DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for priority under 35 U.S.C. 119 (a)-(d) to foreign application JP2023-061606 (filling date 04/05/2023). Certified copy of the foreign priority application has been filed with this application on 05/13/2024.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/02/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Allowable Subject Matter
Claim 6 would be allowable if it overcomes the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph and under 35 U.S.C. 10 , set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Claim 6 recites,
The information processing device according to claim 3, wherein the support information generation part calculates a unit transport time required for the transport processing per substrate based on the transport processing information,
calculates, based on the device setting information, a unit overhead time required for a preparation action per substrate of a time when the preparation action is performed before the substrate processing and after the substrate processing on the substrate held in the substrate processing unit, and
calculates the recipe available time by subtracting the unit transport time and the unit overhead time from a unit processing time per substrate converted from the target processing quantity.
A thorough search has been conducted for the subject matter with the most relevant prior art found to be discussed.
Huang (US20060030966A1) in ¶0050 teaches acquiring mechanical factor which includes vacuum robot transfer time and ATM robot transfer time, etc. may be considered as mechanical factors (¶0031-¶0032). ¶0052 teaches, calculating process time based on mechanical factor and required throughput. However, it doesn’t explicitly teach, calculating a unit transport time required for the transport processing per substrate based on the transport processing information, calculating, based on the device setting information, a unit overhead time required for a preparation action per substrate of a time when the preparation action is performed before the substrate processing and after the substrate processing on the substrate held in the substrate processing unit, and calculating the recipe available time by subtracting the unit transport time and the unit overhead time from a unit processing time per substrate converted from the target processing quantity, in view of the rest of the limitations of the parent claim.
Matsuo (US20100209225A1) in ¶0049 teaches, selecting a transfer route that provides the shortest time of transfer from a certain position to a target position and also teaches, the transfer times to reach the target position by the routes can be calculated from the sum of the moving times in these areas. However, it doesn’t explicitly teach, calculating a unit transport time required for the transport processing per substrate based on the transport processing information, calculating, based on the device setting information, a unit overhead time required for a preparation action per substrate of a time when the preparation action is performed before the substrate processing and after the substrate processing on the substrate held in the substrate processing unit, and calculating the recipe available time by subtracting the unit transport time and the unit overhead time from a unit processing time per substrate converted from the target processing quantity, in view of the rest of the limitations of the parent claim. Maenishi (US20100249971A1) in ¶0249 teaches, the target tact of the transportation lane 217 as 100 seconds by subtracting 3 seconds of the transporting time from 103 seconds of the target production time of the transportation lane 217. However, it doesn’t explicitly teach, calculating a unit transport time required for the transport processing per substrate based on the transport processing information, calculating, based on the device setting information, a unit overhead time required for a preparation action per substrate of a time when the preparation action is performed before the substrate processing and after the substrate processing on the substrate held in the substrate processing unit, and calculating the recipe available time by subtracting the unit transport time and the unit overhead time from a unit processing time per substrate converted from the target processing quantity, in view of the rest of the limitations of the parent claim.
No other art could be found which alone or in combination teaches, calculating a unit transport time required for the transport processing per substrate based on the transport processing information, calculating, based on the device setting information, a unit overhead time required for a preparation action per substrate of a time when the preparation action is performed before the substrate processing and after the substrate processing on the substrate held in the substrate processing unit, and calculating the recipe available time by subtracting the unit transport time and the unit overhead time from a unit processing time per substrate converted from the target processing quantity, in view of the rest of the limitations of the parent claim.
Therefore, claim 6 would be allowable if it overcomes the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph and under 35 U.S.C. 10 , set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
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.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
a target processing quantity reception part that receives a target processing quantity of the substrate per unit time of a time when a processing action repeating the substrate processing and the transport processing on a plurality of substrates is performed in the substrate processing apparatus, in claim 1.
a device information acquisition part that acquires device information, in claim 1
a support information generation part that generates support information, in claim 1
the support information generation part calculates a unit transport time required for the transport processing per substrate, in claim 4
the support information generation part calculates a unit transport time required for the transport processing per substrate by adding up the unit transport processing time according to the transport route, in claim 5
the support information generation part calculates a unit transport time required for the transport processing per substrate based on the transport processing information, in claim 6
the support information generation part generates the support information in claim 7
a machine learning part that causes the learning model to learn a correlation between the input data and the output data by inputting the plurality of sets of learning data to the learning model, in claim 9
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
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.
Claim limitation
a target processing quantity reception part that receives a target processing quantity of the substrate per unit time of a time when a processing action repeating the substrate processing and the transport processing on a plurality of substrates is performed in the substrate processing apparatus, in claim 1.
a device information acquisition part that acquires device information, in claim 1
a support information generation part that generates support information, in claim 1
the support information generation part calculates a unit transport time required for the transport processing per substrate, in claim 4
the support information generation part calculates a unit transport time required for the transport processing per substrate by adding up the unit transport processing time according to the transport route, in claim 5
the support information generation part calculates a unit transport time required for the transport processing per substrate based on the transport processing information, in claim 6
the support information generation part generates the support information in claim 7
a machine learning part that causes the learning model to learn a correlation between the input data and the output data by inputting the plurality of sets of learning data to the learning model, in claim 9
invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
With regards to target processing quantity reception part, as recited in claims 1, the published specification in ¶0095 recites a target processing quantity reception part performing the function, however, there is no disclosure of any particular structure, either explicitly or inherently, describing a target processing quantity reception part. As would be recognized by those of ordinary skill in the art, the function described can be performed in any number of ways in hardware, software or a combination of the two. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which structure or structures perform(s) the claimed function.
With regards to device information acquisition part, as recited in claims 1, the published specification in ¶0096 recites a device information acquisition part performing the function, however, there is no disclosure of any particular structure, either explicitly or inherently, describing a device information acquisition part. As would be recognized by those of ordinary skill in the art, the function described can be performed in any number of ways in hardware, software or a combination of the two. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which structure or structures perform(s) the claimed function.
With regards to support information generation part, as recited in claims 1 and 4-6, the published specification in ¶0106-¶0107 recites a support information generation part performing the function, however, there is no disclosure of any particular structure, either explicitly or inherently, describing a support information generation part. As would be recognized by those of ordinary skill in the art, the function described can be performed in any number of ways in hardware, software or a combination of the two. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which structure or structures perform(s) the claimed function.
With regards to support information generation part, as recited in claims 7, the published specification in ¶0125 recites a support information generation part performing the function, however, there is no disclosure of any particular structure, either explicitly or inherently, describing a support information generation part. As would be recognized by those of ordinary skill in the art, the function described can be performed in any number of ways in hardware, software or a combination of the two. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which structure or structures perform(s) the claimed function.
With regards to machine learning part, as recited in claims 9, the published specification in ¶0136 recites a machine learning part performing the function, however, there is no disclosure of any particular structure, either explicitly or inherently, describing a machine learning part. As would be recognized by those of ordinary skill in the art, the function described can be performed in any number of ways in hardware, software or a combination of the two. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which structure or structures perform(s) the claimed function.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claims 2-3 which have not been addressed above are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph due to their dependency on independent claim 1.
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.
Claim 1-7 and 9 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.
With regards to target processing quantity reception part, as described above, the disclosure does not provide adequate structure to perform the claimed function. The specification does not demonstrate that applicant has made an invention that achieves the claimed function because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention.
With regards to device information acquisition part, as described above, the disclosure does not provide adequate structure to perform the claimed function. The specification does not demonstrate that applicant has made an invention that achieves the claimed function because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention.
With regards to support information generation part, as described above, the disclosure does not provide adequate structure to perform the claimed function. The specification does not demonstrate that applicant has made an invention that achieves the claimed function because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention.
With regards to machine learning part, as described above, the disclosure does not provide adequate structure to perform the claimed function. The specification does not demonstrate that applicant has made an invention that achieves the claimed function because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention.
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.
Claim 1-9 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract without significantly more.
With regards to claim 1, the claim(s) recite(s), “a support information generation part that generates support information comprising a recipe available time which is available for the substrate processing performed according to the recipe information, based on the target processing quantity received by the target processing quantity reception part and the device information acquired by the device information acquisition part.” This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a support information generating unit. That is, other than a support information generation part being claimed as performing this function, nothing in the claim element precludes the step from practically being performed in the mind. For example, without any specific limitation narrowing the generation process of the recipe available time, a human mind is capable of generating recipe available time, based target processing quantity and the device information. The mere nominal recitation of a support information generation part to perform this determination does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process.
This judicial exception is not integrated into a practical application. Claim recites additional elements directed to, An information processing device supporting an operation of a substrate processing apparatus, the substrate processing apparatus comprising: a substrate processing unit that performs a substrate processing on a substrate according to recipe information indicating a processing content of the substrate processing; and a transport processing unit that performs a transport processing of transporting the substrate before the substrate processing and after the substrate processing, the information processing device comprising: a target processing quantity reception part that receives a target processing quantity of the substrate per unit time of a time when a processing action repeating the substrate processing and the transport processing on a plurality of substrates is performed in the substrate processing apparatus; a device information acquisition part that acquires device information comprising transport processing information which defines an action state of the transport processing of the time when the processing action is performed. With regards to An information processing device supporting an operation of a substrate processing apparatus, this limitation merely invokes information processing device as a tool to execute the abstract idea, which fails to integrate the judicial exception into a practical application. (see MPEP 2106.05(f)). With regards to a substrate processing unit that performs a substrate processing on a substrate according to recipe information indicating a processing content of the substrate processing; and a transport processing unit that performs a transport processing of transporting the substrate before the substrate processing and after the substrate processing, this limitation merely indicates a field of use or technical environment of substrate processing in which to implement the abstract idea and fails to integrate the judicial exception into a practical application. (see MPEP 2106.05(h)). With regards to, a target processing quantity reception part that receives a target processing quantity of the substrate per unit time of a time when a processing action repeating the substrate processing and the transport processing on a plurality of substrates is performed in the substrate processing apparatus; a device information acquisition part that acquires device information comprising transport processing information which defines an action state of the transport processing of the time when the processing action is performed, these limitations are directed to acquiring target processing quantity information and device information, which under broadest reasonable interpretation, is directed to mere data gathering and insignificant extra solution activity for the purpose of executing the abstract idea. Therefore, these limitations do not integrate a judicial exception. (see MPEP 2106.05(g)).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim recites additional elements directed to, An information processing device supporting an operation of a substrate processing apparatus, the substrate processing apparatus comprising: a substrate processing unit that performs a substrate processing on a substrate according to recipe information indicating a processing content of the substrate processing; and a transport processing unit that performs a transport processing of transporting the substrate before the substrate processing and after the substrate processing, the information processing device comprising: a target processing quantity reception part that receives a target processing quantity of the substrate per unit time of a time when a processing action repeating the substrate processing and the transport processing on a plurality of substrates is performed in the substrate processing apparatus; a device information acquisition part that acquires device information comprising transport processing information which defines an action state of the transport processing of the time when the processing action is performed. With regards to An information processing device supporting an operation of a substrate processing apparatus, this limitation merely invokes information processing device as a tool to execute the abstract idea, which fails to provide significantly more than the judicial exception. (see MPEP 2106.05(f)). With regards to a substrate processing unit that performs a substrate processing on a substrate according to recipe information indicating a processing content of the substrate processing; and a transport processing unit that performs a transport processing of transporting the substrate before the substrate processing and after the substrate processing, this limitation merely indicates a field of use or technical environment of substrate processing in which to implement the abstract idea and fails to provide significantly more than the judicial exception. (see MPEP 2106.05(h)). With regards to, a target processing quantity reception part that receives a target processing quantity of the substrate per unit time of a time when a processing action repeating the substrate processing and the transport processing on a plurality of substrates is performed in the substrate processing apparatus; a device information acquisition part that acquires device information comprising transport processing information which defines an action state of the transport processing of the time when the processing action is performed, these limitations are directed to acquiring target processing quantity information and device information, which under broadest reasonable interpretation, is directed to mere data gathering and insignificant extra solution activity for the purpose of executing the abstract idea. These elements are recited in a generic manner and are directed to activity that are well-understood, routine and conventional in the field of computer implemented processes. Courts have found gathering data (Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)) to be well‐understood, routine, and conventional when recited as insignificant extra-solution activity (see MPEP 2106.05(d). Therefore, these limitations do not provide significantly more than the judicial exception. (see MPEP 2106.05(d)).
Claim 2 depends on claim 1 therefore it recites the abstract idea of claim 1. Claim 2 further recites, The information processing device according to claim 1, wherein the transport processing unit is composed of a plurality of transport processing units in which a transport route of a time when transporting the substrate in the transport processing is capable of being selected, and the transport processing information comprises: the transport route selected when the processing action is performed; and a unit transport processing time required for each of the transport processings performed by the plurality of transport processing units when the processing action is performed. These limitations limit the reach of the judicial exception to a substrate transport processing unit in which transport route can be selected and the transport processing information to particular types of information. This amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use and as such fails to integrate the judicial exception into a practical application or provide significantly more (see MPEP 2106.05(h)).
Claim 3 depends on claim 1 therefore it recites the abstract idea of claim 1. Claim 3 further recites, the device information comprises device setting information that defines an action content of the substrate processing apparatus of the time when the processing action is performed, by setting a setting value respectively for each of a plurality of device setting items. These limitations limit the reach of the judicial exception to a substrate transport processing unit in which device information comprises particular types of information. This amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use and as such fails to integrate the judicial exception into a practical application or provide significantly more (see MPEP 2106.05(h)).
Claim 4 depends on claim 1 therefore it recites the abstract idea of claim 1. Claim 4 further recites, the support information generation part calculates a unit transport time required for the transport processing per substrate based on the transport processing information, and calculates the recipe available time by subtracting the unit transport time from a unit processing time per substrate converted from the target processing quantity. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a support information generating unit. That is, other than a support information generation part being claimed as performing this function, nothing in the claim element precludes the step from practically being performed in the mind. For example, without any specific limitation narrowing the calculation process, a human mind is capable of calculating a unit transport time required for the transport processing per substrate based on the transport processing information and recipe available time by subtracting the unit transport time from a unit processing time per substrate converted from the target processing quantity. mere nominal recitation of a support information generation part to perform this determination does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process.
Claim 5 depends on claim 1 therefore it recites the abstract idea of claim 1. Claim 5 further recites, the support information generation part calculates a unit transport time required for the transport processing per substrate by adding up the unit transport processing time according to the transport route, and calculates the recipe available time by subtracting the unit transport time from a unit processing time per substrate converted from the target processing quantity. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a support information generating unit. That is, other than a support information generation part being claimed as performing this function, nothing in the claim element precludes the step from practically being performed in the mind. For example, without any specific limitation narrowing the calculation process, a human mind is capable of a unit transport time required for the transport processing per substrate by adding up the unit transport processing time according to the transport route and recipe available time by subtracting the unit transport time from a unit processing time per substrate converted from the target processing quantity. Mere nominal recitation of a support information generation part to perform this determination does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process.
Claim 6 depends on claim 1 therefore it recites the abstract idea of claim 1. Claim 6 further recites the support information generation part calculates a unit transport time required for the transport processing per substrate based on the transport processing information, calculates, based on the device setting information, a unit overhead time required for a preparation action per substrate of a time when the preparation action is performed before the substrate processing and after the substrate processing on the substrate held in the substrate processing unit, and calculates the recipe available time by subtracting the unit transport time and the unit overhead time from a unit processing time per substrate converted from the target processing quantity. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a support information generating unit. That is, other than a support information generation part being claimed as performing this function, nothing in the claim element precludes the step from practically being performed in the mind. For example, a human mind is capable of calculating the claimed unit transport time, the unit overhead time and the recipe available time. Mere nominal recitation of a support information generation part to perform this determination does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process.
Claim 7 depends on claim 1 therefore it recites the abstract idea of claim 1. Claim 7 further recites, the support information generation part generates the support information for the target processing quantity and the device information by inputting the target processing quantity received by the target processing quantity reception part and the device information acquired by the device information acquisition part, to a learning model that has been caused to learn a correlation of the target processing quantity and the device information with the support information by machine learning. This limitation is directed to using a machine learning model as tool to perform a function it performs in its ordinary capacity, which is to learn correlation between information. Therefore, merely applying the abstract idea by invoking a machine learning model as tool fails to integrate the judicial exception into a practical application or provide significantly more (see 2106.05(f)).
Claim 8 recites similar limitation as claim 1 and therefore is also directed to an abstract idea for the same reason as claim 1 above. Claim 8 further recites, An inference device comprising a memory and a processor and performing the function of inferring support information comprising a recipe available time. These additional limitations directed to an inference device is merely the inference device as a tool to perform functions it performs in its ordinary capacity, which is to infer information based on presented information. Therefore, merely applying the abstract idea by invoking an inference device as tool fails to integrate the judicial exception into a practical application or provide significantly more (see 2106.05(f)).
With regards to claim 9, the claim(s) recite(s), “learn a correlation between the input data and the output data.” This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a machine learning part and learning model. That is, other than a machine learning part and learning model being claimed as performing this function, nothing in the claim element precludes the step from practically being performed in the mind. For example, without any specific limitation narrowing the learning process, a human mind is capable of identifying and learning correlation between some input data and output data. The mere nominal recitation of machine learning part and learning model to perform this function does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process.
This judicial exception is not integrated into a practical application. Claim recites additional elements directed to, A machine learning device generating a learning model for supporting an operation of a substrate processing apparatus, the substrate processing apparatus comprising: a substrate processing unit that performs a substrate processing on a substrate according to recipe information indicating a processing content of the substrate processing; and a transport processing unit that performs a transport processing of transporting the substrate before the substrate processing and after the substrate processing, the machine learning device comprising: a learning data storage part that stores a plurality of sets of learning data composed of an input data and an output data; a machine learning part that causes the learning model to learn a correlation between the input data and the output data by inputting the plurality of sets of learning data to the learning model; and a learned model storage part that stores the learning model which has been caused to learn the correlation by the machine learning part, wherein the input data is a target processing quantity of the substrate per unit time of a time when a processing action repeating the substrate processing and the transport processing on a plurality of substrates is performed in the substrate processing apparatus; and device information comprising transport processing information that defines an action state of the transport processing of the time when the processing action is performed, and the output data is support information comprising a recipe available time that is available for the substrate processing performed according to the recipe information.” With regards to, A machine learning device generating a learning model for supporting an operation of a substrate processing apparatus, this limitation merely invokes the machine learning device and the machine learning model as a tool to execute the abstract idea, which fails to integrate the judicial exception into a practical application. (see MPEP 2106.05(f)). With regards to a substrate processing unit that performs a substrate processing on a substrate according to recipe information indicating a processing content of the substrate processing; and a transport processing unit that performs a transport processing of transporting the substrate before the substrate processing and after the substrate processing, this limitation merely indicates a field of use or technical environment of substrate processing in which to implement the abstract idea and fails to integrate the judicial exception into a practical application. (see MPEP 2106.05(h)). With regards to a learning data storage part that stores a plurality of sets of learning data composed of an input data and an output data, and a learned model storage part that stores the learning model which has been caused to learn the correlation by the machine learning part, this limitation merely invokes a computer storage component to perform a function it performs in its ordinary capacity (i.e. store data), therefore fails to integrate the judicial exception into a practical application. (see MPEP 2106.05(f)). With regards to, a machine learning part that causes the learning model to learn a correlation between the input data and the output data by inputting the plurality of sets of learning data to the learning model¸ this limitation merely invokes a learning mode to perform a function it performs in its ordinary capacity (i.e. learn from data), therefore fails to integrate the judicial exception into a practical application. (see MPEP 2106.05(f)). With regards to, the input data is a target processing quantity of the substrate per unit time of a time when a processing action repeating the substrate processing and the transport processing on a plurality of substrates is performed in the substrate processing apparatus; and device information comprising transport processing information that defines an action state of the transport processing of the time when the processing action is performed, and the output data is support information comprising a recipe available time that is available for the substrate processing performed according to the recipe information, these limitations limit the input and output data to specific types of data, which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use, because limiting the data to these particular types of data does not alter or affect the process step of learning correlation between the data. Therefore, these limitations fail to integrate the judicial exception into a practical application. (see MPEP 2106.05(h))
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim recites additional elements directed to, A machine learning device generating a learning model for supporting an operation of a substrate processing apparatus, the substrate processing apparatus comprising: a substrate processing unit that performs a substrate processing on a substrate according to recipe information indicating a processing content of the substrate processing; and a transport processing unit that performs a transport processing of transporting the substrate before the substrate processing and after the substrate processing, the machine learning device comprising: a learning data storage part that stores a plurality of sets of learning data composed of an input data and an output data; a machine learning part that causes the learning model to learn a correlation between the input data and the output data by inputting the plurality of sets of learning data to the learning model; and a learned model storage part that stores the learning model which has been caused to learn the correlation by the machine learning part, wherein the input data is a target processing quantity of the substrate per unit time of a time when a processing action repeating the substrate processing and the transport processing on a plurality of substrates is performed in the substrate processing apparatus; and device information comprising transport processing information that defines an action state of the transport processing of the time when the processing action is performed, and the output data is support information comprising a recipe available time that is available for the substrate processing performed according to the recipe information.” With regards to, A machine learning device generating a learning model for supporting an operation of a substrate processing apparatus, this limitation merely invokes the machine learning device and the machine learning model as a tool to execute the abstract idea, which fails to provide significantly more than the judicial exception. (see MPEP 2106.05(f)). With regards to a substrate processing unit that performs a substrate processing on a substrate according to recipe information indicating a processing content of the substrate processing; and a transport processing unit that performs a transport processing of transporting the substrate before the substrate processing and after the substrate processing, this limitation merely indicates a field of use or technical environment of substrate processing in which to implement the abstract idea and fails to provide significantly more than the judicial exception. (see MPEP 2106.05(h)). With regards to a learning data storage part that stores a plurality of sets of learning data composed of an input data and an output data, and a learned model storage part that stores the learning model which has been caused to learn the correlation by the machine learning part, this limitation merely invokes a computer storage component to perform a function it performs in its ordinary capacity (i.e. store data), therefore fails to provide significantly more than the judicial exception. (see MPEP 2106.05(f)). With regards to, a machine learning part that causes the learning model to learn a correlation between the input data and the output data by inputting the plurality of sets of learning data to the learning model¸ this limitation merely invokes a learning mode to perform a function it performs in its ordinary capacity (i.e. learn from data), therefore fails to provide significantly more than the judicial exception (see MPEP 2106.05(f)). With regards to, the input data is a target processing quantity of the substrate per unit time of a time when a processing action repeating the substrate processing and the transport processing on a plurality of substrates is performed in the substrate processing apparatus; and device information comprising transport processing information that defines an action state of the transport processing of the time when the processing action is performed, and the output data is support information comprising a recipe available time that is available for the substrate processing performed according to the recipe information, these limitations limit the input and output data to specific types of data, which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use, because limiting the data to these particular types of data does not alter or affect the process step of learning correlation between the data. Therefore, these limitations fail to provide significantly more than the judicial exception. (see MPEP 2106.05(h))
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1 and 3 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Huang (US20060030966A1)
Regarding Claim 1,
Huang teaches, An information processing device supporting an operation of a substrate processing apparatus, the substrate processing apparatus comprising:
a substrate processing unit that performs a substrate processing on a substrate according to recipe information indicating a processing content of the substrate processing; and (¶0029 teaches processing chamber 119-127 to perform substrate processing according to a specific recipe)
a transport processing unit that performs a transport processing of transporting the substrate before the substrate processing and after the substrate processing, the information processing device comprising: (¶0017 teaches a robot (e.g., a vacuum robot 129), which may include one or more vacuum robot arms 131, for moving a substrate among the one or more processing chambers 119-127 and the one or more load locks 103 during electronic device manufacturing.)
a target processing quantity reception part that receives a target processing quantity of the substrate per unit time of a time when a processing action repeating the substrate processing and the transport processing on a plurality of substrates is performed in the substrate processing apparatus; (¶0049 teaches, In step 405, a required or desired manufacturing throughput for an electronic device manufacturing tool 101 is determined. For example, a user may determine a required electronic device manufacturing throughput for the tool 101, and input the required throughput into the control system 135 (e.g., via the operator control computer), which may include one or more microprocessors, microcontrollers and/or computer program products)
a device information acquisition part that acquires device information comprising transport processing information which defines an action state of the transport processing of the time when the processing action is performed; and (¶0050 teaches acquiring mechanical factor. ¶0031 and ¶0032 teaches vacuum robot transfer time and ATM robot transfer time, etc. may be considered as mechanical factors.)
a support information generation part that generates support information comprising a recipe available time which is available for the substrate processing performed according to the recipe information, based on the target processing quantity received by the target processing quantity reception part and the device information acquired by the device information acquisition part. (¶0052 teaches, calculating process time based on mechanical factor and required throughput)
Regarding Claim 3,
Huang teaches, The information processing device according to claim 1, wherein the device information comprises device setting information that defines an action content of the substrate processing apparatus of the time when the processing action is performed, by setting a setting value respectively for each of a plurality of device setting items. (¶0025 teaches. process factors which are related to the different possible ways the tool 101 may be used to perform various processes or to achieve a desired end result. ¶0032 teaches, The process time, pre-heat time, periodic maintenance (pm) cycle and pm cleaning recipe time, etc. may be considered process factors. ¶0051 teaches, process factor includes the heating time of a heating chamber for a particular customer specified process)
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 (i.e., changing from AIA to pre-AIA ) 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.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang (US20060030966A1) in view of Matsuo (US20100209225A1)
Regarding Claim 2,
Huang teaches, The information processing device according to claim 1, wherein the transport processing unit is composed of a plurality of transport processing units (Huang in ¶0014 and ¶0017 teaches, ATM robot, which may include one or more ATM robot arms and a robot (e.g., a vacuum robot), which may include one or more vacuum robot arms)
and the transport processing information comprises:
a unit transport processing time required for each of the transport processings performed by the plurality of transport processing units when the processing action is performed. (¶0031 and ¶0032 teaches transport processing information comprising vacuum robot transfer time and ATM robot transfer time.)
Huang doesn’t explicitly teach, in which a transport route of a time when transporting the substrate in the transport processing is capable of being selected, (Matsuo in ¶0049 teaches, selecting a transfer route that provides the shortest time of transfer from a certain position to a target position)
the transport route selected when the processing action is performed; and (Matsuo in ¶0049 teaches, selected transport route)
Matsuo is an art in the area of interest as it relates to a substrate transfer robot, a substrate transfer device, a semiconductor manufacturing apparatus, and a method for producing a semiconductor (see ¶0003). A combination of Matsuo with Huang would teach selecting transport route and also teach the transport processing information to comprise the transport route selected when the processing action is performed. It would have been obvious to one of ordinary skill in the art at the time the invention was filed to combined the teachings of Matsuo with Huang. One would have been motivated to do so because doing so would selecting a transfer route that provides the shortest transfer time, as taught by Matsuo in ¶0053.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang (US20060030966A1) in view of Maenishi (US20100249971A1)
Regarding Claim 4,
Huang teaches, The information processing device according to claim 1,
wherein the support information generation part calculates a unit transport time required for the transport processing per substrate based on the transport processing information, and (Huang in ¶0031 and ¶0032 teaches calculating vacuum robot transfer time and ATM robot transfer time.)
Huang doesn’t teach, calculates the recipe available time by subtracting the unit transport time from a unit processing time per substrate converted from the target processing quantity. (Huang in ¶0052 teaches, calculating process time, however it doesn’t teach calculating process time by subtracting the unit transport time from a unit processing time. Maenishi in ¶0249 teaches, the target tact of the transportation lane 217 as 100 seconds by subtracting 3 seconds of the transporting time from 103 seconds of the target production time of the transportation lane 217.)
Maenishi is an art in the area of interest as it relates to process planning in a production line. Huang in ¶0052 already teaches, determining processing time based on mechanical factor (¶0031 and ¶0032 teaches vacuum robot transfer time and ATM robot transfer time, etc. may be considered as mechanical factors) and required throughput. However, it didn’t teach subtracting the transfer time from the throughput. Maenishi in ¶0249 teaches determining the target by subtracting the transporting time from the target production time. One of ordinary skill in the art could modify Huang in view of the teachings of Maenishi to include calculating processing time by subtracting the transfer time from the throughput. One would have been motivated to do so because doing so because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang (US20060030966A1) in view of Matsuo (US20100209225A1) and further in view of Maenishi (US20100249971A1)
Regarding Claim 5,
Huang teaches, The information processing device according to claim 2, wherein the support information generation part calculates a unit transport time required for the transport processing per substrate (Huang in ¶0031 and ¶0032 teaches calculating vacuum robot transfer time and ATM robot transfer time.)
Huang doesn’t teach, calculates a unit transport time required for the transport processing per substrate by adding up the unit transport processing time according to the transport route, and (Huang doesn’t teach calculating a unit transport time required for the transport processing per substrate by adding up the unit transport processing time according to the transport route. Matsuo in ¶0049 teaches, The transfer times to reach the target position by the routes can be calculated from the sum of the moving times in these areas)
Matsuo is an art in the area of interest as it relates to a substrate transfer robot, a substrate transfer device, a semiconductor manufacturing apparatus, and a method for producing a semiconductor (see ¶0003). A combination of Matsuo with Huang would teach selecting transport route and also teach the transport processing information to comprise the transport route selected when the processing action is performed. It would have been obvious to one of ordinary skill in the art at the time the invention was filed to combined the teachings of Matsuo with Huang. One would have been motivated to do so because doing so would selecting a transfer route that provides the shortest transfer time, as taught by Matsuo in ¶0053.
Huang and Matsuo doesn’t teach, calculates the recipe available time by subtracting the unit transport time from a unit processing time per substrate converted from the target processing quantity. (Huang in ¶0052 teaches, calculating process time, however it doesn’t teach calculating process time by subtracting the unit transport time from a unit processing time. Maenishi (US20100249971A1) in ¶0249 teaches, the target tact of the transportation lane 217 as 100 seconds by subtracting 3 seconds of the transporting time from 103 seconds of the target production time of the transportation lane 217.)
Maenishi is an art in the area of interest as it relates to process planning in a production line. Huang in ¶0052 already teaches, determining processing time based on mechanical factor (¶0031 and ¶0032 teaches vacuum robot transfer time and ATM robot transfer time, etc. may be considered as mechanical factors) and required throughput. However, it didn’t teach subtracting the transfer time from the throughput. Maenishi in ¶0249 teaches determining the target by subtracting the transporting time from the target production time. One of ordinary skill in the art could modify Huang and Matsuo in view of the teachings of Maenishi to include calculating processing time by subtracting the transfer time from the throughput. One would have been motivated to do so because doing so because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim(s) 7-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang (US20060030966A1) in view of Chau (US20220171373A1)
Regarding Claim 7,
Huang doesn’t teach, The information processing device according to claim 1, wherein
the support information generation part generates the support information for the target processing quantity and the device information by inputting the target processing quantity received by the target processing quantity reception part and the device information acquired by the device information acquisition part, to a learning model that has been caused to learn a correlation of the target processing quantity and the device information with the support information by machine learning. (Chau in ¶0142 teaches, At 586, the data analyzer 406 detects patterns regarding PMs, WAC times, wait times, recipe times, and throughput for the tool(s) based on the analysis of the collected data. At 590, the data analyzer 406 provides the detected patterns and variations to the model generator 408 for use in model training using machine learning. ¶0139 teaches, At 518, based on the receive inputs, the model provides optimum scheduling parameter values to the system software the tool with which to process the set of wafers. ¶0162 teaches, The model 1204 can be trained using the discrete event simulator 1200 and reinforcement learning to self-explore and memorize the best scheduling decisions for a given state of a tool. This allows achieving and maintaining optimum throughput performance of the tool.)
Chau is an art in the area of interest as relates to substrate processing systems. A combination of Chau with Huang would allow the combined system to use a learning model to generate support information. It would have been obvious to one of ordinary skill in the art at the time the invention was filed to combined the teachings of Chau with Huang. One would have been motivated to do so because doing so would allow determining the optimum scheduling parameters and minimize idle times for the one of the semiconductor substrates during processing in the plurality of processing chambers, as taught by Chau in ¶0008.
Regarding Claim 8,
Huang teaches, An … device comprising a memory and a processor and supporting an operation of a substrate processing apparatus, the substrate processing apparatus comprising: (¶0006 and ¶0019 teaches, control system including microprocessor and memory)
a substrate processing unit that performs a substrate processing on a substrate according to recipe information indicating a processing content of the substrate processing; and (¶0029 teaches processing chamber 119-127 to perform substrate processing according to a specific recipe)
a transport processing unit that performs a transport processing of transporting the substrate before the substrate processing and after the substrate processing, the …. device performing: (¶0017 teaches a robot (e.g., a vacuum robot 129), which may include one or more vacuum robot arms 131, for moving a substrate among the one or more processing chambers 119-127 and the one or more load locks 103 during electronic device manufacturing.)
a target processing quantity reception processing of receiving a target processing quantity of the substrate per unit time of a time when a processing action repeating the substrate processing and the transport processing on a plurality of substrates is performed in the substrate processing apparatus; (¶0049 teaches, In step 405, a required or desired manufacturing throughput for an electronic device manufacturing tool 101 is determined. For example, a user may determine a required electronic device manufacturing throughput for the tool 101, and input the required throughput into the control system 135 (e.g., via the operator control computer), which may include one or more microprocessors, microcontrollers and/or computer program products)
a device information acquisition processing of acquiring device information comprising transport processing information that defines an action state of the transport processing of the time when the processing action is performed; and (¶0050 teaches acquiring mechanical factor. ¶0031 and ¶0032 teaches vacuum robot transfer time and ATM robot transfer time, etc. may be considered as mechanical factors.)
based on the target processing quantity and the device information, support information comprising a recipe available time that is available for the substrate processing performed according to the recipe information, upon reception of the target processing quantity in the target processing quantity reception processing and acquisition of the device information in the device information acquisition processing. (¶0052 teaches, calculating process time based on mechanical factor and required throughput)
Huang doesn’t teach, inference device, an inference processing of inferring, (Huang doesn’t teach using an inference device to determine a recipe available time. Chau in ¶0124 teaches, using machine learning model to determine best scheduling parameters for processing wafers.)
Chau is an art in the area of interest as relates to substrate processing systems. A combination of Chau with Huang would allow the combined system to use a learning model to generate support information. It would have been obvious to one of ordinary skill in the art at the time the invention was filed to combined the teachings of Chau with Huang. One would have been motivated to do so because doing so would allow determining the optimum scheduling parameters and minimize idle times for the one of the semiconductor substrates during processing in the plurality of processing chambers, as taught by Chau in ¶0008.
Regarding Claim 9,
Huang teaches, the substrate processing apparatus comprising: a substrate processing unit that performs a substrate processing on a substrate according to recipe information indicating a processing content of the substrate processing; and (¶0029 teaches processing chamber 119-127 to perform substrate processing according to a specific recipe)
a transport processing unit that performs a transport processing of transporting the substrate before the substrate processing and after the substrate processing, (¶0017 teaches a robot (e.g., a vacuum robot 129), which may include one or more vacuum robot arms 131, for moving a substrate among the one or more processing chambers 119-127 and the one or more load locks 103 during electronic device manufacturing.)
Huang doesn’t teach, A machine learning device generating a learning model for supporting an operation of a substrate processing apparatus, (¶0117-¶0119 teaches a computer implementing a machine learning model)
the machine learning device comprising: a learning data storage part that stores a plurality of sets of learning data composed of an input data and an output data; (Fig. 3 and ¶0115 teaches data storage 370 that stores
a machine learning part that causes the learning model to learn a correlation between the input data and the output data by inputting the plurality of sets of learning data to the learning model; (Chau 20220171373 in ¶0126 teaches, Using the selected machine learning method, the model generator 408 trains the model to predict optimum scheduling parameter values. The model is trained using data collected from preventive maintenance operations (PMs), recipe times, and wafer-less auto clean (WAC) times of tools, for example. The model is used to capture underlying relationships between scheduling parameter values and various wafer processing scenarios to make predictions accordingly, which eliminates the need to establish guidelines for best value selection.)
and a learned model storage part that stores the learning model which has been caused to learn the correlation by the machine learning part, (¶0017-¶0120 teaches computer with memory storing the learning model)
wherein the input data is a target processing quantity of the substrate per unit time of a time when a processing action repeating the substrate processing and the transport processing on a plurality of substrates is performed in the substrate processing apparatus; and (¶0142 teaches training the model using throughput data)
device information comprising transport processing information that defines an action state of the transport processing of the time when the processing action is performed, and (¶0189-¶0190 teaches training model using robot transfer times)
the output data is support information comprising a recipe available time that is available for the substrate processing performed according to the recipe information. (¶0139 teaches, At 518, based on the receive inputs, the model provides optimum scheduling parameter values to the system software the tool with which to process the set of wafers.)
Chau is an art in the area of interest as relates to substrate processing systems. A combination of Chau with Huang would allow the combined system to use a learning model to generate support information. It would have been obvious to one of ordinary skill in the art at the time the invention was filed to combined the teachings of Chau with Huang. One would have been motivated to do so because doing so would allow determining the optimum scheduling parameters and minimize idle times for the one of the semiconductor substrates during processing in the plurality of processing chambers, as taught by Chau in ¶0008.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ISTIAQUE AHMED whose telephone number is (571)272-7087. The examiner can normally be reached Monday to Thursday 10AM -6PM and alternate Fridays.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kenneth M Lo can be reached at (571) 272-9774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ISTIAQUE AHMED/Examiner, Art Unit 2116
/KENNETH M LO/Supervisory Patent Examiner, Art Unit 2116
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