimized schedule result (Kim, p. 3, PHOTOLITHOGRAHY SCHEDULER BY REINFORCEMENT LEARNING "In this section, we introduce the structure of the photolithography scheduler by reinforcement learning. [RL working out an optimized schedule result ] Figure 2 shows the overall structure of proposed model... The RL agent interacts with the integrated system simulation and observes system state and selects a lot to dispatch.")
The rationale for combining the teachings of Schelthoff and Kim is the same as set forth in the rejection of claim 1.
Schelthoff and Kim do not teach, but Park teaches: to obtain an actual production result with improved production efficiency and capacity of said lot scheduling and dispatching; and comparing said actual production result and said optimized schedule result and (Park, p. 1424, D. Training and Test Phases "L = ... f(yu, qu) (3) where f(yu [said actual production result], qu [said optimized schedule result]) is the loss function given by f(yu, qu) = ...yu-qu [said comparison result] ... (4)")
feeding back a comparison result as a basis for said machine learning algorithms. (Park, p. 1424, D. Training and Test Phases "In line 22, the learning algorithm performs a gradient descent step with respect to θ on the Huber loss L [42], denoted as L…"; the loss L is provided back to the RL model [feeds a comparison result back])
The rationale for combining the teachings of Schelthoff, Kim and Park is the same as set forth in the rejection of claim 1.
In regard to claim 10, Schelthoff teaches: … of multiple said lots processed in multiple said tools using multiple said recipe groups, and… (Schelthoff, [0072] "O. Amount of similar operations in the queue (ql_sim): Similar operations are of the same operation type (independent of its product) and can be therefore produced in batches, [multiple recipe groups (batches with similar operations)] if the equipments are capable of processing batches. Hence, a lot could be preferred if a lot of similar operations is waiting for execution to create full batches.")
Schelthoff does not teach, but Kim teaches: of said lots dispatched according to said optimized schedule result. (Kim, p. 3, PHOTOLITHOGRAHY SCHEDULER BY REINFORCEMENT LEARNING "In this section, we introduce the structure of the photolithography scheduler by reinforcement learning. [RL working out an optimized schedule result ] Figure 2 shows the overall structure of proposed model... The RL agent interacts with the integrated system simulation and observes system state and selects a lot to dispatch.")
The rationale for combining the teachings of Schelthoff and Kim is the same as set forth in the rejection of claim 1.
Schelthoff and Kim do not teach, but Park teaches: wherein said optimized schedule result comprises predicted optimized runtimes… said actual production result comprises actual runtime… (Park, p. 1421, II. Problem Definition "Oj,k can be processed on any machine among its alternatives, and the processing time of Oj,k is denoted as pj,k."; p. 1423, B. State, Action, and Reward "If the previous setup type is Oj′,k′ and ai is Oj,k, τ(si+1) is calculated as the sum of τ(si), pj,k, and σj′,k′,j,k... As a state transition takes place from si to si+1 , ri is observed... ri is defined as follows: ri = ... τ(si+1)... (1) [reward is based on τ(si+1), which is based on the processing time (said runtime)]"; p. 1424, Algorithm 1 "19: Set qu=Q(su,au;θ) 20: Set yu = ru+γ...max...Q^(su+1,a′;θ^) [yu is based on ru, which is based on the processing time (said runtime)]"; yu is actual accumulated rewards and processing time at time i+1 [actual runtime] , qu is predicted accumulated rewards and processing time at time i [predicted optimized runtimes])
The rationale for combining the teachings of Schelthoff, Kim and Park is the same as set forth in the rejection of claim 1.
In regard to claim 13, Schelthoff and Kim do not teach, but Park teaches: further comprising performing data labeling and (Park, p. 1424, D. Training and Test Phases "Given the sampled transitions, a loss is calculated from a Q-value and target value [35] [data labeling] (lines 19–21)."; p. 1424, Algorithm 1 "19: Set qu=Q(su,au;θ) 20: Set yu = ru+γ1F(su+1)maxa′Q^(su+1,a′;θ^) 21: Calculate loss L from (3)–(4)"; yu is the "synthetic label" for the specific state-action input)
memory simulation (Park, p. 1422, III. Scheduling by RL "we adopt the replay buffer [e.g. memory simulation, sample is memory] that contains the set of transitions each of which consists of state, action, reward, and next state... The weights of the Q -network [e.g. memory simulation, weights is memory] are periodically replicated to the target Q -network, which improves the convergence stability of the Q -network [35]."; in light of [0024] "The memory simulation may include static memory like parametric model (i.e. model parameter is memory), non-parametric model (i.e. sample is memory)... or neural network model (i.e. weights in network connectivity is memory), etc.";) to said history data and said basic data based on said comparison result, and information from said data labeling and said memory simulation are used as a basis for said machine learning algorithms. (Park, p. 1424, Algorithm 1 Scheduling with Q-Network "1:… replay buffer B [history/basic data] to size NB… Calculate loss L from (3)-(4) 22: Perform a gradient descent step on L [said comparison result] w.r.t. θ "; RL uses the yu [said data labeling] and stored samples in the replay buffer or weights θ [memory simulation] for learning)
The rationale for combining the teachings of Schelthoff, Kim and Park is the same as set forth in the rejection of claim 1.
In regard to claim 14, Schelthoff and Kim do not teach, but Park teaches: further comprising using or excluding specific said machine learning algorithms based on said comparison result. (Park, p. 1424, D. Training and Test Phases "In line 32, the weights of the target Q-network, denoted as θ^ , are periodically replaced to those for the Q-network [using or excluding specific said machine learning algorithms (periodically synced the weights)] for ensuring stable convergence as in [35]. The update period is denoted as NU."; p. 1422, III. SCHEDULING BY RL "The weights of the Q-network are periodically replicated to the target Q-network, which improves the convergence stability of the Q-network [35]."; p. 1424, D. Training and Test Phases "L = ... (yu, qu) (3) where f(yu,qu) is the loss function given by f(yu, qu) = ...yu-qu [said comparison result] ... (4)"; the weights of the target Q-network θ^ and the Q-network are periodically synced, [using or excluding specific said machine learning algorithms] and the weights of Q-network are calculated based on the loss, which comprising said comparison result, yu-qu [based on said comparison result])
The rationale for combining the teachings of Schelthoff, Kim and Park is the same as set forth in the rejection of claim 1.
In regard to claim 15, Schelthoff teaches: wherein said machine learning algorithms comprise decision tree, random forest, artificial neural network or Bayesian network. (Schelthoff, [0005] "There are approaches on waiting time predictions by forecasting models, wherein the forecasting models can be neural networks or data mining models to forecast cycle times.")
Claims 11-12 rejected under 35 U.S.C. 103 as being unpatentable over Schelthoff, Kim and Park as applied to claim 10, and in further view of Sun (CN 110084375 A, 20190802)
In regard to claim 11, Schelthoff and Kim do not teach, but Park teaches: wherein comparing said actual production result and said optimized schedule result comprises working out a difference between said predicted optimized runtimes and said actual runtime, and (Park, p. 1424, D. Training and Test Phases "L = ... f(yu, qu) (3) where f(yu [said actual production result, said actual runtime], qu [said optimized schedule result, said predict optimized runtime]) is the loss function given by f(yu, qu) = ...yu-qu [said comparison result, a difference] ... (4)"; TD Error = (actual performance/target q-value) - (predicted performance/current q-value)) (Park, p. 1421, II. Problem Definition "Oj,k can be processed on any machine among its alternatives, and the processing time of Oj,k is denoted as pj,k."; p. 1423, B. State, Action, and Reward "If the previous setup type is Oj′,k′ and ai is Oj,k, τ(si+1) is calculated as the sum of τ(si), pj,k, and σj′,k′,j,k... As a state transition takes place from si to si+1 , ri is observed... ri is defined as follows: ri = ... τ(si+1)... (1) [reward is based on τ(si+1), which is based on the processing time (said runtime)]"; p. 1424, Algorithm 1 "19: Set qu=Q(su,au;θ) 20: Set yu = ru+γ...max...Q^(su+1,a′;θ^) [yu is based on ru, which is based on the processing time (said runtime)]"; yu is actual accumulated rewards and processing time at time i+1 [said actual runtime] , qu is predicted accumulated rewards and processing time at time i [said predict optimized runtime], therefore yu-qu is [a difference between said predict optimized runtime and said actual runtime])
The rationale for combining the teachings of Schelthoff, Kim and Park is the same as set forth in the rejection of claim 1.
Schelthoff, Kim and Park do not teach, but Sun teaches: if said difference exceeds a set value, determining there is a disparity between said optimized schedule result and said actual production result, and (Sun p. 4 "using TDerror (deviation between the estimated value and the current value of that time sequence difference learning method ) method for updating its network parameter until reaching the maximum number or loss value less than the given threshold value")
if said difference is smaller than said set value, determining there is no disparity between said optimized schedule result and said actual production result. (Sun p. 4 "using TDerror (deviation between the estimated value and the current value of that time sequence difference learning method ) method for updating its network parameter until reaching the maximum number or loss value less than the given threshold value")
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Schelthoff, Kim and Park to incorporate the teachings of Sun by including using the loss as Temporal Difference (TD) loss in RL learning to update its network parameter. Doing so would enable optimal learning from each step and adapt quickly to the current environments. (Sun, p. 4 "evaluating the actor network according to the current environment state and the self-state selecting proper action, and according to the critic network using the policy gradient method to update its network parameter, so as to obtain the optimal control strategy")
In regard to claim 12, Schelthoff, Kim and Park do not teach, but Sun teaches: wherein when it is determined that there is a disparity between said optimized schedule result and said actual production result, (Sun p. 4 "using TDerror (deviation between the estimated value and the current value of that time sequence difference learning method ) method for updating its network parameter until reaching the maximum number or loss value less than the given threshold value")
feedback said comparison result as a basis for said machine learning algorithms. (Sun p. 4 "using TDerror (deviation between the estimated value and the current value of that time sequence difference learning method ) method for updating its network parameter [feedback said comparison result] until reaching the maximum number or loss value less than the given threshold value")
The rationale for combining the teachings of Schelthoff, Kim, Park and Sun is the same as set forth in the rejection of claim 12.
Claim 16 rejected under 35 U.S.C. 103 as being unpatentable over Schelthoff, Kim and Park as applied to claim 9, and in further view of Kozakowski ("Q-Value Weighted Regression: Reinforcement Learning with Limited Data" 20210212)
In regard to claim 16, Schelthoff, Kim and Park do not teach, but Kozakowski teaches: wherein acquiring said history data and said basic data comprises said machine learning algorithm decides whether to process said history data and said basic data with average, median or weighted treatment. (Kozakowski, p. 2, 2. Q-Value Weighted Regression "Improvement is achieved by weighting the actor loss by exponentiated advantage Aμ(s, a) of an action, skewing the regression towards the better-performing actions."; p. 3, 2.3 Q-Value Weighted Regression, a) Counterfactual Action Sampling "we calculate the advantage of the sampling policy μ based on a learned Q-function: Aμ(s, a)=Qμ(s, a)−Vμ(s)"; RL selects high-value experiences from a dataset, effectively "weighting" past data [weighted treatment] based on learned Q-values to improve decision-making, especially with limited samples, rather than just processing data randomly)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Schelthoff, Kim and Park to incorporate the teachings of Kozakowski by including Q-Value Weighted Regression (QWR) with the sampling policy μ. Doing so would achieve good performance in the RL learning (Kozakowski, p. 1, Abstract "We introduce Q-Value Weighted Regression (QWR), a simple RL algorithm that excels in these aspects. QWR is an extension of Advantage Weighted Regression (AWR)… We also verify that QWR performs well in the offline RL setting")
Claim 17 rejected under 35 U.S.C. 103 as being unpatentable over Schelthoff, Kim and Park as applied to claim 9, and in further view of Chau (US 20220171373 A1)
In regard to claim 17, Schelthoff, Kim and Park do not teach, but Chau teaches: wherein comparing said actual production result and said optimized schedule result comprises auto-regulating algorithms and (Chau, [0155] "FIG. 11 shows an example of high bias and high variance. High variance can indicate over-fitting. Over-fitting can be prevented using various methods. For example, regularization [auto-regulating algorithms] can be used, where large weights can be penalized using penalties or constraints on their squared values (L2 penalty) or absolute values (L1 penalty). [comparing said actual production result and said optimized schedule result]")
variation factors in information cross-validation based on said recipe groups or products. (Chau, [0148] Other validation methods can be used to validate the model. For example, an N-fold cross-validation [variation factors in information cross-validation] method may be used. In this method, the total dataset [based on said products] is divided into one final test set and N other subsets, where N is an integer greater than one."; [0135] "At 504, configuration and recipe data [input datasets includes recipe data of products] for the one or more tools are received."; [0005] "The processing chambers in the substrate processing tools usually repeat the same task on multiple substrates. The processing chambers operate based on a recipe that defines process parameters."; recipe data are sequencing information for the substrates/wafers [products]; also see Schelthoff teaches feature set (input data) includes recipe group or products (lots, wafers)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Schelthoff, Kim and Park to incorporate the teachings of Chau by including regularization and cross-validation. Doing so would prevent over-fitting and achieve lower error rate. (Chau, [0155] "Over-fitting can be prevented using various methods. For example, regularization can be used, where large weights can be penalized using penalties or constraints..."; [0148] "The model with the lowest validation error rate is deployed for use.")
Response to Arguments
Applicant's amendments with respect to the claim objections have been fully considered and are sufficient to overcome the objections. The objections have been withdrawn.
Applicant's arguments with respect to the rejection of the claims under 35 U.S.C. 101 have been fully considered but they are not persuasive:
Applicant argues: (p. 8) These limitations include: (1)... (2)… (3)… As defined and limited by the claim language, the above-identified judicial exceptions (i.e., history information module and dispatching module) are each integrated into a practical application. Specifically, the modules are implemented to process production lots within the process tools in an actual semiconductor fabrication plant. The claimed invention therefore applies the alleged judicial exceptions in a meaningful way to achieve a specific technological improvement in semiconductor manufacturing operations, rather than merely reciting them in the abstract…
Examiner answers: (1) The first limitation is a preamble and is given no patentable weight. (2) The second limitation recites storing data, and therefore is evaluated as an additional element as insignificant extra-solution activity – MPEP 2106.05(g) and WURC in step 2A prong 2 and step 2B. (3) The third limitation recites dispatching lots to tools, i.e. dispatching wafers to machines, and therefore is evaluated as an additional element as a particular technological environment or field of use – MPEP 2106.05(h). Simply adding “a semiconductor plant,” “fab tools,” or “the lots to be processed” is just a field of use, i.e. describing all of these operations occur in a semiconductor field does not make the claim eligible.
Applicant argues: (p. 8-9) Furthermore, (4)… (5)… As defined and limited by the claim language, the above-identified additional elements are amount to significantly more than the alleged judicial exception. Specifically, the claimed invention generates an optimized scheduling result that maximizes tool utilization across process tools while reducing the overall cycle time of lots to be processed in a semiconductor fabrication facility. The resulting production output demonstrably improves manufacturing efficiency and increases production capacity in lot scheduling and dispatching operations. Accordingly, the claims do not merely recite a judicial exception, but instead…
Examiner answers: The amended features “an optimized schedule result with maximum output for every said tool and reduced overall cycle time of said lots” and “an actual production result with improved production efficiency and capacity of said lot scheduling and dispatching” simply express the intended result of the claimed invention (in light of specification [0019] “in order to ensure maximum output for every tool”; [0003] “in hope of achieving the goals of intelligent manufacture, improving production capacity and reducing cycle time.”; [0020] “therefore the production capacity and efficiency may be improved.”), therefore those features are not given weight.
The specific process steps for achieving the claimed improvement will be given patentable weight. However, language reciting the intended result will not be considered. Also see 112(a) rejection.
Applicant's arguments with respect to the rejection of the claims under 35 U.S.C. 103 have been fully considered but they are not persuasive:
Applicant argues: (p. 11) This passage indicates that Schelthzoff determines the number of lots that are to undergo the same operation and uses this number as a basis for grouping such lots together for full-batch processing. Accordingly, it is evident that Schelthoff does not teach or suggest estimating runtimes and switching times associated with processing a particular type of lot on a particular type of tool using a particular type of recipe group.
Examiner answers: Schelthzoff teaches in [0072] “Similar operations are of the same operation type (independent of its product) and can be therefore produced in batches, if the equipments are capable of processing batches. Hence, a lot could be preferred if a lot of similar operations is waiting for execution to create full batches.” The paragraph describes a production strategy where multiple independent items requiring the same type of processing are grouped together into batches for manufacturing.
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.
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/S.C./Examiner, Art Unit 2146
/USMAAN SAEED/Supervisory Patent Examiner, Art Unit 2146
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