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 .
DETAILED ACTION
1. This communication is a first office action, non-final rejection on the merits. Claims 1-19, as originally filed, are currently pending and have been considered below.
Priority
2. As required by M.P.E.P.201.14(c), acknowledgement is made of applicant’s claim for priority based on US continuation 17882172 filed on 2022/08/05 and WO continuation PCT/JP2021/004810 2021/02/09.
Information Disclosure Statement
3. The information disclosure statement (IDS) submitted on 11/12/24 and 7/24/24 has been considered. The submission is in compliance with the provisions of 37 CFR 1.97. Form PTO-1449 is signed and attached hereto.
Claim Rejections - 35 USC § 101
4. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea).
Claim 1:
Step Analysis 1: Statutory Category? Yes. The claim is a method claim.
2A - Prong 1: Judicial Exception Recited?
Yes. The claim recites the limitation of outputting alarm based on abnormality from detected information. This limitation, as drafted, is a method that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “outputting alarm based on abnormality,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “acquiring data related to alarm and outputting alarm based on abnormal sensor data, the claim encompasses a user simply acquiring data from sensor and outputting alarm from abnormal sensor data in his/her mind. The mere nominal recitation of acquiring data related to alarm and outputting alarm based on abnormal sensor data does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental.
2A - Prong 2: Integrated into a Practical Application?
No. The claim recites two additional elements: comparing data with threshold and identifying data closest to the threshold value for generating alarm performs the acquiring data information step. The acquiring step is recited at a high level of generality (i.e., as a general means of gathering data for use in the comparison step), and amounts to mere data gathering or manipulations, which is a form of insignificant extra-solution activity. The identifying and acquiring steps for comparing with threshold data is also recited at a high level of generality, and merely automates the data acquiring step. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component (the identifying, acquiring and comparing with threshold data).
The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (the identifying, acquiring and comparing with threshold data). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to the abstract idea.
2B: Claim provides an Inventive Concept?
No. As discussed with respect to Step 2A Prong Two, the additional elements comparing and identifying data for generating alarm in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the determination step was considered to be extra-solution activity in Step 2A, and thus it is re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The background of the example does not provide any indication that the driver circuit is anything other than a generic, and the Symantec, TLI, and OIP Techs. court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or determination of data over a driver circuit is a well understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
Accordingly, a conclusion that the determining step is well-understood, routine, conventional activity is supported under Berkheimer.
Claim 2:
Similar analysis applied as analyzing cause of alarm from acquired data.
Claim 3:
Similar analysis applied as analyzing cause of alarm stored and searching alarm ID and analysis item serial numbers and specifying item that cause the alarm.
Claim 4:
Similar analysis applied as acquiring data to the analysis items serial number defined by the serial number of the items.
Claim 5:
Similar analysis applied as method use alarm cause investigation table and cause analysis process based on item serial numbers corresponding to the alarm ID.
Claim 6:
Similar analysis applied as method use cause analysis process based on item serial numbers corresponding to the alarm ID repeatedly acquired.
Claim 7:
Similar analysis applied as method create FT factor diagrams for each alarm ID and creating alarm analysis table and alarm cause investigation table.
Claim 8:
Similar analysis applied as alarm outputted containing alarm ID indicating abnormality.
Claim 9:
Similar analysis applied as determining rank with a difference between apparatus data and threshold value and displaying relationship.
Claim 10:
Similar analysis applied as data for factor of generating the alarm.
Claim 11:
Similar analysis applied as containing sensor information related to pump or exhaust and analyzing a cause of the alarm.
Claim 12:
Similar analysis applied as process chamber controlling steps of claim 1.
Claim 13:
Similar analysis applied as data selected from group.
Claim 14:
Similar analysis applied as issuing alarm from selected data.
Claim 15:
Similar analysis applied as arranging in same floor.
Claim 16:
Similar analysis applied as determining rank of cause of alarm and displaying alarm rank in chronological on display screen.
Claim 17:
Similar analysis applied as in claim 1.
Claim 18:
Similar analysis applied as in claim 1.
Claim 19:
Similar analysis applied as in claim 1.
Claim Rejections - 35 USC § 103
5. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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.
6. The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) 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.
7. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
8. Claims 1-19 are rejected under 35 U.S.C. 103(a) as being unpatentable over Asai (US 20170285613 A1) (hereinafter Asai’ 613) in view of Asai (US 20220019191 A1) (hereinafter Asai’191).
Regarding claim 1, Asai’ 613 discloses a method of identifying a cause of an abnormality (para 191-192, FDC monitoring characteristic values output from apparatus 1 and identifying and classifying types of abnormality, diagnosing cause of abnormality, FDC monitoring characteristic values and classifying types of abnormality, that is, diagnosing cause or site of the abnormality), the method comprising:
(a) outputting an alarm indicating the abnormality detected based on sensor information (para 255, monitoring data output to derive information indicating operation state, para 310, indicating operation state of apparatus 1 by using device state monitoring result data output from device, para 200, alarm is issued when a sensor detects an abnormality (i.e., abnormality detected based on sensor information));
(b) acquiring a plurality of apparatus data comprising a plurality piece of the sensor information related to the alarm (para 210, diagnosis result data acquired in S12 and determined whether diagnosis result data is normal or abnormal and extent of abnormality, para 246, data acquired in the diagnosis result data acquiring step S12 and determination result and scoring result of data determining step S13,
para 58, data related to alarm monitoring (alarm occurrence information data), maintenance information (event data) generated, and failure information data to abnormality analysis (abnormality analysis data));
(c) comparing each of the apparatus data with a threshold value (para 109, compares measured value with reference data and determines whether abnormal or normal based on a deviation from reference data, para 127, 234, monitoring result data used to determine abnormal with threshold value comparison in S53, determined abnormal state (threshold exceeding), para 118, alarm indicating data exceeds threshold value (S56), para 117, compares data with a threshold value set for data (S53)).
Asai’ 613 specifically fails to disclose (d) when none of the apparatus data acquired in (b) exceed the threshold value according to a comparison result in (c) identifying a specific apparatus data among the plurality of apparatus data closest to the threshold value as a factor of generating the alarm.
In analogous art, Asai’191discloses (d) when none of the apparatus data acquired in (b) exceed the threshold value according to a comparison result in (c) (para 145, 147, when the device data value is not within the band range (i.e., none of the apparatus threshold), that is, when the device data value is larger than the upper limit value, the process proceeds to step S104 and determines that the device data value acquired in the step S101 is an abnormal deviation point, para 136, FDC monitoring part 313 compares data (device state monitoring data) generated from apparatus 1 and determines data is abnormal when the device data deviates (i.e., from comparison result)), identifying a specific apparatus data among the plurality of apparatus data closest to the threshold value as a factor of generating the alarm (para 234, When the failure information data identifier (ID), diagnosed that failure has occurred for failure information generation state, para 117, when data exceeds threshold value (Yes in S54), information indicating that data exceeds threshold value and a result (abnormal) of comparison and an alarm indicating that data exceeds threshold value, para 199, an alarm is issued when a sensor detects an abnormality ( (i.e., based on identifying data closest to the threshold value)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify teaching of semiconductor manufacturing apparatus for forming a film on a substrate disclosed by Asai’ 613 to use measurement data accumulated in the accumulation unit is determined to be abnormal, with respect to a combination of data including the measurement data as taught by Asai’191to use FDC monitoring program, an abnormality pattern extraction program, and an abnormality predictive pattern extraction program to detect abnormality pattern [Asai’191, paragraph 190].
Regarding claim 2, Asai’ 613 discloses the method of claim 1, further comprising (e) analyzing a cause of the alarm by acquiring analysis items used for analyzing the cause of the alarm, wherein two or more of the apparatus data related to the analysis items are acquired in (e) (para 210, diagnosis result data acquired in S12 and determined whether diagnosis result data is normal or abnormal and what extent the abnormality is, para 170, data analysis receives device data (failure information data) indicating state of abnormal state, para 94, device state monitoring result data acquired by device state monitoring control part 215).
Regarding claim 3, Asai’ 613 discloses the method of claim 2, wherein an alarm analysis table containing at least the analysis items used for analyzing the cause of the alarm is stored, and the alarm analysis table further contains an alarm ID for specifying a type of the alarm (para 235, failure information data have identifier (ID), it is diagnosed that failure has occurred), the number of items indicating the number of the analysis items used for analyzing the cause of the alarm represented by the alarm ID and analysis item serial numbers for specifying the analysis items (para 168, data analysis control part 215f executes a data analysis program to analyze cause of abnormality), and wherein (e) comprises: (e1) searching the alarm analysis table with the alarm ID; (e2) acquiring the number of the items and the analysis item serial numbers (para 126, monitoring value (number of uses) of the initialization target parts data); and (e3) specifying candidates for an item that causes the alarm (para 137, determines device data is abnormal when device data deviates more than predetermined number of times, para 191, diagnosing cause or site of abnormality).
Regarding claim 4, Asai’ 613 discloses the method of claim 3, wherein (e3) comprises acquiring the apparatus data corresponding to the analysis items specified by the analysis item serial numbers of the number defined by the number of the items (para 137, determines device data is abnormal when device data deviates more than predetermined number of times, para 176, data analysis control part 215f reads failure information monitoring result data a predetermined number of times).
Regarding claim 5, Asai’ 613 discloses the method of claim 3, wherein an alarm cause investigation table, in which a cause analysis process related to the analysis item serial numbers is defined, is further stored, and wherein the cause analysis process based on the analysis item serial numbers corresponding to the alarm ID is acquired in (e) (para 137, determines device data is abnormal when device data deviates more than predetermined number of times, para 191, diagnosing cause or site of abnormality, para 150, counts numbers determined in the step S104 based on comparison result stored in the storage part 215h).
Regarding claim 6, Asai’ 613 discloses the method of claim 5, wherein the cause analysis process related to the analysis item serial numbers in accordance with the number of the items corresponding to the alarm ID is repeatedly acquired in (e) (para 235, failure information data have identifier (ID), diagnosed failure has occurred, para 150, counts numbers determined in step S104 based on comparison result)
Regarding claim 7, Asai’ 613 discloses the method of claim 3, wherein a plurality of FT factor diagrams created in advance for each alarm ID are further stored (para 235, failure information data have identifier (ID), diagnosed that failure has occurred), and wherein (e) further comprises: (e4) acquiring an FT factor diagram corresponding to a specified alarm ID among the plurality of FT factor diagrams (para 133, FIG. 14, device state monitoring control part 215e includes a setting part 311, a band generation part 312, fault detection & classification (FDC) monitoring part 313, a count part 314 and diagnosis part 315); and (e5) based on the FT factor diagram acquired in (e4), creating the alarm analysis table and an alarm cause investigation table in which a cause analysis process related to the analysis item serial numbers is defined (para 167, monitoring control part 215e is configured to be able to use S. FDC which is a data abnormality diagnosis method independently created by the maker of the apparatus 1, as well as U.FDC which is a known data abnormality diagnosis).
Regarding claim 8, Asai’ 613 discloses the method of claim 1, wherein the alarm outputted in (a) contains an alarm ID for specifying a type of the alarm indicating the abnormality and an occurrence time of the alarm when the abnormality is detected (para 235, all types of failure information data have the same identifier (ID), and indicate abnormality for the failure information generation state).
Regarding claim 9, Asai’ 613 discloses the method of claim 1, further comprising: (f) determining a rank of a cause of the alarm in accordance with a difference between each of the apparatus data and the threshold value when at least one of the apparatus data acquired in (b) exceeds the threshold value; and (g) displaying a relationship between each of the apparatus data and the threshold value in chronological order on a display screen (para 118, operation display part 227 an alarm indicating that the parts data of maintenance target parts exceeds threshold value (S56), para 157, processing of the apparatus 1 has been performed in the order of batch Nos. 999, 998, . . . , 3, 2, 1, para 278, bar graph of parameter file 9, when comparison result is abnormal, difference between data can be displayed).
Regarding claim 10, Asai’ 613 discloses the method of claim 1, wherein, when the alarm is generated and two or more of the apparatus data are related to the alarm in (d), an apparatus data exceeding the threshold value is specified among the two or more of the apparatus data as the factor of generating the alarm (para 58, alarm monitoring (alarm occurrence data) information (event data) generated and failure information data related to abnormality analysis (abnormality analysis data), para 202, alarm monitoring monitors situation of generation of failure data of apparatus).
Regarding claim 11, Asai’ 613 discloses the method of claim 1, wherein the apparatus data containing the sensor information related to a pump for an exhaust is acquired in (b) as an analysis item used for analyzing a cause of the alarm when the alarm is caused by a flawed exhaust control (para 168, analyze cause of abnormality from sensor data, para 191, classifying types of abnormality, diagnosing cause or site of the abnormality, para 244, based on the diagnosis result data of Exhaust, pump and FAN) acquired in the diagnosis data acquiring step S12, and indicating occurrence of abnormality data diagnosed as abnormal data.).
Regarding claim 12, Asai’ 613 discloses a processing apparatus comprising: a process chamber in which a substrate is processed; and a controller configured to be capable of controlling the steps of claim 1 (para37, A process chamber 29 is formed in the processing furnace 28, para 52, process chamber 29 is in contact with surface of substrate 18 and a predetermined process is performed on the surface of substrate 18).
Regarding claim 13, Asai’ 613 discloses the processing apparatus of claim 12, wherein at least one of the apparatus data is related to an exhaust apparatus configured to exhaust an atmosphere of the process chamber, and comprises one or more data selected from the group consisting of a pump current, a pump rotation speed and a pump back pressure (para064, exhaust mechanism 212B constituted by a pressure sensor, an APC valve as a pressure valve, and a vacuum pump is connected to the pressure controller 212b. Based on a pressure value detected by the pressure sensor, the pressure controller 212b is configured to control the opening degree of the APC valve and the operation of the vacuum pump so that the process chamber 29 has a desired pressure).
Asai’191 teaches APC valve as a pressure valve, and a vacuum pump is connected to the pressure controller 212b [64].
Regarding claim 14, Asai’ 613 discloses the processing apparatus of claim 13, wherein the controller is further configured to be capable of issuing the alarm when an average value of at least one data selected from the group consisting of the pump current, the pump rotation speed and the pump back pressure deviates from a pre-set threshold value for a predetermined number of times in a row (para064, exhaust mechanism 212B constituted by a pressure sensor, an APC valve as a pressure valve, and a vacuum pump is connected to the pressure controller 212b. Based on a pressure value detected by pressure sensor, para 80, substrate 18 begin to be rotated by rotation mechanism in accordance with an instruction from the transfer control part 211).
Asai’191 teaches transfer mechanism 15 is capable of holding the pod 9, raising/lowering in a vertical direction and advancing/retracting in a horizontal direction [033].
Regarding claim 15, Asai’ 613 discloses the processing apparatus of claim 13, wherein the process chamber and the exhaust apparatus are configured to be capable of being arranged on a same floor (para 37, process chamber 29 is formed in processing furnace 28, process chamber 29 is a furnace opening opened/closed by a furnace opening shutter 31, para 51, process chamber 29 that process chamber 29 has a desired pressure (degree of vacuum)).
Asai’191 teaches apparatus 1 is installed in a clean room, [059].
Regarding claim 16, Asai’ 613 discloses a display method comprising: the steps (a) to (d) of claim 1; (e) determining a rank of a cause of the alarm in accordance with a difference between each of the apparatus data and a predetermined threshold value when at least one of the apparatus data acquired in (b) exceeds the threshold value; and (f) displaying the alarm, the rank, and a relationship between each of the apparatus data and the threshold value in chronological order on a display screen (para 118, operation display part 227 an alarm indicating that the parts data of maintenance target parts exceeds threshold value (S56), para 157, processing of apparatus 1 has been performed in the order of batch Nos. 999, 998, . . . , 3, 2, 1, para 278, graph of parameter 9, when comparison result is abnormal, difference between data displayed).
Regarding claim 17, Asai’ 613 discloses a processing apparatus comprising: a process chamber in which a substrate is processed (para 191-192, FDC monitoring characteristic values output from apparatus 1 and identifying and classifying types of abnormality, diagnosing cause of abnormality, FDC monitoring characteristic values and classifying types of abnormality, that is, diagnosing cause or site of the abnormality, para 52, process chamber 29 is in contact with surface of substrate 18 and predetermined process is performed on the surface of substrate 18); and a controller configured to be capable of:
(a) outputting an alarm indicating an abnormality detected based on sensor information (para 255, monitoring data output to derive information indicating operation state, para 310, indicating operation state of apparatus 1 by using device state monitoring result data output from device, para 200, alarm is issued when a sensor detects an abnormality (i.e., abnormality detected based on sensor information));
(b) acquiring a plurality of apparatus data comprising a plurality piece of the sensor information related to the alarm (para 210, diagnosis result data acquired in S12 and determined whether diagnosis result data is normal or abnormal and extent of abnormality, para 246, data acquired in the diagnosis result data acquiring step S12 and determination result and scoring result of data determining step S13, para 58, data related to alarm monitoring (alarm occurrence information data), maintenance information (event data) generated, and failure information data to abnormality analysis (abnormality analysis data));
(c) comparing each of the apparatus data with a threshold value (para 109, compares measured value with reference data and determines whether abnormal or normal based on a deviation from reference data, para 127, 234, monitoring result data used to determine abnormal with threshold value comparison in S53, determined abnormal state (threshold exceeding), para 118, alarm indicating data exceeds threshold value (S56), para 117, compares data with a threshold value set for data (S53)).
Asai’ 613 specifically fails to disclose (d) when none of the apparatus data acquired in (b) exceed the threshold value according to a comparison result in (c), identifying a specific apparatus data among the plurality of apparatus data closest to the threshold value as a factor of generating the alarm.
In analogous art, Asai’191discloses (d) when none of the apparatus data acquired in (b) exceed the threshold value according to a comparison result in (c) (para 145, 147, when the device data value is not within the band range (i.e., none of the apparatus threshold), that is, when the device data value is larger than the upper limit value, the process proceeds to step S104 and determines that the device data value acquired in the step S101 is an abnormal deviation point (i.e., from comparison result), para 136, FDC monitoring part 313 compares data (device state monitoring data) generated from apparatus 1 and determines data is abnormal when the device data deviates (i.e., from comparison result)), identifying a specific apparatus data among the plurality of apparatus data closest to the threshold value as a factor of generating the alarm (para 234, When the failure information data identifier (ID), diagnosed that failure has occurred for failure information generation state, para 117, when data exceeds threshold value (Yes in S54), information indicating that data exceeds threshold value and a result (abnormal) of comparison and an alarm indicating that data exceeds threshold value, para 199, an alarm is issued when a sensor detects an abnormality ( (i.e., based on identifying data closest to the threshold value)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify teaching of semiconductor manufacturing apparatus for forming a film on a substrate disclosed by Asai’ 613 to use measurement data accumulated in the accumulation unit is determined to be abnormal, with respect to a combination of data including the measurement data as taught by Asai’191to use FDC monitoring program, an abnormality pattern extraction program, and an abnormality predictive pattern extraction program to detect abnormality pattern [Asai’191, paragraph 190].
Regarding claim 18, Asai’ 613 discloses a method of manufacturing a semiconductor device (para 191-192, FDC monitoring characteristic values output from apparatus 1 and identifying and classifying types of abnormality, diagnosing cause of abnormality, FDC monitoring characteristic values and classifying types of abnormality, that is, diagnosing cause or site of the abnormality), comprising: (a) processing a substrate (para 75, substrate processing process of manufacturing a semiconductor device is performed);
(b) outputting an alarm indicating an abnormality detected based on sensor information (para 255, monitoring data output to derive information indicating operation state, para 310, indicating operation state of apparatus 1 by using device state monitoring result data output from device, para 200, alarm is issued when a sensor detects an abnormality (i.e., abnormality detected based on sensor information));
(c) acquiring a plurality of apparatus data comprising a plurality piece of the sensor information related to the alarm (para 210, diagnosis result data acquired in S12 and determined whether diagnosis result data is normal or abnormal and extent of abnormality, para 246, data acquired in the diagnosis result data acquiring step S12 and determination result and scoring result of data determining step S13,
para 58, data related to alarm monitoring (alarm occurrence information data), maintenance information (event data) generated, and failure information data to abnormality analysis (abnormality analysis data));
(d) comparing each of the apparatus data with a threshold value (para 109, compares measured value with reference data and determines whether abnormal or normal based on a deviation from reference data, para 127, 234, monitoring result data used to determine abnormal with threshold value comparison in S53, determined abnormal state (threshold exceeding), para 118, alarm indicating data exceeds threshold value (S56), para 117, compares data with a threshold value set for data (S53)).
Asai’ 613 specifically fails to disclose (e) when none of the apparatus data acquired in (c) exceed the threshold value according to a comparison result in (d), identifying a specific apparatus data among the plurality of apparatus data closest to the threshold value as a factor of generating the alarm.
In analogous art, Asai’191discloses (e) when none of the apparatus data acquired in (c) exceed the threshold value according to a comparison result in (d) (para 145, 147, when the device data value is not within the band range (i.e., none of the apparatus), that is, when the device data value is larger than the upper limit value, the process proceeds to step S104 and determines that the device data value acquired in the step S101 is an abnormal deviation point, para 136, FDC monitoring part 313 compares data (device state monitoring data) generated from apparatus 1 and determines data is abnormal when the device data deviates), identifying a specific apparatus data among the plurality of apparatus data closest to the threshold value as a factor of generating the alarm (para 234, When the failure information data identifier (ID), diagnosed that failure has occurred for failure information generation state, para 117, when data exceeds threshold value (Yes in S54), information indicating that data exceeds threshold value and a result (abnormal) of comparison and an alarm indicating that data exceeds threshold value, para 199, an alarm is issued when a sensor detects an abnormality ( (i.e., based on identifying data closest to the threshold value)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify teaching of semiconductor manufacturing apparatus for forming a film on a substrate disclosed by Asai’ 613 to use measurement data accumulated in the accumulation unit is determined to be abnormal, with respect to a combination of data including the measurement data as taught by Asai’191to use FDC monitoring program, an abnormality pattern extraction program, and an abnormality predictive pattern extraction program to detect abnormality pattern [Asai’191, paragraph 190].
Regarding claim 19, Asai’ 613 discloses a non-transitory computer-readable recording medium storing a program that causes, by a computer, a processing apparatus to perform (a) to (e) of claim 18 (para 66, program for executing above-described processes in a general-purpose computer from a recording medium (in which the program is stored, claim 19, A non-transitory computer -readable recording medium storing a program that causes controller to perform a process).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Mirza Alam whose telephone number is (469) 295-9286. The examiner can be reached on Monday-Thursday 7:30AM-6:00PM (EST).
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Steven Lim can be reached on 571-270-1210. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MIRZA F ALAM/Primary Examiner, Art Unit 2688