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 foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. 18/589,462, filed on 02/28/2024. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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:
In regards to Claim 14:
an acquisitor configured to…
a learner configured to…
an estimator configured to…
In regards to Claim 15:
an acquisitor configured to…
a first learner configured to…
a first estimator configured to…
an output configured to…
a first feature value storage configured to…
a second learner configured to…
a second estimator configured to…
a second feature value storage configured to…
a third learner configured to…
a third estimator configured to…
In regards to Claim 16:
a determiner configured to…
an instructor configured to…
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 (a)
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(s) 14-16 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.
Claim(s) 14-16 lack written description because the terms interpreted under 112(f) do not have a corresponding structure in the specification and therefore lack any written description as claimed.
Claim Rejections - 35 USC § 112 (b)
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(s) 14-16 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. (i.e., no association between the structure and the function can be found in the specification.) 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.
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(s) 1-13 is/are rejected under 35 U.S.C. 101 because the claimed inventio is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Regarding claim 1:
Step 1 (whether a claim is to a statutory category):
Yes, the claim is within the four statutory categories (a process, machine, manufacture or composition of matter). Claim 1 recites a method, therefore, falls within a process category. Claim 14 recites an apparatus, therefore, falls within a machine category.
Step 2A Prong 1 (whether a claim is directed to a judicial exception):
Yes, “acquiring time series data from a plurality of types of sensors having different sampling periods provided in a substrate processing apparatus” wherein obtaining data describes a mental process (i.e., observation) (see MPEP 2106.04(a)(2)(III)). And, “performing learning of first learning models that output information relating to the substrate processing apparatus in a case where the time series data from the sensors are input,” wherein obtaining data describes a mental process (i.e., observation, evaluation) (see (MPEP 2106.04(a)(2)(III))).
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate
the exception into a practical application):
No, “using each of the pieces of time series data having different sampling periods for each of the sensors individually; and” describes additional elements that integrate the judicial exception into a practical application with the words "apply it" (or an equivalent), such as mere (i.e., selecting a particular data source or type of data to be manipulated) to implement an abstract idea on a computer (see MPEP 2106.05(f)). And, “inputting the time series data from the sensors into the corresponding first learning models after learning to output an estimation result based on information obtained from the first learning models” recite additional elements that integrate the judicial exception into a practical application with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea (see MPEP 2106.05(f)).
Step 2B (Inventive concept):
No, “using each of the pieces of time series data having different sampling periods for each of the sensors individually; and” and further “inputting the time series data from the sensors into the corresponding first learning models after learning to output an estimation result based on information obtained from the first learning models” describes an additional element as “apply it”, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Regarding claim 2:
Further modifies the abstract idea of claim 1.
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate
the exception into a practical application):
No, “wherein the first learning model of each of the sensors is introduced for each of the edge devices corresponding to each of the sensors, and” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h). And, “the method further comprises executing learning processing of the first learning model and estimation processing by the first learning model in each of the edge devices” recite additional elements that integrate the judicial exception into a practical application with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea (see MPEP 2106.05(f)).
Step 2B (Inventive concept):
No, “wherein the first learning model of each of the sensors is introduced for each of the edge devices corresponding to each of the sensors, and” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h). and further in “the method further comprises executing learning processing of the first learning model and estimation processing by the first learning model in each of the edge devices” describes an additional element as “apply it”, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Regarding claim 3:
Further modifies the abstract idea of claim 1.
Step 2A Prong 1 (whether a claim is directed to a judicial exception):
Yes, “wherein the first learning models include at least one of an observation model for estimating a state of the substrate processing apparatus based on the time series data from the sensor, and” wherein estimating a state describes a mental process (i.e., evaluation) (see MPEP 2106.04(a)(2)(III)). And, “a control model for estimating a control value of the substrate processing apparatus based on the time series data from the sensor” wherein estimating a value describes a mental process (i.e., evaluation) (see MPEP 2106.04(a)(2)(III)).
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate the exception into a practical application):
No, “a control model” describes additional elements that integrate the judicial exception into a practical application with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea (see MPEP 2106.05(f)).
Step 2B (Inventive concept):
No, “a control model” describes an additional element as “apply it”, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Regarding claim 4:
Further modifies the abstract idea of claim 1.
Step 2A Prong 1 (whether a claim is directed to a judicial exception):
Yes, “extracting a first feature value from the time series data;” wherein the extracting involves arithmetic operation (see spec [0061]) involves calculating and comparing values recites a mathematical concept, as it involves mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), (I)).
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate the exception into a practical application):
No, “outputting the extracted first feature value to a first device in the substrate processing apparatus” is an additional element that amounts to adding insignificant extra-solution activity (i.e., post solution activity) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
Step 2B (Inventive concept):
No, “outputting the extracted first feature value to a first device in the substrate processing apparatus” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore, the additional element is directed to outputting the extracted feature value to a device is insignificant activity (i.e., such as mere data outputting) which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
Regarding claim 5:
Further modifies the abstract idea of claim 4.
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate the exception into a practical application):
No, “executing, in the first device, processing of storing the first feature value extracted from the time series data” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(d), 2106.05(g).). And, “processing of performing learning of a second learning model that outputs the information relating to the substrate processing apparatus in a case where the first feature value is input based on the stored first feature value, and” is an additional element that amounts to adding insignificant extra-solution activity (i.e., post solution activity) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g). And, “processing of inputting a newly acquired first feature value into a second learning model after learning to output an estimation result based on information obtained from the second learning model” does not recite additional elements that integrate the judicial exception into a practical application with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea (see MPEP 2106.05(f)).
Step 2B (Inventive concept):
No, “executing, in the first device, processing of storing the first feature value extracted from the time series data” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to storing and retrieving information in memory, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II). And, “processing of performing learning of a second learning model that outputs the information relating to the substrate processing apparatus in a case where the first feature value is input based on the stored first feature value, and” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore, the additional element is directed to outputting the extracted feature value to a device is insignificant activity (i.e., such as mere data outputting) which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II). And, “processing of inputting a newly acquired first feature value into a second learning model after learning to output an estimation result based on information obtained from the second learning model” describes an additional element as “apply it”, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Regarding claim 6:
Further modifies the abstract idea of claim 5.
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate the exception into a practical application):
No, “executing, in the first device, processing of outputting a relearning instruction of the first learning models based on an estimation result using the second learning model” does not recite additional elements that integrate the judicial exception into a practical application with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea (see MPEP 2106.05(f)).
Step 2B (Inventive concept):
No, “executing, in the first device, processing of outputting a relearning instruction of the first learning models based on an estimation result using the second learning model” describes an additional element as “apply it”, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Regarding claim 7:
Further modifies the abstract idea of claim 5.
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate the exception into a practical application):
No, “outputting a correction value for correcting an arithmetic result by the first learning models based on an arithmetic result by the second learning model” is an additional element that amounts to adding insignificant extra-solution activity (i.e., post solution activity) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
Step 2B (Inventive concept):
No, “outputting a correction value for correcting an arithmetic result by the first learning models based on an arithmetic result by the second learning model” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore, the additional element is directed to outputting the correction value is insignificant activity (i.e., such as mere data outputting) which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
Regarding claim 8:
Further modifies the abstract idea of claim 5.
Step 2A Prong 1 (whether a claim is directed to a judicial exception):
Yes, “extracting a second feature value of the time series data for each substrate processing apparatus; and” wherein the extracting involves arithmetic operation (see spec [0061]) involves calculating and comparing values recites a mathematical concept, as it involves mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), (I)).
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate the exception into a practical application):
No, “outputting the extracted second feature value to a second device outside the substrate processing apparatus” is an additional element that amounts to adding insignificant extra-solution activity (i.e., post solution activity) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
Step 2B (Inventive concept):
No, “outputting the extracted second feature value to a second device outside the substrate processing apparatus” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore, the additional element is directed to outputting the extracted value to a device is insignificant activity (i.e., such as mere data outputting) which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
Regarding claim 9:
Further modifies the abstract idea of claim 8.
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate the exception into a practical application):
No, “executing, in the second device, processing of storing the second feature value extracted for each substrate processing apparatus” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(d), 2106.05(g).). And, “processing of performing learning of a third learning model that outputs the information relating to the substrate processing apparatus in a case where the second feature value is input based on the stored second feature value, and” is an additional element that amounts to adding insignificant extra-solution activity (i.e., post solution activity) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g) And, “processing of inputting a newly acquired second feature value into a third learning model after learning to output an estimation result based on information obtained from the third learning model” recite additional elements that integrate the judicial exception into a practical application with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea (see MPEP 2106.05(f)).
Step 2B (Inventive concept):
No, “executing, in the second device, processing of storing the second feature value extracted for each substrate processing apparatus” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore, the additional element is directed to storing and retrieving information in memory, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II). And, “processing of performing learning of a third learning model that outputs the information relating to the substrate processing apparatus in a case where the second feature value is input based on the stored second feature value, and” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore, the additional element is directed to outputting the extracted feature value to a device is insignificant activity (i.e., such as mere data outputting) which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II). And, “processing of inputting a newly acquired second feature value into a third learning model after learning to output an estimation result based on information obtained from the third learning model” describes an additional element as “apply it”, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Regarding claim 10:
Further modifies the abstract idea of claim 9.
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate the exception into a practical application):
No, “executing, in the second device, processing of outputting a relearning instruction of the first learning models or the second learning model based on an estimation result using the third learning model” do not recite additional elements that integrate the judicial exception into a practical application with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea (see MPEP 2106.05(f)).
Step 2B (Inventive concept):
No, “executing, in the second device, processing of outputting a relearning instruction of the first learning models or the second learning model based on an estimation result using the third learning model” describes an additional element as “apply it”, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Regarding claim 11:
Further modifies the abstract idea of claim 9.
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate the exception into a practical application):
No, “outputting a correction value for correcting an arithmetic result by the first learning models based on an arithmetic result by the second learning model” is an additional element that amounts to adding insignificant extra-solution activity (i.e., post solution activity) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
Step 2B (Inventive concept):
No, “outputting a correction value for correcting an arithmetic result by the first learning models based on an arithmetic result by the second learning model” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore, the additional element is directed to outputting the correction value is insignificant activity (i.e., such as mere data outputting) which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
Regarding claim 12:
Further modifies the abstract idea of claim 1.
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate the exception into a practical application):
No, “introducing one of the learned first learning models to a new substrate processing apparatus in a case where the new substrate processing apparatus is installed” recite additional elements that integrate the judicial exception into a practical application with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea (see MPEP 2106.05(f)).
Step 2B (Inventive concept):
No, “introducing one of the learned first learning models to a new substrate processing apparatus in a case where the new substrate processing apparatus is installed” describes an additional element as “apply it”, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Regarding claim 13:
Further modifies the abstract idea of claim 1.
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate the exception into a practical application):
No, “further comprising: displaying a performance of each learning model in a list” is an additional element that amounts to adding insignificant extra-solution activity (i.e., post solution activity) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
Step 2B (Inventive concept):
No, “further comprising: displaying a performance of each learning model in a list” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore, the additional element is directed to outputting performance of the model is insignificant activity (i.e., such as mere data outputting) which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
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.
Claim(s) 1, 12-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hanada et al., (US20220114484A1), in view of Chen et al., (US20140122022A1).
Regarding claim 1 and analogous claim 14:
Hanada teaches:
performing learning of first learning models that output information relating to the substrate processing apparatus in a case where the time series data from the sensors are input, ([0028], “Preferably, the multiple learning models include a learning model generated by performing machine learning [performing learning] by use of only a training dataset obtained in an initial state of the substrate processing apparatus [data from the sensors], and a learning model generated by performing machine learning by use of a training dataset including a training dataset obtained after elapse of the initial state of the substrate processing apparatus”)
inputting the time series data from the sensors into the corresponding first learning models after learning to output an estimation result based on information obtained from the first learning models ([0005], “a device for determining (predicting) a failure of a substrate processing apparatus (specifically, an abnormality occurrence area and an estimated time for failure) (i.e., wherein inputting) by using a learning model (a support vector machine) generated by performing machine learning. In a determination device including a learning model as described in Patent Literatures 1 to 3, the learning model is generated by performing machine learning by using a predetermined training dataset in an initial state (for example, before shipping of the substrate processing apparatus). Thereafter, after elapse of the initial state (for example, after shipping of the substrate processing apparatus), machine learning (relearning) of a learning model is commonly practiced again by using or adding a new training dataset for the purpose of improving determination accuracy.”)
Hanada does not explicitly teach:
An information processing method comprising: acquiring time series data from a plurality of types of sensors having different sampling periods provided in a substrate processing apparatus;
using each of the pieces of time series data having different sampling periods for each of the sensors individually;
Chen teaches:
An information processing method comprising: acquiring time series data from a plurality of types of sensors having different sampling periods provided in a substrate processing apparatus; ([0027], “time series data [time series data] from multiple low-frequency sampling sensors”…[0063], “The time series data from multiple sensors [sensors] are received in time sequence”)
using each of the pieces of time series data having different sampling periods for each of the sensors individually; ([0029]-[0030], “a method for processing time series data from multiple sensors according to one embodiment of the present invention. The time series data processed according to the method of FIG. 2 come from multiple sensors (not shown), for example, from 1000 sensors”…[0032], “The time series data as processed according to the method shown in FIG. 2 all comprise a timestamp and a value associated with the timestamp. A timestamp represents sampling time of a value, simply denoted as “TS” or “T”; the value associated with the timestamp is also called a sampling value, simply denoted as “V.” This specification also uses a two-tuple “<timestamp, value>” to indicate a pair of associated timestamp and value, also called “timestamp-value pair.” For the convenience of depiction, without causing confusion, the value included in a time series data of a sensor is called “sensor value,” and the time or timestamp included in a time series data of a sensor is called “sensor time (i.e., wherein sampling periods for each sensor)”)
Chen and Hanada are both related to the same field of endeavor (i.e., time series sensor data). In view of the teachings of Chen it would have been obvious for a person of ordinary skill in the art to apply the teachings of Chen to Hanada before the effective filing date of the claimed invention in order to improve the efficiency of processing time series data generated by multiple sensors (Chen, [0002], “Time series data comprises timestamps and values associated with the timestamps, for example, comprising sampling time and sample values from sensors. In applications such as the energy system, intelligent power grid, etc., millions of sensors are usually deployed. These sensors generate massive time series data that are required to be persistently stored in a database for query. Before persistently storing time series data, it is first required to receive time series data in real-time from sensors and temporarily store the received time series data in a database of a temporary memory. The time series data are generally organized and stored temporarily according to individual sensors. This approach, although straightforward, has low utilization efficiency of buffers, and in particular so when processing massive time series data from a considerable number of low-frequency sampling sensors.”).
Regarding claim 12:
Hanada, as modified by Chen, teaches the method of claim 1.
Hanada further teaches:
further comprising: introducing one of the learned first learning models to a new substrate processing apparatus in a case where the new substrate processing apparatus is installed ([0011], “the determination device as described in Patent Literature 4 is a device which guides light generated in a chamber included in the substrate processing apparatus to a spectroscope installed outside the chamber, and determines (detects) the endpoint of etching on a substrate by measuring the intensity of light that has a predetermined wavelength with this spectroscope.”)
The motivation for claim 12 is the same as for claim 1.
Regarding claim 13:
Hanada, as modified by Chen, teaches the method of claim 1.
Hanada further teaches:
further comprising: displaying a performance of each learning model in a list ([0112], “Further, when the determination section 22 automatically performs switching of the learning model 25, for example, as in the example shown in FIG. 1C (however, the selection button 20 b is unnecessary in this case), it is possible to adopt a mode in which the display field 20 a of the selected learning model 25 (the first learning model 25 a or the second learning model 25 b), among the display fields 20 a of all the learning models 25, is lighting displayed on the monitor so as to be discriminable from other learning models 25. Alternatively, it is possible to display only the display field 20 a of the selected learning model 25 on the monitor (the display fields”)
The motivation for claim 13 is the same as for claim 1.
Claim(s) 2-6, 8-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hanada et al., in view of Chen et al., further in view of Tan et al., (US20200160208A1)
Regarding claim 2:
Hanada, as modified by Chen, teaches the method of claim 1.
Hanada, as modified by Chen, does not explicitly teach:
wherein the first learning model of each of the sensors is introduced for each of the edge devices corresponding to each of the sensors, and
the method further comprises executing learning processing of the first learning model and estimation processing by the first learning model in each of the edge devices
Tan teaches:
wherein the first learning model of each of the sensors is introduced for each of the edge devices corresponding to each of the sensors, and ([0045], “Each edge device may maintain its own edge-specific parameters for the ML model which include weights, coefficients, etc., which are unique to the edge device (i.e., wherein learning model of each sensor). However, the edge device may also contain a virtual component (virtual model 340 shown in FIG. 3A) which includes virtual parameters which are averaged, or otherwise shared among each of the edge devices in a cluster or group. The edge device may update or modify its current ML model based on both its specific parameters as well as group parameters included in the virtual component. Over time, updated models may begin to converge such as shown in the curves of FIG. 3B. In some embodiments, incoming data may be filtered first based on the virtual component that is shared among edge devices and then further refined based on edge-specific components that are unique to a particular edge device. The unique components may be unique to the location, data, type of sensors, etc., that are associated with or providing data to the edge device.”)
the method further comprises executing learning processing of the first learning model and estimation processing by the first learning model in each of the edge devices ([0048], “an edge device may trigger an AI model update based on various scenarios. For example, an edge-to-edge AI model update may be triggered by an edge device in response to the edge device receiving new data. As another example, the update may be triggered in response to the edge device detecting significant changes between a local (edge specific) model and a virtual model. As another example, the update may be triggered in response to another edge device pushing an updated model to the edge device”…[0055], “the method may include executing the updated ML model based on incoming data from the industrial asset to generate predictive information about the industrial asset. In some embodiments, the incoming data may include image data captured by an imaging sensor, and the updated ML model may be configured to detect regions of interest of the industrial asset based on the image data. In some embodiment, the incoming data may include time-series data captured by one or more sensors, and the updated ML model is configured to identify changes in an operating characteristic of the industrial asset based on the time-series data.”)
Tan and Hanada are both related to the same field of endeavor (i.e., machine learning). In view of the teachings of Tan it would have been obvious for a person of ordinary skill in the art to apply the teachings of Tan to Hanada before the effective filing date of the claimed invention in order to improve the efficiency of processing time series data generated by multiple sensors by processing ml models at edge devices (Tan, [0019], “According to various aspects, edge devices may use machine learning models to monitor and predict attributes associated with the industrial asset. Often, an ML model is processed on edge data that is collected from sensors on or about the industrial asset. For example, sensors may capture time-series data (temperature, pressure, vibration, etc.) about an industrial asset which can be processed using ML models to identify operating characteristics of the industrial asset that need to be changed. As another example, images may be captured of an industrial asset which can be processed using ML models to identify various image features or regions of interest (e.g., damage, wear, tear, etc.) to the industrial asset. In order for these models to operate accurately, the models must receive.”)
Regarding claim 3:
Hanada, as modified by Chen, teaches the method of claim 1.
Hanada further teaches:
wherein the first learning models include at least one of an observation model for estimating a state of the substrate processing apparatus based on the time series data from the sensor, and
Hanada, as modified by Chen, does not explicitly teach:
a control model for estimating a control value of the substrate processing apparatus based on the time series data from the sensor
Tan further teaches:
a control model for estimating a control value of the substrate processing apparatus based on the time series data from the sensor ([0019], “edge devices may use machine learning models to monitor and predict attributes associated with the industrial asset. Often, an ML model is processed on edge data that is collected from sensors (i.e., wherein substrate processing) on or about the industrial asset. For example, sensors may capture time-series data [time series data from the sensor] (temperature, pressure, vibration, etc.)”)
The motivation for claim 3 is the same as for claim 2.
Regarding claim 4:
Hanada, as modified by Chen, teaches the method of claim 1.
Hanada further teaches:
outputting the extracted first feature value to a first device in the substrate processing apparatus ([0033], “each of which is configured to receive input data created based on process log data of a substrate processing apparatus, and each of which is configured to output a determination result regarding production process in the substrate processing apparatus,”)
Hanada, as modified by Chen, does not explicitly teach:
further comprising: extracting a first feature value from the time series data; and
Tan further teaches:
further comprising: extracting a first feature value from the time series data; and ([0019], “For example, sensors may capture time-series data (temperature, pressure, vibration, etc.) about an industrial asset which can be processed using ML models to identify operating characteristics of the industrial asset that need to be changed. As another example, images may be captured of an industrial asset which can be processed using ML models to identify various image features or regions of interest (e.g., damage, wear, tear, etc.) to the industrial asset. In order for these models to operate accurately, the models must receive. In the example of image data, the image data may be used to detect a specific feature from an industrial asset (e.g., damage to a surface of the asset, etc.) A machine learning model may be trained to identify how likely such a feature exists in an image. A result of the ML model output may be a data point for the image where the data point is arranged in a multi-dimensional feature space with a likelihood of the feature existing within the image being arranged on one axis (e.g., y axis) and time on another axis (e.g., x axis)”)
The motivation for claim 4 is the same as for claim 2.
Regarding claim 5:
Hanada, as modified by Chen and Tan, teaches the method of claim 4.
Hanada further teaches:
processing of performing learning of a second learning model that outputs the information relating to the substrate processing apparatus in a case where the first feature value is input based on the stored first feature value, and ([0018], “device for a substrate processing apparatus, for performing determination regarding production process in the substrate processing apparatus, the production process determination device comprising: a process log acquisition section that is configured to acquire process log data of the substrate processing apparatus; and a determination section that is configured to create input data based on the process log data and to perform determination regarding production process in the substrate processing apparatus based on the input data, wherein the determination section includes multiple learning models each of which is configured to receive the input data and each of which is configured to output a determination result regarding the production process, and the multiple learning models (i.e., wherein multiple learning models is interpreted as first, second, etc.)”)
processing of inputting a newly acquired first feature value into a second learning model after learning to output an estimation result based on information obtained from the second learning model ([0018], “a determination section that is configured to create input data based on the process log data and to perform determination regarding production process in the substrate processing apparatus based on the input data, wherein the determination section includes multiple learning models (i.e., wherein multiple learning models is interpreted as first, second, etc.) each of which is configured to receive the input data and each of which is configured to output a determination result (i.e., wherein output an estimation result) regarding the production process, and the multiple learning models”)
Chen further teaches:
further comprising: executing, in the first device, processing of storing the first feature value extracted from the time series data ([0048], “the time series data may be compressed to generate compressed time series data. Correspondingly, in the first storing step, the storing time series data”)
The motivation for claim 5 is the same as for claim 1.
Regarding claim 6:
Hanada, as modified by Chen and Tan, teaches the method of claim 5.
Hanada further teaches:
further comprising: executing, in the first device, processing of outputting a relearning instruction of the first learning models based on an estimation result using the second learning model ([0099], “ it is also possible to perform relearning of the first learning model 25 a and the second learning model 25 b by using a new training dataset, and to perform relearning of the first learning model 25 a and the second learning model ”)
Regarding claim 8:
Hanada, as modified by Chen and Tan, teaches the method of claim 5.
Hanada further teaches:
outputting the extracted second feature value to a second device outside the substrate processing apparatus ([0011], “the determination device as described in Patent Literature 4 is a device which guides light generated in a chamber included in the substrate processing apparatus to a spectroscope installed outside the chamber, and determines (detects) the endpoint of etching on a substrate by measuring the intensity of light that has a predetermined wavelength with this spectroscope (i.e., wherein a second device is interpreted to be outside)”)
Tan further teaches:
further comprising: extracting a second feature value of the time series data for each substrate processing apparatus; and ([0019], “For example, sensors may capture time-series data (temperature, pressure, vibration, etc.) about an industrial asset which can be processed using ML models to identify operating characteristics of the industrial asset that need to be changed. As another example, images may be captured of an industrial asset which can be processed using ML models to identify various image features or regions of interest (e.g., damage, wear, tear, etc.) to the industrial asset. In order for these models to operate accurately, the models must receive. In the example of image data, the image data may be used to detect a specific feature from an industrial asset (e.g., damage to a surface of the asset, etc.) A machine learning model may be trained to identify how likely such a feature exists in an image (i.e., wherein under the broadest reasonable interpretation (BRI) first and second feature value). A result of the ML model output may be a data point for the image where the data point is arranged in a multi-dimensional feature space with a likelihood of the feature existing within the image being arranged on one axis (e.g., y axis) and time on another axis (e.g., x axis)”)
The motivation for claim 8 is the same as for claim 1.
Regarding claim 9:
Hanada, as modified by Chen and Tan, teaches the method of claim 5.
Hanada further teaches:
processing of performing learning of a third learning model that outputs the information relating to the substrate processing apparatus in a case where the second feature value is input based on the stored second feature value, and ([0018], “device for a substrate processing apparatus, for performing determination regarding production process in the substrate processing apparatus, the production process determination device comprising: a process log acquisition section that is configured to acquire process log data of the substrate processing apparatus; and a determination section that is configured to create input data based on the process log data and to perform determination regarding production process in the substrate processing apparatus based on the input data, wherein the determination section includes multiple learning models each of which is configured to receive the input data and each of which is configured to output a determination result regarding the production process, and the multiple learning models (i.e., wherein multiple learning models is interpreted as first, second, third etc.)”)
processing of inputting a newly acquired second feature value into a third learning model after learning to output an estimation result based on information obtained from the third learning model ([0018], “a determination section that is configured to create input data based on the process log data and to perform determination regarding production process in the substrate processing apparatus based on the input data, wherein the determination section includes multiple learning models (i.e., wherein multiple learning models is interpreted as first, second, third etc.) each of which is configured to receive the input data and each of which is configured to output a determination result (i.e., wherein output an estimation result) regarding the production process, and the multiple learning models”)
Chen further teaches:
further comprising: executing, in the second device, processing of storing the second feature value extracted for each substrate processing apparatus (([0048], “the time series data may be compressed to generate compressed time series data. Correspondingly, in the first storing step, the storing time series data”))
The motivation for claim 9 is the same as for claim 1.
Regarding claim 10:
Hanada, as modified by Chen and Tan, teaches the method of claim 5.
Hanada further teaches:
further comprising: executing, in the second device, processing of outputting a relearning instruction of the first learning models or the second learning model based on an estimation result using the third learning model ([0099], “When necessary, it is also possible to perform relearning of the first learning model 25 a and the second learning model 25 b by using a new training dataset, and to perform relearning”…[0119], “The determination section 22A of the production process determination device 20A according to the variant is configured to perform determination by using all of the first learning model 25 a, the second learning model 25 b, and the third learning model 25 c. Therefore, for example, as shown in FIG. 5B, all of the display fields 20 a of the selected learning models 25 (the first learning model 25 a to the third learning model”)
Claim(s) 7, 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hanada et al., in view of Chen et al., further in view of Tan et al., and Kengo et al., (JP2020161086A)
Regarding claim 7 and analogous claim 11:
Hanada, as modified by Chen and Tan, teaches the method of claim 5.
Hanada, as modified by Chen and Tan, does not explicitly teach:
further comprising: outputting a correction value for correcting an arithmetic result by the first learning models based on an arithmetic result by the second learning model
Kengo teaches:
further comprising: outputting a correction value for correcting an arithmetic result by the first learning models based on an arithmetic result by the second learning model ([0033]-[0035], “The correction value information 32 is information regarding the correction value C. For example, the correction value information 32 is updated at a predetermined timing. The control unit 2 calculates the probability value P in which the input data is in the first state based on the input data input from the sensor group 101, and updates the correction value C of the probability value P at a predetermined timing. The calculation unit 21 calculates the input data based on the neural network, and calculates the probability value that the state related to the input data is the first state. Specifically, the calculation unit 21 calculates the probability value P in which the input data is in the first state based on the learning model 31”)
Kengo and Hanada are both related to the same field of endeavor (i.e., machine learning). In view of the teachings of Kengo it would have been obvious for a person of ordinary skill in the art to apply the teachings of Kengo to Hanada before the effective filing date of the claimed invention in order to improve the efficiency of processing time series data generated by multiple sensors by processing ml models and correcting mechanism (Kengo, [0005], “to provide a control device and a correction method capable of easily correcting an error in a learning model.”)
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hanada et al., in view of Chen et al., further in view of Tan et al.
Regarding claim 15:
Hanada teaches:
A substrate processing system comprising: a plurality of substrate processing apparatuses configured to include edge devices connected to sensors and a host device connected to the edge devices, and execute substrate processing inside a chamber; and([0011], “here is known a determination device which includes a spectroscope as a device for determining (detecting) an endpoint of etching on a substrate in a substrate processing apparatus for performing etching processing on a substrate, though which does not use any learning model. Specifically, the determination device as described in Patent Literature 4 is a device which guides light generated in a chamber included in the substrate processing apparatus”)
a first learner configured to perform learning of a first learning model that outputs information relating to the substrate processing apparatus on which the sensor is provided in a case where the time series data from the one of a plurality of types of sensors is input based on the acquired time series data, ([0028], “Preferably, the multiple learning models include a learning model generated by performing machine learning [performing learning] by use of only a training dataset obtained in an initial state of the substrate processing apparatus [data from the sensors], and a learning model generated by performing machine learning by use of a training dataset including a training dataset obtained after elapse of the initial state of the substrate processing apparatus”)
a first estimator configured to input the time series data from the one of a plurality of types of sensors into the first learning model after learning to output an estimation result based on information obtained from the first learning model, and ([0005], “a device for determining (predicting) a failure of a substrate processing apparatus (specifically, an abnormality occurrence area and an estimated time for failure) (i.e., wherein inputting) by using a learning model (a support vector machine) generated by performing machine learning. In a determination device including a learning model as described in Patent Literatures 1 to 3, the learning model is generated by performing machine learning by using a predetermined training dataset in an initial state (for example, before shipping of the substrate processing apparatus). Thereafter, after elapse of the initial state (for example, after shipping of the substrate processing apparatus), machine learning (relearning) of a learning model is commonly practiced again by using or adding a new training dataset for the purpose of improving determination accuracy.”)
a second learner configured to perform learning of a second learning model that outputs the information relating to the substrate processing apparatus in a case where the first feature value is input based on the stored first feature value, ([0018], “device for a substrate processing apparatus, for performing determination regarding production process in the substrate processing apparatus, the production process determination device comprising: a process log acquisition section that is configured to acquire process log data of the substrate processing apparatus; and a determination section that is configured to create input data based on the process log data and to perform determination regarding production process in the substrate processing apparatus based on the input data, wherein the determination section includes multiple learning models each of which is configured to receive the input data and each of which is configured to output a determination result regarding the production process, and the multiple learning models (i.e., wherein multiple learning models is interpreted as first, second, etc.)”)
a second estimator configured to input a newly acquired first feature value into a second learning model after learning to output an estimation result based on information obtained from the second learning model, and ([0018], “a determination section that is configured to create input data based on the process log data and to perform determination regarding production process in the substrate processing apparatus based on the input data, wherein the determination section includes multiple learning models (i.e., wherein multiple learning models is interpreted as first, second, etc.) each of which is configured to receive the input data and each of which is configured to output a determination result (i.e., wherein output an estimation result) regarding the production process, and the multiple learning models”)
a third learner configured to perform learning of a third learning model that outputs the information relating to the substrate processing apparatus in a case where the second feature value is input based on the stored second feature value, and ([0018], “device for a substrate processing apparatus, for performing determination regarding production process in the substrate processing apparatus, the production process determination device comprising: a process log acquisition section that is configured to acquire process log data of the substrate processing apparatus; and a determination section that is configured to create input data based on the process log data and to perform determination regarding production process in the substrate processing apparatus based on the input data, wherein the determination section includes multiple learning models each of which is configured to receive the input data and each of which is configured to output a determination result regarding the production process, and the multiple learning models (i.e., wherein multiple learning models is interpreted as first, second, third etc.)”)
a third estimator configured to input a newly acquired second feature value to a third learning model after the learning to output an estimation result based on information obtained from the third learning model ([0018], “a determination section that is configured to create input data based on the process log data and to perform determination regarding production process in the substrate processing apparatus based on the input data, wherein the determination section includes multiple learning models (i.e., wherein multiple learning models is interpreted as first, second, third etc.) each of which is configured to receive the input data and each of which is configured to output a determination result (i.e., wherein output an estimation result) regarding the production process, and the multiple learning models”)
Chen teaches:
wherein each of the edge devices includes an acquisitor configured to acquire time series data from one of a plurality of types of sensors having different sampling periods, ([0027], “time series data [time series data] from multiple low-frequency sampling sensors”…[0063], “The time series data from multiple sensors [sensors] are received in time sequence”)
the host device includes a first feature value storage configured to store the first feature value input from the edge device, (([0048], “the time series data may be compressed to generate compressed time series data. Correspondingly, in the first storing step, the storing time series data”))
the apparatus group server includes a second feature value storage configured to store the second feature value received from the host device, (([0048], “the time series data may be compressed to generate compressed time series data. Correspondingly, in the first storing step, the storing time series data”))
Chen and Hanada are both related to the same field of endeavor (i.e., time series sensor data). In view of the teachings of Chen it would have been obvious for a person of ordinary skill in the art to apply the teachings of Chen to Hanada before the effective filing date of the claimed invention in order to improve the efficiency of processing time series data generated by multiple sensors (Chen, [0002], “Time series data comprises timestamps and values associated with the timestamps, for example, comprising sampling time and sample values from sensors. In applications such as the energy system, intelligent power grid, etc., millions of sensors are usually deployed. These sensors generate massive time series data that are required to be persistently stored in a database for query. Before persistently storing time series data, it is first required to receive time series data in real-time from sensors and temporarily store the received time series data in a database of a temporary memory. The time series data are generally organized and stored temporarily according to individual sensors. This approach, although straightforward, has low utilization efficiency of buffers, and in particular so when processing massive time series data from a considerable number of low-frequency sampling sensors.”).
Tan teaches:
an apparatus group server communicably connected to the plurality of substrate processing apparatuses ([0004], “edge devices sense or otherwise capture data and submit the data to a cloud platform or other central host. ”)
an output configured to output a first feature value extracted from the time series data to the host device, ([0019], “For example, sensors may capture time-series data (temperature, pressure, vibration, etc.) about an industrial asset which can be processed using ML models to identify operating characteristics of the industrial asset that need to be changed. As another example, images may be captured of an industrial asset which can be processed using ML models to identify various image features or regions of interest (e.g., damage, wear, tear, etc.) to the industrial asset. In order for these models to operate accurately, the models must receive. In the example of image data, the image data may be used to detect a specific feature from an industrial asset (e.g., damage to a surface of the asset, etc.) A machine learning model may be trained to identify how likely such a feature exists in an image. A result of the ML model output may be a data point for the image where the data point is arranged in a multi-dimensional feature space with a likelihood of the feature existing within the image being arranged on one axis (e.g., y axis) and time on another axis (e.g., x axis)”)
a transmitter configured to transmit a second feature value of the time series data extracted for each substrate processing apparatus to the apparatus group server, and([0019], “For example, sensors may capture time-series data (temperature, pressure, vibration, etc.) about an industrial asset which can be processed using ML models to identify operating characteristics of the industrial asset that need to be changed. As another example, images may be captured of an industrial asset which can be processed using ML models to identify various image features or regions of interest (e.g., damage, wear, tear, etc.) to the industrial asset. In order for these models to operate accurately, the models must receive. In the example of image data, the image data may be used to detect a specific feature from an industrial asset (e.g., damage to a surface of the asset, etc.) A machine learning model may be trained to identify how likely such a feature exists in an image (i.e., wherein under the broadest reasonable interpretation (BRI) first and second feature value). A result of the ML model output may be a data point for the image where the data point is arranged in a multi-dimensional feature space with a likelihood of the feature existing within the image being arranged on one axis (e.g., y axis) and time on another axis (e.g., x axis)”)
Tan and Hanada are both related to the same field of endeavor (i.e., machine learning). In view of the teachings of Tan it would have been obvious for a person of ordinary skill in the art to apply the teachings of Tan to Hanada before the effective filing date of the claimed invention in order to improve the efficiency of processing time series data generated by multiple sensors by processing ml models at edge devices (Tan, [0019], “According to various aspects, edge devices may use machine learning models to monitor and predict attributes associated with the industrial asset. Often, an ML model is processed on edge data that is collected from sensors on or about the industrial asset. For example, sensors may capture time-series data (temperature, pressure, vibration, etc.) about an industrial asset which can be processed using ML models to identify operating characteristics of the industrial asset that need to be changed. As another example, images may be captured of an industrial asset which can be processed using ML models to identify various image features or regions of interest (e.g., damage, wear, tear, etc.) to the industrial asset. In order for these models to operate accurately, the models must receive.”)
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hanada et al., in view of Chen et al., further in view of Tan et al. and in Song et al., (US20200160207A1)
Regarding claim 16:
Hanada, as modified by Chen and Tan, teach the system of claim 15:
Hanada, as modified by Chen and Tan do not explicitly teach:
wherein the host device and the apparatus group server include a determiner configured to determine whether or not it is necessary to update the first learning model based on an estimation result by a learning model provided in each of the host device and the apparatus group server, and
an instructor configured to instruct the edge device to relearn the first learning model in a case where it is determined that an update is necessary
Song teaches:
wherein the host device and the apparatus group server include a determiner configured to determine whether or not it is necessary to update the first learning model based on an estimation result by a learning model provided in each of the host device and the apparatus group server, and ([0020], “The example embodiments are directed to a cloud-edge system in which updates to machine learning (ML) models (also referred to as artificial intelligence models) are automatically triggered based on an edge device, a cloud platform, or any combination thereof. According to various embodiments, an edge device may monitor an accuracy of an ML model over time and initiate a model update with the cloud platform when the accuracy of the model falls below a predetermined threshold (i.e., wherein determine whether or not it is necessary under the broadest reasonable interpretation (BRI) the system determines an update when the model accuracy fall below a threshold)”…[0025], “To perform the ML model update, the edge-cloud system may generate a new data set of data points from received edge data which differ from the data pattern of the initial training data. A clustering program can run continuously on the new data set to identify clusters of continuous data points which differ by the predetermined distance from the data pattern in the initial training data set. When the clustering program identifies a cluster of data points within the new data set having a predetermined threshold size (e.g., 100 data points, etc.) the edge can request (or the cloud can automatically trigger) an update to the ML model. In response, the cloud may train a new ML model or updated an existing ML model based on the data within the new data set.”)
an instructor configured to instruct the edge device to relearn the first learning model in a case where it is determined that an update is necessary ([0025], “To perform the ML model update, the edge-cloud system may generate a new data set of data points from received edge data which differ from the data pattern of the initial training data. A clustering program can run continuously on the new data set to identify clusters of continuous data points which differ by the predetermined distance from the data pattern in the initial training data set. When the clustering program identifies a cluster of data points within the new data set having a predetermined threshold size (e.g., 100 data points, etc.) the edge can request (or the cloud can automatically trigger) an update to the ML model (i.e., wherein instruct under the broadest reasonable interpretation (BRI) is interpreted as a request (automatic trigger to relearn)). In response, the cloud may train a new ML model or updated an existing ML model based on the data within the new data set.”)
Song and Hanada are both related to the same field of endeavor (i.e., time series sensor data). In view of the teachings of Song it would have been obvious for a person of ordinary skill in the art to apply the teachings of Song to Hanada before the effective filing date of the claimed invention in order to improve the efficiency of processing time series data generated by multiple sensors and monitor machine learning model performance (Song, Abstract, “The example embodiments are directed to a system and methods for determining to update a machine learning model based on model degradation. In one example, the method may include one or more of receiving data acquired at an edge of an Internet of things (IoT) network from an industrial asset, executing a machine learning model with the received data as input to generate a predictive output associated with the industrial asset, determining that a performance of the machine learning model on the edge has degraded based on the generated predictive output of the machine learning model, and transmitting information about the degraded performance of the machine learning model to a central server within the IoT network.”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Naohara et al., (JP2021120984A) teaches acquiring time series data obtained in a substrate processing device that is relevant to the claimed invention.
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/AMINA MORENO BENOURAIDA/ Examiner, Art Unit 2129
/SCHYLER S SANKS/Primary Examiner, Art Unit 2129
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