Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Application and Claims
This action is in reply to the application filed on 7/10/2024.
This communication is the first action on the merits.
IDS filed on 7/10/2024 is acknowledged and considered by the Examiner.
Claims 1-18 is/are currently pending and have been examined.
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.
Claims 1-18 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 1 (similarly 7) recite, “An anomaly determination method in a production management … that manages a production line including a first production device corresponding to a first process, a second production device corresponding to a second process following the first process, and a third production device corresponding to a third process following the second process, the anomaly determination method comprising:
from among (i) first operating status data including a first production quantity of the first production device and a first operation timepoint corresponding to each production in the first production device, (ii) second operating status data including a second production quantity of the second production device and a second operation timepoint corresponding to each production in the second production device, and (iii) third operating status data including a third production quantity of the third production device and a third operation timepoint corresponding to each production in the third production device, obtaining at least (i) the first operating status data and (iii) the third operating status data via a …;
when it is determined that the second operating status data was not obtained based on the first operation timepoint and the third operation timepoint, predicting a dummy operation timepoint corresponding to a planned production quantity of the second production device based on the first operating status data and the third operating status data, and generating dummy operating status data that corresponds to the second operating status data and includes the planned production quantity and the dummy operation timepoint;
based on the first operating status data, the third operating status data, and the dummy operating status data, performing anomaly determination processing on production processes, including processes from the first process to the third process, as a whole; and
outputting determination result information to display the determination result information on a display … included in the production management …, the determination result information representing a result of the anomaly determination processing”
Claim 8 (similarly 10) recite, “An anomaly determination method in a production management … that manages a production line including a first production device corresponding to a first process, a second production device corresponding to a second process following the first process, and a third production device corresponding to a third process following the second process, the anomaly determination method comprising:
obtaining, via a …, (i) first operating status data including a first production quantity of the first production device and a first operation timepoint corresponding to each production in the first production device and (ii) third operating status data including a third production quantity of the third production device and a third operation timepoint corresponding to each production in the third production device;
predicting a dummy operation timepoint corresponding to a planned production quantity of the second production device based on the first operating status data and the third operating status data, and generating dummy operating status data including the planned production quantity and the dummy operation timepoint;
based on the first operating status data, the third operating status data, and the dummy operating status data, performing anomaly determination processing on production processes, including processes from the first process to the third process, as a whole; and
outputting determination result information to display the determination result information on a display … included in the production management system, the determination result information representing a result of the anomaly determination processing.”
Claim 11 recite, “An anomaly determination method in a production management … that manages a production line including a first production device corresponding to a first process and a second production device corresponding to a second process, the second process being a final process and following the first process, the anomaly determination method comprising:
from among (i) first operating status data including a first production quantity of the first production device and a first operation timepoint corresponding to each production in the first production device and (ii) second operating status data including a second production quantity of the second production device and a second operation timepoint corresponding to each production in the second production device, obtaining at least the first operating status data via a …;
when it is determined that the second operating status data was not obtained based on the first operation timepoint, predicting a dummy operation timepoint corresponding to a planned production quantity of the second production device based on the first operating status data, and generating dummy operating status data that corresponds to the second operating status data and includes the planned production quantity and the dummy operation timepoint;
based on the first operating status data and the dummy operating status data, performing anomaly determination processing on production processes, including the first process and the second process, as a whole; and
outputting determination result information to display the determination result information on a display … included in the production management system, the determination result information representing a result of the anomaly determination processing”
Claim 13 recite, “An anomaly determination method in a production management system that manages a production line including a first production device corresponding to a first process and a second production device corresponding to a second process following the first process, the first process being an initial process, the anomaly determination method comprising:
from among (i) first operating status data including a first production quantity of the first production device and a first operation timepoint corresponding to each production in the first production device and (ii) second operating status data including a second production quantity of the second production device and a second operation timepoint corresponding to each production in the second production device, obtaining at least the second operating status data via a …;
when it is determined that the first operating status data was not obtained based on the second operation timepoint, predicting a dummy operation timepoint corresponding to a planned production quantity of the first production device based on the second operating status data, and generating dummy operating status data that corresponds to the first operating status data and includes the planned production quantity and the dummy operation timepoint;
based on the dummy operating status data and the second operating status data, performing anomaly determination processing on production processes, including the first process and the second process, as a whole; and
outputting determination result information to display the determination result information on a display … included in the production management system, the determination result information representing a result of the anomaly determination processing.”
Analyzing under Step 2A, Prong 1:
The limitations regarding, …manages a production line including a first production device corresponding to a first process, a second production device corresponding to a second process following the first process, and a third production device corresponding to a third process following the second process…from among (i) first operating status data including a first production quantity of the first production device and a first operation timepoint corresponding to each production in the first production device, (ii) second operating status data including a second production quantity of the second production device and a second operation timepoint corresponding to each production in the second production device, and (iii) third operating status data including a third production quantity of the third production device and a third operation timepoint corresponding to each production in the third production device, obtaining at least (i) the first operating status data and (iii) the third operating status data via a …; when it is determined that the second operating status data was not obtained based on the first operation timepoint and the third operation timepoint, predicting a dummy operation timepoint corresponding to a planned production quantity of the second production device based on the first operating status data and the third operating status data, and generating dummy operating status data that corresponds to the second operating status data and includes the planned production quantity and the dummy operation timepoint; based on the first operating status data, the third operating status data, and the dummy operating status data, performing anomaly determination processing on production processes, including processes from the first process to the third process, as a whole; and outputting determination result information to display the determination result information on a display … included in the production management …, the determination result information representing a result of the anomaly determination processing…obtaining, via a …, (i) first operating status data including a first production quantity of the first production device and a first operation timepoint corresponding to each production in the first production device and (ii) third operating status data including a third production quantity of the third production device and a third operation timepoint corresponding to each production in the third production device; predicting a dummy operation timepoint corresponding to a planned production quantity of the second production device based on the first operating status data and the third operating status data, and generating dummy operating status data including the planned production quantity and the dummy operation timepoint; based on the first operating status data, the third operating status data, and the dummy operating status data, performing anomaly determination processing on production processes, including processes from the first process to the third process, as a whole; and outputting determination result information to display the determination result information on a display … included in the production management system, the determination result information representing a result of the anomaly determination processing…from among (i) first operating status data including a first production quantity of the first production device and a first operation timepoint corresponding to each production in the first production device and (ii) second operating status data including a second production quantity of the second production device and a second operation timepoint corresponding to each production in the second production device, obtaining at least the first operating status data via a …; when it is determined that the second operating status data was not obtained based on the first operation timepoint, predicting a dummy operation timepoint corresponding to a planned production quantity of the second production device based on the first operating status data, and generating dummy operating status data that corresponds to the second operating status data and includes the planned production quantity and the dummy operation timepoint; based on the first operating status data and the dummy operating status data, performing anomaly determination processing on production processes, including the first process and the second process, as a whole; and outputting determination result information to display the determination result information on a display … included in the production management system, the determination result information representing a result of the anomaly determination processing…from among (i) first operating status data including a first production quantity of the first production device and a first operation timepoint corresponding to each production in the first production device and (ii) second operating status data including a second production quantity of the second production device and a second operation timepoint corresponding to each production in the second production device, obtaining at least the second operating status data via a …; when it is determined that the first operating status data was not obtained based on the second operation timepoint, predicting a dummy operation timepoint corresponding to a planned production quantity of the first production device based on the second operating status data, and generating dummy operating status data that corresponds to the first operating status data and includes the planned production quantity and the dummy operation timepoint; based on the dummy operating status data and the second operating status data, performing anomaly determination processing on production processes, including the first process and the second process, as a whole; and outputting determination result information to display the determination result information on a display … included in the production management system, the determination result information representing a result of the anomaly determination processing…, under the broadest reasonable interpretation, can include a human using their mind and using pen and paper to, …manages a production line including a first production device corresponding to a first process, a second production device corresponding to a second process following the first process, and a third production device corresponding to a third process following the second process…from among (i) first operating status data including a first production quantity of the first production device and a first operation timepoint corresponding to each production in the first production device, (ii) second operating status data including a second production quantity of the second production device and a second operation timepoint corresponding to each production in the second production device, and (iii) third operating status data including a third production quantity of the third production device and a third operation timepoint corresponding to each production in the third production device, obtaining at least (i) the first operating status data and (iii) the third operating status data via a …; when it is determined that the second operating status data was not obtained based on the first operation timepoint and the third operation timepoint, predicting a dummy operation timepoint corresponding to a planned production quantity of the second production device based on the first operating status data and the third operating status data, and generating dummy operating status data that corresponds to the second operating status data and includes the planned production quantity and the dummy operation timepoint; based on the first operating status data, the third operating status data, and the dummy operating status data, performing anomaly determination processing on production processes, including processes from the first process to the third process, as a whole; and outputting determination result information to display the determination result information on a display … included in the production management …, the determination result information representing a result of the anomaly determination processing…obtaining, via a …, (i) first operating status data including a first production quantity of the first production device and a first operation timepoint corresponding to each production in the first production device and (ii) third operating status data including a third production quantity of the third production device and a third operation timepoint corresponding to each production in the third production device; predicting a dummy operation timepoint corresponding to a planned production quantity of the second production device based on the first operating status data and the third operating status data, and generating dummy operating status data including the planned production quantity and the dummy operation timepoint; based on the first operating status data, the third operating status data, and the dummy operating status data, performing anomaly determination processing on production processes, including processes from the first process to the third process, as a whole; and outputting determination result information to display the determination result information on a display … included in the production management system, the determination result information representing a result of the anomaly determination processing…from among (i) first operating status data including a first production quantity of the first production device and a first operation timepoint corresponding to each production in the first production device and (ii) second operating status data including a second production quantity of the second production device and a second operation timepoint corresponding to each production in the second production device, obtaining at least the first operating status data via a …; when it is determined that the second operating status data was not obtained based on the first operation timepoint, predicting a dummy operation timepoint corresponding to a planned production quantity of the second production device based on the first operating status data, and generating dummy operating status data that corresponds to the second operating status data and includes the planned production quantity and the dummy operation timepoint; based on the first operating status data and the dummy operating status data, performing anomaly determination processing on production processes, including the first process and the second process, as a whole; and outputting determination result information to display the determination result information on a display … included in the production management system, the determination result information representing a result of the anomaly determination processing…from among (i) first operating status data including a first production quantity of the first production device and a first operation timepoint corresponding to each production in the first production device and (ii) second operating status data including a second production quantity of the second production device and a second operation timepoint corresponding to each production in the second production device, obtaining at least the second operating status data via a …; when it is determined that the first operating status data was not obtained based on the second operation timepoint, predicting a dummy operation timepoint corresponding to a planned production quantity of the first production device based on the second operating status data, and generating dummy operating status data that corresponds to the first operating status data and includes the planned production quantity and the dummy operation timepoint; based on the dummy operating status data and the second operating status data, performing anomaly determination processing on production processes, including the first process and the second process, as a whole; and outputting determination result information to display the determination result information on a display … included in the production management system, the determination result information representing a result of the anomaly determination processing…; therefore, the claims are directed to a mental process.
Further, …manages a production line including a first production device corresponding to a first process, a second production device corresponding to a second process following the first process, and a third production device corresponding to a third process following the second process…from among (i) first operating status data including a first production quantity of the first production device and a first operation timepoint corresponding to each production in the first production device, (ii) second operating status data including a second production quantity of the second production device and a second operation timepoint corresponding to each production in the second production device, and (iii) third operating status data including a third production quantity of the third production device and a third operation timepoint corresponding to each production in the third production device, obtaining at least (i) the first operating status data and (iii) the third operating status data via a …; when it is determined that the second operating status data was not obtained based on the first operation timepoint and the third operation timepoint, predicting a dummy operation timepoint corresponding to a planned production quantity of the second production device based on the first operating status data and the third operating status data, and generating dummy operating status data that corresponds to the second operating status data and includes the planned production quantity and the dummy operation timepoint; based on the first operating status data, the third operating status data, and the dummy operating status data, performing anomaly determination processing on production processes, including processes from the first process to the third process, as a whole; and outputting determination result information to display the determination result information on a display … included in the production management …, the determination result information representing a result of the anomaly determination processing…obtaining, via a …, (i) first operating status data including a first production quantity of the first production device and a first operation timepoint corresponding to each production in the first production device and (ii) third operating status data including a third production quantity of the third production device and a third operation timepoint corresponding to each production in the third production device; predicting a dummy operation timepoint corresponding to a planned production quantity of the second production device based on the first operating status data and the third operating status data, and generating dummy operating status data including the planned production quantity and the dummy operation timepoint; based on the first operating status data, the third operating status data, and the dummy operating status data, performing anomaly determination processing on production processes, including processes from the first process to the third process, as a whole; and outputting determination result information to display the determination result information on a display … included in the production management system, the determination result information representing a result of the anomaly determination processing…from among (i) first operating status data including a first production quantity of the first production device and a first operation timepoint corresponding to each production in the first production device and (ii) second operating status data including a second production quantity of the second production device and a second operation timepoint corresponding to each production in the second production device, obtaining at least the first operating status data via a …; when it is determined that the second operating status data was not obtained based on the first operation timepoint, predicting a dummy operation timepoint corresponding to a planned production quantity of the second production device based on the first operating status data, and generating dummy operating status data that corresponds to the second operating status data and includes the planned production quantity and the dummy operation timepoint; based on the first operating status data and the dummy operating status data, performing anomaly determination processing on production processes, including the first process and the second process, as a whole; and outputting determination result information to display the determination result information on a display … included in the production management system, the determination result information representing a result of the anomaly determination processing…from among (i) first operating status data including a first production quantity of the first production device and a first operation timepoint corresponding to each production in the first production device and (ii) second operating status data including a second production quantity of the second production device and a second operation timepoint corresponding to each production in the second production device, obtaining at least the second operating status data via a …; when it is determined that the first operating status data was not obtained based on the second operation timepoint, predicting a dummy operation timepoint corresponding to a planned production quantity of the first production device based on the second operating status data, and generating dummy operating status data that corresponds to the first operating status data and includes the planned production quantity and the dummy operation timepoint; based on the dummy operating status data and the second operating status data, performing anomaly determination processing on production processes, including the first process and the second process, as a whole; and outputting determination result information to display the determination result information on a display … included in the production management system, the determination result information representing a result of the anomaly determination processing…, are instructions for humans to observe production devices to see if production quantity match planned production quantities from production devices, if production numbers do not match, instruct humans to report anomalies, which are commercial interactions and managing interactions between people, therefore the claims, are directed to certain methods of organizing human activities.
Accordingly, the claims are directed to a mental process, certain methods of organizing human activities, and thus, the claims are directed to an abstract idea under the first prong of Step 2A.
Analyzing under Step 2A, Prong 2:
This judicial exception is not integrated into a practical application under the second prong of Step 2A.
In particular, the claims recite the additional elements beyond the recited abstract idea identified under Step 2A, Prong 1, such as:
Claim 1, 7, 8, 10, 11, 13: system, production line including a first production device corresponding to a first process, a second production device corresponding to a second process following the first process, and a third production device corresponding to a third process following the second process, network, display device,
Claim 6: belt conveyor
Claim 15, 16, 17, 18: A non-transitory computer-readable recording medium having recorded thereon a program for causing a computer to execute
, and pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components.
Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer and a manufacturing environment.
Additionally, with respect to, “…obtaining …”, “…display…”, “…outputting…”, these elements do not add a meaningful limitations to integrate the abstract idea into a practical application because they are extra-solution activity, pre and post solution activity - i.e. data gathering – “…obtaining …”, data output – “…display…”, “…outputting…”
Analyzing under Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B.
As noted above, the aforementioned additional elements beyond the recited abstract idea are not sufficient to amount to significantly more than the recited abstract idea because, as an order combination, the additional elements are no more than mere instructions to implement the idea using generic computer components (i.e. apply it).
Additionally, as an order combination, the additional elements append the recited abstract idea to well-understood, routine, and conventional activities in the field as individually evinced by the applicant’s own disclosure, as required by the Berkheimer Memo, in at least:
[0064] FIG. 2 is a block diagram illustrating the functional configuration of production management system 1 according to the present embodiment. As illustrated in FIG. 2, production management system 1 includes anomaly determination device 2 and obtainer 13. Anomaly determination device 2 includes inputter 20, controller 30,outputter 40, and storage 50.
[0074] Controller 30 is a processing unit that performs the main processing of the anomaly determination method. Controller 30 includes, for example, a processor such as a central processing unit (CPU), non-volatile memory in which a program is stored, volatile memory that is a temporary storage area for executing the program, and an input/output port. Controller 30 may be a single computer device or a plurality of computer devices connected via a network. The processing executed by controller 30 may, for example, be performed by cloud computing.
[0173] Other Embodiments Hereinbefore, the anomaly determination method and the production management system according to one or more aspects have been described based on embodiments, but the present disclosure is not limited to these embodiments. Various modifications to the present embodiment that may be conceived by those skilled in the art, as well as embodiments resulting from combinations of elements from different embodiments, are intended to be included within the scope of the present disclosure as long as these do not depart from the essence of the present disclosure.
[0174] For example, in the above embodiment, two cases were described, namely a case where anomaly determination processing is performed using the production quantity of the equipment and a case where anomaly determination processing is performed using the wait time in the equipment, but both cases may be performed, or only one may be performed. In cases in which both are performed, an anomaly can be determined to have occurred when an anomaly has been determined to occurred in at least one of them.
[0175] In cases in which only one is performed, this reduces the amount of information included in the operating status data, and decreases the data volume. For example, if the production quantity of the equipment is used, the operating status data does not need to include the wait time. If the wait time of the equipment is used, the operating status data does not need to include the production quantity.
[0176] The communication method between devices described in the above embodiment is not particularly limited. In cases in which wireless communication is performed between devices, the wireless communication method (communication standard) is, for example, short-range wireless communication such as ZigBee (registered trademark), Bluetooth (registered trademark), or wireless local area network (LAN). Alternatively, the wireless communication method (communication standard) may be communication via a wide area communication network such as the Internet. Wired communication may be performed between devices instead of wireless communication. More specifically, wired communication is communication using power line communication (PLC) or wired LAN.
[0177] In the above embodiment, processing performed by a particular processing unit may be performed by a different processing unit. The
order of the plurality of processes may be changed, or the plurality of processes may be executed in parallel. In the above embodiment, the allocation of elements of the production management system to the devices is merely one example. For example, an element included in one device may be included in another device. The production management system may also be implemented as a single device.
[0178] For example, the processing described in the above embodiment may be realized by centralized processing using a single device (system), or may be realized by distributed processing using a plurality of devices. The processor that executes the program described above may be a single processor or a plurality of processors. Stated differently, the processing may be centralized or distributed.
[0179] In the above embodiment, all or part of the elements such as the controller may be configured using dedicated hardware, or may be implemented by executing a software program suitable for each element. Each element may be implemented by a program execution unit such as a CPU or processor reading and executing a software program recorded on a recording medium such as an HDD or semiconductor memory.
[0180] The elements such as the controller may be configured of one or more electronic circuits. The one or more electronic circuits may each be a general-purpose circuit or a dedicated circuit.
[0181] The one or more electronic circuits may include, for example, a semiconductor device, an integrated circuit (IC), or a large scale integrated (LSI) circuit. The IC or LSI circuit may be integrated on a single chip, or may be integrated on a plurality of chips. Although these circuits are referred to as IC or LSI circuit here, the terminology may change depending on the degree of integration, and these circuits may be called system LSI circuit, a very large scale integrated (VLSI) circuit, or an ultra large scale integrated (ULSI) circuit. A field programmable gate array (FPGA) that is programmed after manufacturing the LSI circuitry can be used for the same purpose.
[0182] General or specific aspects of the present disclosure may be realized as a system, an apparatus or device, a method, an integrated circuit, or a computer program. Alternatively, the computer program may be realized on a non-transitory computer-readable recording medium such as an optical disc, an HDD, or semiconductor memory. Any given combination of a system, an apparatus or device, a method, an integrated circuit, a computer program, and a recording medium may be used to realize the aspects.
[0183] Various changes, substitutions, additions, omissions, etc., can be made to each of the above embodiments within the scope of the claims or their equivalents.
Furthermore, as an ordered combination, these elements amount to generic computer components receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d).
Moreover, the remaining elements of dependent claims do not transform the recited abstract idea into a patent eligible invention because these remaining elements merely recite further abstract limitations that provide nothing more than simply a narrowing of the abstract idea recited in the independent claims.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components to “apply” the recited abstract idea, perform insignificant extra-solution activity, and generally link the abstract idea to a technical environment. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-18 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections – 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claims 1-18 is/are rejected under 35 U.S.C. 103 as being unpatentable by US Patent Publication to US20190155258A1 to Unagami et al., (hereinafter referred to as “Unagami”) in view of US Patent Publication to US20190095556A1 to Satoh et al., (hereinafter referred to as “Satoh”)
As per Claim 1, Unagami teaches: (Original) An anomaly determination method in a production management system that manages a production line including a first production device corresponding to a first process, a second production device corresponding to a second process following the first process, and a third production device corresponding to a third process following the second process, the anomaly determination method comprising: ([0027][0046])
from among (i) first operating status data including a first production quantity of the first production device and a first operation timepoint corresponding to each production in the first production device, (ii) second operating status data including a second production quantity of the second production device and a second operation timepoint corresponding to each production in the second production device, and (iii) third operating status data including a third production quantity of the third production device and a third operation timepoint corresponding to each production in the third production device, obtaining at least (i) the first operating status data and (iii) the third operating status data via a network; (in at least [0027][0130][0046] the second stage is carried out continuously after the first stage. It is not a limiting example that the first stage and the second stage are continuous, and a third stage in which a process different from those in the first stage and the second stage may be provided between the first stage and the second stage. In addition, as illustrated in FIG. 1, the first stage and the second stage are in-lined. [0057] in FIG. 4A, the product item number is “AAAA,” the manufacturing apparatuses to be used are “the manufacturing apparatuses 120 and 220,” the planned production count of the manufacturing apparatus 120 is “1000,” the planned production time of the manufacturing apparatus 120 is “10:00 to 12:00,” the planned production count of the manufacturing apparatus 220 is “995,” and the planned production time of the manufacturing apparatus 220 is “11:30 to 12:30.” The planned production count is, for example, the number of the processing objects that are to be delivered into the corresponding manufacturing apparatus.)
… a planned production quantity of the second production device based on the first operating status data and the third operating status data, and generating … operating status data that corresponds to the second operating status data and includes the planned production quantity and the … operation timepoint; (in at least [0034] The production instruction includes, for example, the time at which the production is to be started and a planned production count. [0035] The duration of the predetermined time interval may be, for example, a duration required to process processing objects of a planned production count (e.g., a production count for one Lot) that is based on the production plan. [0094] First, the irregularity detecting unit 312 determines whether the production log matches the production plan (S171). For example, the irregularity detecting unit 312 determines whether the delivery counts indicated in FIG. 4B(a) and FIG. 4B(b) match the planned production count indicated in FIG. 4A. In a case in which the delivery count of 1000 matches the planned production count of 1000 in the production plan in the manufacturing apparatus 120 as illustrated in FIG. 4A and FIG. 4B(a) (Yes in S171), the processing proceeds to step S172. Meanwhile, in a case in which the delivery count does not match the planned production count in the manufacturing apparatus 120 (No in S171), the irregularity detecting unit 312 determines that irregularity is present in the first production log and the second production log (S173). In step S173, of the first production log and the second production log, the production log having the delivery count that does not match the planned production count is determined to have irregularity. [0095] step S172 when the production log matches the production plan in step S171, but this is not a limiting example. For example, if the difference between the production plan and the production log is no greater than a predetermined value, the processing may proceed to step S172. The predetermined value may be set, for example, on the basis of an actual record or the like of the proportion defective between a manufacturing system upstream from the manufacturing system 100 and the manufacturing system 100. [0096] Step S171 is carried out on each of the first and second production logs. In other words, the processing proceeds to step S173 in a case in which at least one of the first and second production logs fails to match the production plan [0097] In a case in which the determination of Yes is made in step S171, it is determined whether the first and second production logs match (S172). In step S172, for example, it is determined whether the delivery count of the processing objects delivered into the second stage matches the first production count. In other words, in step S172, any irregularity in the first production log and the second production log is determined on the basis of the first production count and the delivery count in the second stage. )
based on the first operating status data, the third operating status data, and the … operating status data, performing anomaly determination processing on production processes, including processes from the first process to the third process, as a whole; and (in at least [0046] the second stage is carried out continuously after the first stage. It is not a limiting example that the first stage and the second stage are continuous, and a third stage in which a process different from those in the first stage and the second stage may be provided between the first stage and the second stage. In addition, as illustrated in FIG. 1, the first stage and the second stage are in-lined. [0093] FIG. 8 is a flowchart illustrating the irregularity detection processing carried out by the irregularity detecting unit 312 according to the present embodiment. Specifically, the flowchart illustrates the processing carried out by the irregularity detecting unit 312 included in the integration server 300. What is characteristic of the present embodiment is that the irregularity detecting unit 312 carries out the processing in step S172. [0105] in FIG. 9, in a case in which the production log matches the production plan (Yes in step S171), it is determined whether the difference between the first and second production logs is greater than a predetermined value (S182). For example, in a case in which the manufacturing apparatus 220 does not include the sensor 221, information that indicates the number of the processing objects in the second production log includes the good product count and the defective product count. For example, in a case in which the difference between the good product count in the first production log and the good product count in the second production log is greater than a predetermined value (Yes in S182), the irregularity detecting unit 312 may determine that irregularity is present in the first production log and the second production log (S173). The predetermined value may be determined as desired or may be determined on the basis of the actual record of the proportion defective in the manufacturing system 200. The predetermined value may be a value that is no smaller than the product of the mean value of the proportion defective in the manufacturing system 200 and the good product count in the first production log, for example. The good product count in the second production log is an example of a second production count. The second production count, however, is not limited to the good product count and may be the sum of the good product count and the defective product count or the number of the processing objects that have been processed in the manufacturing apparatus 220.)
outputting determination result information to display the determination result information on a display device included in the production management system, the determination result information representing a result of the anomaly determination processing. (in at least [0099] the irregularity detecting unit 312 causes the notifying unit 340 to provide a notification indicating the present of irregularity (S174). In other words, in a case in which the detection result of the irregularity detecting unit 312 indicates the presence of irregularity, the notifying unit 340 provides a notification indicating the irregularity. This configuration makes it possible to notify a manufacturing operator of irregularity in at least one of the first and second production logs. In other words, the manufacturing operator can be notified of any computer virus infection or the like of at least one of the production management servers 110 and 210 and of any alteration of the production logs.)
Although implied, Unagami does not expressly disclose the following limitations, which however, are taught by Satoh,
when it is determined that the second operating status data was not obtained based on the first operation timepoint and the third operation timepoint, predicting a dummy operation timepoint corresponding to a … based on the first operating status data and the third operating status data, and generating … operating status data that corresponds to the second operating status data and includes the … and the dummy operation timepoint (in at least [0004] a simulation using a mathematical model is also helpful when a data obtaining period is insufficient with respect to actually obtained observation data, or observation data includes a missing value due to a sensor failure [0131] The simulation processing table 460 stores a parameters value ID 461, a classification destination 462 related to parameters values, given data 463, prediction values 464, observation data 465, and a likelihood 466 between the prediction values 464 and the observation data 465. The prediction values 464 are a calculation result based on a mathematical model. Additionally, the simulation processing table 460 includes a number of iterations 467 of a mathematical model calculation, a iteration determination result 468 for ending iteration processing, and update data (output data) 469. The update data (output data) 469 include prediction values, first parameters values, and second parameters values. [0142] The mathematical model calculation unit 323 predicts values at a next time step (Step S607). The mathematical model calculation unit 323 executes the processing a plurality of number of times depending on a number of ensembles and a number of parallel calculations (unillustrated) and, thereby, executes ensembles based on Eqn. 3. The likelihood calculation unit 324 calculates updated values of model outputs and a likelihood in accordance with the processing indicated in Eqn. 4 to Eqn. 6, based on the prediction values calculated by the mathematical model calculation unit 323 and observation data stored in the observation data storage unit 322 (Step S609). Then, the likelihood calculation unit 324 stores the updated values and the likelihood into the prediction values-and-second-parameters storage unit 325 and the likelihood storage unit 326, respectively (Step S611). [0143] a determination of whether a simulation time reaches a predetermined end time is executed (Step S613). When the simulation time does not reach the end time, the processing returns to the calculation based on the mathematical model (Step S607). When the simulation time reaches the end time, the determination unit 331 in the global data update unit 330 calculates a determination indicator for determining whether or not to update the first parameters values (Step S615), and determines whether or not to update the first parameters values (Step S617). When the first parameters values are updated, a candidate of new first parameters values is calculated, and the calculated candidate is stored into the first parameters storage unit 313 (Step S619). Subsequently, the processing returns to the parameters values obtainment for mathematical model calculation (Step S605). [0144] performing a simulation with high calculation efficiency, without estimating unsuitable or locally optimum parameters, even when a mathematical model and data for the simulation have uncertainty, and a dimension of parameters to be estimated is high. The present example embodiment is particularly effective when observation data have a temporally and spatially uneven distribution due to an insufficient obtaining period of observation data, missing data [0156] a simulation processing history and setting parameters, initial values, and an algorithm at a start of a new simulation, based on the history.)
…dummy operating status data…dummy operation timepoint…(in at least [0004] a simulation using a mathematical model is also helpful when a data obtaining period is insufficient with respect to actually obtained observation data, or observation data includes a missing value due to a sensor failure [0127] FIG. 4C is a diagram illustrating a structure of the prediction values-and-second-parameters storage unit 325, the first parameters storage unit 341, or the prediction values-and-second-parameters storage unit 342, according to the present example embodiment. [0128] The prediction values-and-second-parameters storage unit 325, the first parameters storage unit 341, or the prediction values-and-second-parameters storage unit 342 stores updated prediction values 452, updated first parameters values 453, and updated second parameters values 454 in association with a simulation ID 451. The storage units store a simulation result when a simulation ends. [0130] FIG. 4D is a diagram illustrating a structure of a simulation processing table 460 according to the second example embodiment of the present invention. The simulation processing table 460 is a table used by the simulation device 200 while executing a simulation.)
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified the teachings of Unagami, as taught by Satoh above, with a reasonable expectation of success if arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make this modification to the teachings of Unagami with the motivation of, …local data processing means for iterating update of the prediction values and the second-parameters values to improve a degree of consistency between the prediction values and observation values reflecting uncertainty… performs local data processing in such a way as to improve a consistency degree between prediction values and observation data while updating second parameters values assumed not to be constant at respective grid points, and also executes global data processing in such a way as to improve the consistency degree between the prediction values and the observation data while updating first parameters values assumed to be constant at the respective grid points…a simulation method according to the present example embodiment separates influence due to uncertainty of a mathematical model and data, and is able to estimate parameters, based on ideal parameter dependency. Further, the method is able to separate estimation methods of variables and parameters depending on properties of the variables and the parameters and optimize each, and therefore a calculation amount required for simulation is reduced. Additionally, separating common (global) parameters and local parameters depending on a range of influence by the parameter improves simulation accuracy in a situation in which an amount of time-series observation data is small or a situation in which some observation data are missing, and estimation accuracy of common parameters at a plurality of calculation points.…The present example embodiment is particularly effective when observation data have a temporally and spatially uneven distribution due to an insufficient obtaining period of observation data, missing data…, as recited in Satoh.
As per Claim 2, Unagami teaches: (Original) The anomaly determination method according to claim 1,
wherein the outputting of the determination result information includes outputting that the anomaly determination processing was performed using the … operating status data in the second process. (in at least [0099] the irregularity detecting unit 312 causes the notifying unit 340 to provide a notification indicating the present of irregularity (S174). In other words, in a case in which the detection result of the irregularity detecting unit 312 indicates the presence of irregularity, the notifying unit 340 provides a notification indicating the irregularity. This configuration makes it possible to notify a manufacturing operator of irregularity in at least one of the first and second production logs. In other words, the manufacturing operator can be notified of any computer virus infection or the like of at least one of the production management servers 110 and 210 and of any alteration of the production logs.)
Although implied, Unagami does not expressly disclose the following limitations, which however, are taught by Satoh,
…dummy operating status data…(in at least [0004] a simulation using a mathematical model is also helpful when a data obtaining period is insufficient with respect to actually obtained observation data, or observation data includes a missing value due to a sensor failure [0127] FIG. 4C is a diagram illustrating a structure of the prediction values-and-second-parameters storage unit 325, the first parameters storage unit 341, or the prediction values-and-second-parameters storage unit 342, according to the present example embodiment. [0128] The prediction values-and-second-parameters storage unit 325, the first parameters storage unit 341, or the prediction values-and-second-parameters storage unit 342 stores updated prediction values 452, updated first parameters values 453, and updated second parameters values 454 in association with a simulation ID 451. The storage units store a simulation result when a simulation ends. [0130] FIG. 4D is a diagram illustrating a structure of a simulation processing table 460 according to the second example embodiment of the present invention. The simulation processing table 460 is a table used by the simulation device 200 while executing a simulation. [0143] a determination of whether a simulation time reaches a predetermined end time is executed (Step S613). When the simulation time does not reach the end time, the processing returns to the calculation based on the mathematical model (Step S607). When the simulation time reaches the end time, the determination unit 331 in the global data update unit 330 calculates a determination indicator for determining whether or not to update the first parameters values (Step S615), and determines whether or not to update the first parameters values (Step S617). When the first parameters values are updated, a candidate of new first parameters values is calculated, and the calculated candidate is stored into the first parameters storage unit 313 (Step S619). Subsequently, the processing returns to the parameters values obtainment for mathematical model calculation (Step S605).)
The reason and rationale to combine Unagami and Satoh are the same as recited above.
As per Claim 3, Unagami teaches: (Currently Amended) The anomaly determination method according to claim 1,
wherein the anomaly determination processing determines presence or absence of an anomaly in the production processes as a whole based on a total production quantity during a total operating time for an entirety of the production processes. (in at least [0063] in FIG. 4B(a) and FIG. 4B(b), the production logs each include the product item number, the manufacturing apparatus, the delivery count, the good product count, the defective product count, and the production time. The delivery count corresponds to the number of the processing objects detected by a sensor (e.g., the sensors 121 and 221). The good product count and the defective product count correspond to the number of the good products and the number of the defective products, respectively, counted as the determination unit determines the quality of each processing object on which the predetermined process has been carried out by the manufacturing apparatus. [0084] The production instruction includes, for example, the planned production count and the planned production time. Steps S21 and S22 correspond to step S4 illustrated in FIG. 5. [0098] In a case in which the delivery count in the second stage fails to match the good product count in the first stage (No in S172), there is a possibility that at least one of the first and second production logs has been altered. Thus, the irregularity detecting unit 312 determines that irregularity is present in the first and second production logs (S173). The determination in step S172 is made with the good product count out of the processing objects that have been processed in the first stage regarded as the first production count and with the delivery count of the processing objects delivered into the second stage regarded as the number of the processing objects in the second stage. When it is determined in step S172 that the delivery count in the second stage fails to match the good product count in the first stage, the irregularity detecting unit 312 determines that irregularity is present between the first and second production logs. [0101] In a case in which the delivery count in the second stage matches the good product count in the first stage (Yes in S172), the processing proceeds to step S175, and the irregularity detecting unit 312 determines that no irregularity is present in the first and second production logs (S175). When it is determined in step S172 that the delivery count in the second stage matches the good product count in the first stage, the irregularity detecting unit 312 determines that no irregularity is present in the first and second production logs. [0105] in FIG. 9, in a case in which the production log matches the production plan (Yes in step S171), it is determined whether the difference between the first and second production logs is greater than a predetermined value (S182). For example, in a case in which the manufacturing apparatus 220 does not include the sensor 221, information that indicates the number of the processing objects in the second production log includes the good product count and the defective product count. For example, in a case in which the difference between the good product count in the first production log and the good product count in the second production log is greater than a predetermined value (Yes in S182), the irregularity detecting unit 312 may determine that irregularity is present in the first production log and the second production log (S173). The predetermined value may be determined as desired or may be determined on the basis of the actual record of the proportion defective in the manufacturing system 200. The predetermined value may be a value that is no smaller than the product of the mean value of the proportion defective in the manufacturing system 200 and the good product count in the first production log, for example. The good product count in the second production log is an example of a second production count. The second production count, however, is not limited to the good product count and may be the sum of the good product count and the defective product count or the number of the processing objects that have been processed in the manufacturing apparatus 220.)
As per Claim 4, Unagami teaches: (Currently Amended) The anomaly determination method according to claim 1,
wherein for each process among all the production processes, the anomaly determination processing determines presence or absence of an anomaly in the process based on a production quantity during an operating time of the process. (in at least [0094] First, the irregularity detecting unit 312 determines whether the production log matches the production plan (S171). For example, the irregularity detecting unit 312 determines whether the delivery counts indicated in FIG. 4B(a) and FIG. 4B(b) match the planned production count indicated in FIG. 4A. In a case in which the delivery count of 1000 matches the planned production count of 1000 in the production plan in the manufacturing apparatus 120 as illustrated in FIG. 4A and FIG. 4B(a) (Yes in S171), the processing proceeds to step S172. Meanwhile, in a case in which the delivery count does not match the planned production count in the manufacturing apparatus 120 (No in S171), the irregularity detecting unit 312 determines that irregularity is present in the first production log and the second production log (S173). In step S173, of the first production log and the second production log, the production log having the delivery count that does not match the planned production count is determined to have irregularity. [0095] proceeds to step S172 when the production log matches the production plan in step S171, but this is not a limiting example. For example, if the difference between the production plan and the production log is no greater than a predetermined value, the processing may proceed to step S172. The predetermined value may be set, for example, on the basis of an actual record or the like of the proportion defective between a manufacturing system upstream from the manufacturing system 100 and the manufacturing system 100. [0096] Step S171 is carried out on each of the first and second production logs. In other words, the processing proceeds to step S173 in a case in which at least one of the first and second production logs fails to match the production plan. [0097] In a case in which the determination of Yes is made in step S171, it is determined whether the first and second production logs match (S172). In step S172, for example, it is determined whether the delivery count of the processing objects delivered into the second stage matches the first production count. In other words, in step S172, any irregularity in the first production log and the second production log is determined on the basis of the first production count and the delivery count in the second stage. [0098] In a case in which the delivery count in the second stage fails to match the good product count in the first stage (No in S172), there is a possibility that at least one of the first and second production logs has been altered. Thus, the irregularity detecting unit 312 determines that irregularity is present in the first and second production logs (S173). The determination in step S172 is made with the good product count out of the processing objects that have been processed in the first stage regarded as the first production count and with the delivery count of the processing objects delivered into the second stage regarded as the number of the processing objects in the second stage. When it is determined in step S172 that the delivery count in the second stage fails to match the good product count in the first stage, the irregularity detecting unit 312 determines that irregularity is present between the first and second production logs. [0105] in FIG. 9, in a case in which the production log matches the production plan (Yes in step S171), it is determined whether the difference between the first and second production logs is greater than a predetermined value (S182). For example, in a case in which the manufacturing apparatus 220 does not include the sensor 221, information that indicates the number of the processing objects in the second production log includes the good product count and the defective product count. For example, in a case in which the difference between the good product count in the first production log and the good product count in the second production log is greater than a predetermined value (Yes in S182), the irregularity detecting unit 312 may determine that irregularity is present in the first production log and the second production log (S173). The predetermined value may be determined as desired or may be determined on the basis of the actual record of the proportion defective in the manufacturing system 200. The predetermined value may be a value that is no smaller than the product of the mean value of the proportion defective in the manufacturing system 200 and the good product count in the first production log, for example. The good product count in the second production log is an example of a second production count. The second production count, however, is not limited to the good product count and may be the sum of the good product count and the defective product count or the number of the processing objects that have been processed in the manufacturing apparatus 220.)
As per Claim 5, Unagami teaches: (Currently Amended) The anomaly determination method according to claim 1,
wherein the result of the anomaly determination processing includes that there is no anomaly in the production processes. (in at least [0101] the processing proceeds to step S175, and the irregularity detecting unit 312 determines that no irregularity is present in the first and second production logs (S175). When it is determined in step S172 that the delivery count in the second stage matches the good product count in the first stage, the irregularity detecting unit 312 determines that no irregularity is present in the first and second production logs.)
As per Claim 6, Unagami teaches: (Currently Amended) The anomaly determination method according to claim 1,
wherein the first production device, the second production device, and the third production device are connected by a belt conveyor in the production line. (in at least [0027] in FIG. 1, the irregularity detecting system 10 includes a manufacturing system 100, a manufacturing system 200, and the integration server 300. The manufacturing systems 100 and 200 each carry out a predetermined process on a processing object being conveyed in a manufacturing line 400. In the present embodiment, the manufacturing systems 100 and 200 each carry out the predetermined process on a processing object being conveyed in the manufacturing line 400 in accordance with a production plan created by the integration server 300. The irregularity detecting system 10 may include another manufacturing system aside from the manufacturing systems 100 and 200. In addition, as illustrated in FIG. 1, the manufacturing systems 100 and 200 are in-lined. A processing object corresponds to an intermediate product that has not yet been made into a finished product (product). A finished product is fabricated from a processing object through at least the manufacturing systems 100 and 200. [0046] that the first stage and the second stage are continuous, and a third stage in which a process different from those in the first stage and the second stage may be provided between the first stage and the second stage)
As per Claim 8, Unagami teaches: (Original) An anomaly determination method in a production management system that manages a production line including a first production device corresponding to a first process, a second production device corresponding to a second process following the first process, and a third production device corresponding to a third process following the second process, the anomaly determination method comprising: ([0027][0046])
obtaining, via a network, (i) first operating status data including a first production quantity of the first production device and a first operation timepoint corresponding to each production in the first production device and (ii) third operating status data including a third production quantity of the third production device and a third operation timepoint corresponding to each production in the third production device; (in at least [0027][0130][0046] the second stage is carried out continuously after the first stage. It is not a limiting example that the first stage and the second stage are continuous, and a third stage in which a process different from those in the first stage and the second stage may be provided between the first stage and the second stage. In addition, as illustrated in FIG. 1, the first stage and the second stage are in-lined. [0057] in FIG. 4A, the product item number is “AAAA,” the manufacturing apparatuses to be used are “the manufacturing apparatuses 120 and 220,” the planned production count of the manufacturing apparatus 120 is “1000,” the planned production time of the manufacturing apparatus 120 is “10:00 to 12:00,” the planned production count of the manufacturing apparatus 220 is “995,” and the planned production time of the manufacturing apparatus 220 is “11:30 to 12:30.” The planned production count is, for example, the number of the processing objects that are to be delivered into the corresponding manufacturing apparatus.)
… a planned production quantity of the second production device based on the first operating status data and the third operating status data, and generating … operating status data including the planned production quantity and the … operation timepoint; (in at least [0034] The production instruction includes, for example, the time at which the production is to be started and a planned production count. [0035] The duration of the predetermined time interval may be, for example, a duration required to process processing objects of a planned production count (e.g., a production count for one Lot) that is based on the production plan. [0094] First, the irregularity detecting unit 312 determines whether the production log matches the production plan (S171). For example, the irregularity detecting unit 312 determines whether the delivery counts indicated in FIG. 4B(a) and FIG. 4B(b) match the planned production count indicated in FIG. 4A. In a case in which the delivery count of 1000 matches the planned production count of 1000 in the production plan in the manufacturing apparatus 120 as illustrated in FIG. 4A and FIG. 4B(a) (Yes in S171), the processing proceeds to step S172. Meanwhile, in a case in which the delivery count does not match the planned production count in the manufacturing apparatus 120 (No in S171), the irregularity detecting unit 312 determines that irregularity is present in the first production log and the second production log (S173). In step S173, of the first production log and the second production log, the production log having the delivery count that does not match the planned production count is determined to have irregularity. [0095] step S172 when the production log matches the production plan in step S171, but this is not a limiting example. For example, if the difference between the production plan and the production log is no greater than a predetermined value, the processing may proceed to step S172. The predetermined value may be set, for example, on the basis of an actual record or the like of the proportion defective between a manufacturing system upstream from the manufacturing system 100 and the manufacturing system 100. [0096] Step S171 is carried out on each of the first and second production logs. In other words, the processing proceeds to step S173 in a case in which at least one of the first and second production logs fails to match the production plan [0097] In a case in which the determination of Yes is made in step S171, it is determined whether the first and second production logs match (S172). In step S172, for example, it is determined whether the delivery count of the processing objects delivered into the second stage matches the first production count. In other words, in step S172, any irregularity in the first production log and the second production log is determined on the basis of the first production count and the delivery count in the second stage. )
based on the first operating status data, the third operating status data, and the dummy operating status data, performing anomaly determination processing on production processes, including processes from the first process to the third process, as a whole; and (in at least [0046] the second stage is carried out continuously after the first stage. It is not a limiting example that the first stage and the second stage are continuous, and a third stage in which a process different from those in the first stage and the second stage may be provided between the first stage and the second stage. In addition, as illustrated in FIG. 1, the first stage and the second stage are in-lined. [0093] FIG. 8 is a flowchart illustrating the irregularity detection processing carried out by the irregularity detecting unit 312 according to the present embodiment. Specifically, the flowchart illustrates the processing carried out by the irregularity detecting unit 312 included in the integration server 300. What is characteristic of the present embodiment is that the irregularity detecting unit 312 carries out the processing in step S172. [0105] in FIG. 9, in a case in which the production log matches the production plan (Yes in step S171), it is determined whether the difference between the first and second production logs is greater than a predetermined value (S182). For example, in a case in which the manufacturing apparatus 220 does not include the sensor 221, information that indicates the number of the processing objects in the second production log includes the good product count and the defective product count. For example, in a case in which the difference between the good product count in the first production log and the good product count in the second production log is greater than a predetermined value (Yes in S182), the irregularity detecting unit 312 may determine that irregularity is present in the first production log and the second production log (S173). The predetermined value may be determined as desired or may be determined on the basis of the actual record of the proportion defective in the manufacturing system 200. The predetermined value may be a value that is no smaller than the product of the mean value of the proportion defective in the manufacturing system 200 and the good product count in the first production log, for example. The good product count in the second production log is an example of a second production count. The second production count, however, is not limited to the good product count and may be the sum of the good product count and the defective product count or the number of the processing objects that have been processed in the manufacturing apparatus 220.)
outputting determination result information to display the determination result information on a display device included in the production management system, the determination result information representing a result of the anomaly determination processing. (in at least [0099] the irregularity detecting unit 312 causes the notifying unit 340 to provide a notification indicating the present of irregularity (S174). In other words, in a case in which the detection result of the irregularity detecting unit 312 indicates the presence of irregularity, the notifying unit 340 provides a notification indicating the irregularity. This configuration makes it possible to notify a manufacturing operator of irregularity in at least one of the first and second production logs. In other words, the manufacturing operator can be notified of any computer virus infection or the like of at least one of the production management servers 110 and 210 and of any alteration of the production logs.)
Although implied, Unagami does not expressly disclose the following limitations, which however, are taught by Satoh,
predicting a dummy operation timepoint corresponding to a … based on the first operating status data and the third operating status data, and generating dummy operating status data including … and the dummy operation timepoint (in at least [0004] a simulation using a mathematical model is also helpful when a data obtaining period is insufficient with respect to actually obtained observation data, or observation data includes a missing value due to a sensor failure [0131] The simulation processing table 460 stores a parameters value ID 461, a classification destination 462 related to parameters values, given data 463, prediction values 464, observation data 465, and a likelihood 466 between the prediction values 464 and the observation data 465. The prediction values 464 are a calculation result based on a mathematical model. Additionally, the simulation processing table 460 includes a number of iterations 467 of a mathematical model calculation, a iteration determination result 468 for ending iteration processing, and update data (output data) 469. The update data (output data) 469 include prediction values, first parameters values, and second parameters values. [0142] The mathematical model calculation unit 323 predicts values at a next time step (Step S607). The mathematical model calculation unit 323 executes the processing a plurality of number of times depending on a number of ensembles and a number of parallel calculations (unillustrated) and, thereby, executes ensembles based on Eqn. 3. The likelihood calculation unit 324 calculates updated values of model outputs and a likelihood in accordance with the processing indicated in Eqn. 4 to Eqn. 6, based on the prediction values calculated by the mathematical model calculation unit 323 and observation data stored in the observation data storage unit 322 (Step S609). Then, the likelihood calculation unit 324 stores the updated values and the likelihood into the prediction values-and-second-parameters storage unit 325 and the likelihood storage unit 326, respectively (Step S611). [0143] a determination of whether a simulation time reaches a predetermined end time is executed (Step S613). When the simulation time does not reach the end time, the processing returns to the calculation based on the mathematical model (Step S607). When the simulation time reaches the end time, the determination unit 331 in the global data update unit 330 calculates a determination indicator for determining whether or not to update the first parameters values (Step S615), and determines whether or not to update the first parameters values (Step S617). When the first parameters values are updated, a candidate of new first parameters values is calculated, and the calculated candidate is stored into the first parameters storage unit 313 (Step S619). Subsequently, the processing returns to the parameters values obtainment for mathematical model calculation (Step S605). [0144] performing a simulation with high calculation efficiency, without estimating unsuitable or locally optimum parameters, even when a mathematical model and data for the simulation have uncertainty, and a dimension of parameters to be estimated is high. The present example embodiment is particularly effective when observation data have a temporally and spatially uneven distribution due to an insufficient obtaining period of observation data, missing data [0156] a simulation processing history and setting parameters, initial values, and an algorithm at a start of a new simulation, based on the history.)
…dummy operating status data…dummy operation timepoint…(in at least [0004] a simulation using a mathematical model is also helpful when a data obtaining period is insufficient with respect to actually obtained observation data, or observation data includes a missing value due to a sensor failure [0127] FIG. 4C is a diagram illustrating a structure of the prediction values-and-second-parameters storage unit 325, the first parameters storage unit 341, or the prediction values-and-second-parameters storage unit 342, according to the present example embodiment. [0128] The prediction values-and-second-parameters storage unit 325, the first parameters storage unit 341, or the prediction values-and-second-parameters storage unit 342 stores updated prediction values 452, updated first parameters values 453, and updated second parameters values 454 in association with a simulation ID 451. The storage units store a simulation result when a simulation ends. [0130] FIG. 4D is a diagram illustrating a structure of a simulation processing table 460 according to the second example embodiment of the present invention. The simulation processing table 460 is a table used by the simulation device 200 while executing a simulation.)
The reason and rationale to combine Unagami and Satoh are the same as recited above.
As per Claim 11, Unagami teaches: (Original) An anomaly determination method in a production management system that manages a production line including a first production device corresponding to a first process and a second production device corresponding to a second process, the second process being a final process and following the first process, the anomaly determination method comprising: ([0027][0046])
from among (i) first operating status data including a first production quantity of the first production device and a first operation timepoint corresponding to each production in the first production device and (ii) second operating status data including a second production quantity of the second production device and a second operation timepoint corresponding to each production in the second production device, obtaining at least the first operating status data via a network; (in at least [0027][0130][0046] the second stage is carried out continuously after the first stage. It is not a limiting example that the first stage and the second stage are continuous, and a third stage in which a process different from those in the first stage and the second stage may be provided between the first stage and the second stage. In addition, as illustrated in FIG. 1, the first stage and the second stage are in-lined. [0057] in FIG. 4A, the product item number is “AAAA,” the manufacturing apparatuses to be used are “the manufacturing apparatuses 120 and 220,” the planned production count of the manufacturing apparatus 120 is “1000,” the planned production time of the manufacturing apparatus 120 is “10:00 to 12:00,” the planned production count of the manufacturing apparatus 220 is “995,” and the planned production time of the manufacturing apparatus 220 is “11:30 to 12:30.” The planned production count is, for example, the number of the processing objects that are to be delivered into the corresponding manufacturing apparatus.)
… operation timepoint corresponding to a planned production quantity of the second production device based on the first operating status data, and generating … operating status data that corresponds to the second operating status data and includes the planned production quantity and the … operation timepoint; (in at least [0034] The production instruction includes, for example, the time at which the production is to be started and a planned production count. [0035] The duration of the predetermined time interval may be, for example, a duration required to process processing objects of a planned production count (e.g., a production count for one Lot) that is based on the production plan. [0094] First, the irregularity detecting unit 312 determines whether the production log matches the production plan (S171). For example, the irregularity detecting unit 312 determines whether the delivery counts indicated in FIG. 4B(a) and FIG. 4B(b) match the planned production count indicated in FIG. 4A. In a case in which the delivery count of 1000 matches the planned production count of 1000 in the production plan in the manufacturing apparatus 120 as illustrated in FIG. 4A and FIG. 4B(a) (Yes in S171), the processing proceeds to step S172. Meanwhile, in a case in which the delivery count does not match the planned production count in the manufacturing apparatus 120 (No in S171), the irregularity detecting unit 312 determines that irregularity is present in the first production log and the second production log (S173). In step S173, of the first production log and the second production log, the production log having the delivery count that does not match the planned production count is determined to have irregularity. [0095] step S172 when the production log matches the production plan in step S171, but this is not a limiting example. For example, if the difference between the production plan and the production log is no greater than a predetermined value, the processing may proceed to step S172. The predetermined value may be set, for example, on the basis of an actual record or the like of the proportion defective between a manufacturing system upstream from the manufacturing system 100 and the manufacturing system 100. [0096] Step S171 is carried out on each of the first and second production logs. In other words, the processing proceeds to step S173 in a case in which at least one of the first and second production logs fails to match the production plan [0097] In a case in which the determination of Yes is made in step S171, it is determined whether the first and second production logs match (S172). In step S172, for example, it is determined whether the delivery count of the processing objects delivered into the second stage matches the first production count. In other words, in step S172, any irregularity in the first production log and the second production log is determined on the basis of the first production count and the delivery count in the second stage. )
based on the first operating status data and the … operating status data, performing anomaly determination processing on production processes, including the first process and the second process, as a whole; and (in at least [0046] the second stage is carried out continuously after the first stage. It is not a limiting example that the first stage and the second stage are continuous, and a third stage in which a process different from those in the first stage and the second stage may be provided between the first stage and the second stage. In addition, as illustrated in FIG. 1, the first stage and the second stage are in-lined. [0093] FIG. 8 is a flowchart illustrating the irregularity detection processing carried out by the irregularity detecting unit 312 according to the present embodiment. Specifically, the flowchart illustrates the processing carried out by the irregularity detecting unit 312 included in the integration server 300. What is characteristic of the present embodiment is that the irregularity detecting unit 312 carries out the processing in step S172. [0105] in FIG. 9, in a case in which the production log matches the production plan (Yes in step S171), it is determined whether the difference between the first and second production logs is greater than a predetermined value (S182). For example, in a case in which the manufacturing apparatus 220 does not include the sensor 221, information that indicates the number of the processing objects in the second production log includes the good product count and the defective product count. For example, in a case in which the difference between the good product count in the first production log and the good product count in the second production log is greater than a predetermined value (Yes in S182), the irregularity detecting unit 312 may determine that irregularity is present in the first production log and the second production log (S173). The predetermined value may be determined as desired or may be determined on the basis of the actual record of the proportion defective in the manufacturing system 200. The predetermined value may be a value that is no smaller than the product of the mean value of the proportion defective in the manufacturing system 200 and the good product count in the first production log, for example. The good product count in the second production log is an example of a second production count. The second production count, however, is not limited to the good product count and may be the sum of the good product count and the defective product count or the number of the processing objects that have been processed in the manufacturing apparatus 220.)
outputting determination result information to display the determination result information on a display device included in the production management system, the determination result information representing a result of the anomaly determination processing. (in at least [0099] the irregularity detecting unit 312 causes the notifying unit 340 to provide a notification indicating the present of irregularity (S174). In other words, in a case in which the detection result of the irregularity detecting unit 312 indicates the presence of irregularity, the notifying unit 340 provides a notification indicating the irregularity. This configuration makes it possible to notify a manufacturing operator of irregularity in at least one of the first and second production logs. In other words, the manufacturing operator can be notified of any computer virus infection or the like of at least one of the production management servers 110 and 210 and of any alteration of the production logs.)
Although implied, Unagami does not expressly disclose the following limitations, which however, are taught by Satoh,
when it is determined that the second operating status data was not obtained based on the first operation timepoint, predicting a dummy operation timepoint corresponding to … based on the first operating status data, and generating dummy operating status data that corresponds to the second operating status data and includes the … and the dummy operation timepoint; (in at least [0004] a simulation using a mathematical model is also helpful when a data obtaining period is insufficient with respect to actually obtained observation data, or observation data includes a missing value due to a sensor failure [0131] The simulation processing table 460 stores a parameters value ID 461, a classification destination 462 related to parameters values, given data 463, prediction values 464, observation data 465, and a likelihood 466 between the prediction values 464 and the observation data 465. The prediction values 464 are a calculation result based on a mathematical model. Additionally, the simulation processing table 460 includes a number of iterations 467 of a mathematical model calculation, a iteration determination result 468 for ending iteration processing, and update data (output data) 469. The update data (output data) 469 include prediction values, first parameters values, and second parameters values. [0142] The mathematical model calculation unit 323 predicts values at a next time step (Step S607). The mathematical model calculation unit 323 executes the processing a plurality of number of times depending on a number of ensembles and a number of parallel calculations (unillustrated) and, thereby, executes ensembles based on Eqn. 3. The likelihood calculation unit 324 calculates updated values of model outputs and a likelihood in accordance with the processing indicated in Eqn. 4 to Eqn. 6, based on the prediction values calculated by the mathematical model calculation unit 323 and observation data stored in the observation data storage unit 322 (Step S609). Then, the likelihood calculation unit 324 stores the updated values and the likelihood into the prediction values-and-second-parameters storage unit 325 and the likelihood storage unit 326, respectively (Step S611). [0143] a determination of whether a simulation time reaches a predetermined end time is executed (Step S613). When the simulation time does not reach the end time, the processing returns to the calculation based on the mathematical model (Step S607). When the simulation time reaches the end time, the determination unit 331 in the global data update unit 330 calculates a determination indicator for determining whether or not to update the first parameters values (Step S615), and determines whether or not to update the first parameters values (Step S617). When the first parameters values are updated, a candidate of new first parameters values is calculated, and the calculated candidate is stored into the first parameters storage unit 313 (Step S619). Subsequently, the processing returns to the parameters values obtainment for mathematical model calculation (Step S605). [0144] performing a simulation with high calculation efficiency, without estimating unsuitable or locally optimum parameters, even when a mathematical model and data for the simulation have uncertainty, and a dimension of parameters to be estimated is high. The present example embodiment is particularly effective when observation data have a temporally and spatially uneven distribution due to an insufficient obtaining period of observation data, missing data [0156] a simulation processing history and setting parameters, initial values, and an algorithm at a start of a new simulation, based on the history.)
…dummy operating status data…dummy operation timepoint…(in at least [0004] a simulation using a mathematical model is also helpful when a data obtaining period is insufficient with respect to actually obtained observation data, or observation data includes a missing value due to a sensor failure [0127] FIG. 4C is a diagram illustrating a structure of the prediction values-and-second-parameters storage unit 325, the first parameters storage unit 341, or the prediction values-and-second-parameters storage unit 342, according to the present example embodiment. [0128] The prediction values-and-second-parameters storage unit 325, the first parameters storage unit 341, or the prediction values-and-second-parameters storage unit 342 stores updated prediction values 452, updated first parameters values 453, and updated second parameters values 454 in association with a simulation ID 451. The storage units store a simulation result when a simulation ends. [0130] FIG. 4D is a diagram illustrating a structure of a simulation processing table 460 according to the second example embodiment of the present invention. The simulation processing table 460 is a table used by the simulation device 200 while executing a simulation.)
The reason and rationale to combine Unagami and Satoh are the same as recited above.
As per Claim 13, Unagami teaches: (Original) An anomaly determination method in a production management system that manages a production line including a first production device corresponding to a first process and a second production device corresponding to a second process following the first process, the first process being an initial process, the anomaly determination method comprising: ([0027] [0046])
from among (i) first operating status data including a first production quantity of the first production device and a first operation timepoint corresponding to each production in the first production device and (ii) second operating status data including a second production quantity of the second production device and a second operation timepoint corresponding to each production in the second production device, obtaining at least the second operating status data via a network; (in at least [0027][0130][0046] the second stage is carried out continuously after the first stage. It is not a limiting example that the first stage and the second stage are continuous, and a third stage in which a process different from those in the first stage and the second stage may be provided between the first stage and the second stage. In addition, as illustrated in FIG. 1, the first stage and the second stage are in-lined. [0057] in FIG. 4A, the product item number is “AAAA,” the manufacturing apparatuses to be used are “the manufacturing apparatuses 120 and 220,” the planned production count of the manufacturing apparatus 120 is “1000,” the planned production time of the manufacturing apparatus 120 is “10:00 to 12:00,” the planned production count of the manufacturing apparatus 220 is “995,” and the planned production time of the manufacturing apparatus 220 is “11:30 to 12:30.” The planned production count is, for example, the number of the processing objects that are to be delivered into the corresponding manufacturing apparatus.)
… operation timepoint corresponding to a planned production quantity of the first production device based on the second operating status data, and generating … operating status data that corresponds to the first operating status data and includes the planned production quantity and the … operation timepoint; (in at least [0034] The production instruction includes, for example, the time at which the production is to be started and a planned production count. [0035] The duration of the predetermined time interval may be, for example, a duration required to process processing objects of a planned production count (e.g., a production count for one Lot) that is based on the production plan. [0094] First, the irregularity detecting unit 312 determines whether the production log matches the production plan (S171). For example, the irregularity detecting unit 312 determines whether the delivery counts indicated in FIG. 4B(a) and FIG. 4B(b) match the planned production count indicated in FIG. 4A. In a case in which the delivery count of 1000 matches the planned production count of 1000 in the production plan in the manufacturing apparatus 120 as illustrated in FIG. 4A and FIG. 4B(a) (Yes in S171), the processing proceeds to step S172. Meanwhile, in a case in which the delivery count does not match the planned production count in the manufacturing apparatus 120 (No in S171), the irregularity detecting unit 312 determines that irregularity is present in the first production log and the second production log (S173). In step S173, of the first production log and the second production log, the production log having the delivery count that does not match the planned production count is determined to have irregularity. [0095] step S172 when the production log matches the production plan in step S171, but this is not a limiting example. For example, if the difference between the production plan and the production log is no greater than a predetermined value, the processing may proceed to step S172. The predetermined value may be set, for example, on the basis of an actual record or the like of the proportion defective between a manufacturing system upstream from the manufacturing system 100 and the manufacturing system 100. [0096] Step S171 is carried out on each of the first and second production logs. In other words, the processing proceeds to step S173 in a case in which at least one of the first and second production logs fails to match the production plan [0097] In a case in which the determination of Yes is made in step S171, it is determined whether the first and second production logs match (S172). In step S172, for example, it is determined whether the delivery count of the processing objects delivered into the second stage matches the first production count. In other words, in step S172, any irregularity in the first production log and the second production log is determined on the basis of the first production count and the delivery count in the second stage. )
based on the … operating status data and the second operating status data, performing anomaly determination processing on production processes, including the first process and the second process, as a whole; and (in at least [0046] the second stage is carried out continuously after the first stage. It is not a limiting example that the first stage and the second stage are continuous, and a third stage in which a process different from those in the first stage and the second stage may be provided between the first stage and the second stage. In addition, as illustrated in FIG. 1, the first stage and the second stage are in-lined. [0093] FIG. 8 is a flowchart illustrating the irregularity detection processing carried out by the irregularity detecting unit 312 according to the present embodiment. Specifically, the flowchart illustrates the processing carried out by the irregularity detecting unit 312 included in the integration server 300. What is characteristic of the present embodiment is that the irregularity detecting unit 312 carries out the processing in step S172. [0105] in FIG. 9, in a case in which the production log matches the production plan (Yes in step S171), it is determined whether the difference between the first and second production logs is greater than a predetermined value (S182). For example, in a case in which the manufacturing apparatus 220 does not include the sensor 221, information that indicates the number of the processing objects in the second production log includes the good product count and the defective product count. For example, in a case in which the difference between the good product count in the first production log and the good product count in the second production log is greater than a predetermined value (Yes in S182), the irregularity detecting unit 312 may determine that irregularity is present in the first production log and the second production log (S173). The predetermined value may be determined as desired or may be determined on the basis of the actual record of the proportion defective in the manufacturing system 200. The predetermined value may be a value that is no smaller than the product of the mean value of the proportion defective in the manufacturing system 200 and the good product count in the first production log, for example. The good product count in the second production log is an example of a second production count. The second production count, however, is not limited to the good product count and may be the sum of the good product count and the defective product count or the number of the processing objects that have been processed in the manufacturing apparatus 220.)
outputting determination result information to display the determination result information on a display device included in the production management system, the determination result information representing a result of the anomaly determination processing. (in at least [0099] the irregularity detecting unit 312 causes the notifying unit 340 to provide a notification indicating the present of irregularity (S174). In other words, in a case in which the detection result of the irregularity detecting unit 312 indicates the presence of irregularity, the notifying unit 340 provides a notification indicating the irregularity. This configuration makes it possible to notify a manufacturing operator of irregularity in at least one of the first and second production logs. In other words, the manufacturing operator can be notified of any computer virus infection or the like of at least one of the production management servers 110 and 210 and of any alteration of the production logs.)
Although implied, Unagami does not expressly disclose the following limitations, which however, are taught by Satoh,
when it is determined that the first operating status data was not obtained based on the second operation timepoint, predicting a dummy operation timepoint corresponding to … based on the second operating status data, and generating dummy operating status data that corresponds to the first operating status data and includes the … and the dummy operation timepoint (in at least [0004] a simulation using a mathematical model is also helpful when a data obtaining period is insufficient with respect to actually obtained observation data, or observation data includes a missing value due to a sensor failure [0131] The simulation processing table 460 stores a parameters value ID 461, a classification destination 462 related to parameters values, given data 463, prediction values 464, observation data 465, and a likelihood 466 between the prediction values 464 and the observation data 465. The prediction values 464 are a calculation result based on a mathematical model. Additionally, the simulation processing table 460 includes a number of iterations 467 of a mathematical model calculation, a iteration determination result 468 for ending iteration processing, and update data (output data) 469. The update data (output data) 469 include prediction values, first parameters values, and second parameters values. [0142] The mathematical model calculation unit 323 predicts values at a next time step (Step S607). The mathematical model calculation unit 323 executes the processing a plurality of number of times depending on a number of ensembles and a number of parallel calculations (unillustrated) and, thereby, executes ensembles based on Eqn. 3. The likelihood calculation unit 324 calculates updated values of model outputs and a likelihood in accordance with the processing indicated in Eqn. 4 to Eqn. 6, based on the prediction values calculated by the mathematical model calculation unit 323 and observation data stored in the observation data storage unit 322 (Step S609). Then, the likelihood calculation unit 324 stores the updated values and the likelihood into the prediction values-and-second-parameters storage unit 325 and the likelihood storage unit 326, respectively (Step S611). [0143] a determination of whether a simulation time reaches a predetermined end time is executed (Step S613). When the simulation time does not reach the end time, the processing returns to the calculation based on the mathematical model (Step S607). When the simulation time reaches the end time, the determination unit 331 in the global data update unit 330 calculates a determination indicator for determining whether or not to update the first parameters values (Step S615), and determines whether or not to update the first parameters values (Step S617). When the first parameters values are updated, a candidate of new first parameters values is calculated, and the calculated candidate is stored into the first parameters storage unit 313 (Step S619). Subsequently, the processing returns to the parameters values obtainment for mathematical model calculation (Step S605). [0144] performing a simulation with high calculation efficiency, without estimating unsuitable or locally optimum parameters, even when a mathematical model and data for the simulation have uncertainty, and a dimension of parameters to be estimated is high. The present example embodiment is particularly effective when observation data have a temporally and spatially uneven distribution due to an insufficient obtaining period of observation data, missing data [0156] a simulation processing history and setting parameters, initial values, and an algorithm at a start of a new simulation, based on the history.)
…dummy operating status data…dummy operation timepoint…(in at least [0004] a simulation using a mathematical model is also helpful when a data obtaining period is insufficient with respect to actually obtained observation data, or observation data includes a missing value due to a sensor failure [0127] FIG. 4C is a diagram illustrating a structure of the prediction values-and-second-parameters storage unit 325, the first parameters storage unit 341, or the prediction values-and-second-parameters storage unit 342, according to the present example embodiment. [0128] The prediction values-and-second-parameters storage unit 325, the first parameters storage unit 341, or the prediction values-and-second-parameters storage unit 342 stores updated prediction values 452, updated first parameters values 453, and updated second parameters values 454 in association with a simulation ID 451. The storage units store a simulation result when a simulation ends. [0130] FIG. 4D is a diagram illustrating a structure of a simulation processing table 460 according to the second example embodiment of the present invention. The simulation processing table 460 is a table used by the simulation device 200 while executing a simulation.)
The reason and rationale to combine Unagami and Satoh are the same as recited above.
As per Claim 14, Unagami teaches: (Original) The anomaly determination method according to claim 13,
wherein the outputting of the determination result information includes outputting that the anomaly determination processing was performed using the … operating status data in the first process. (in at least [0099] the irregularity detecting unit 312 causes the notifying unit 340 to provide a notification indicating the present of irregularity (S174). In other words, in a case in which the detection result of the irregularity detecting unit 312 indicates the presence of irregularity, the notifying unit 340 provides a notification indicating the irregularity. This configuration makes it possible to notify a manufacturing operator of irregularity in at least one of the first and second production logs. In other words, the manufacturing operator can be notified of any computer virus infection or the like of at least one of the production management servers 110 and 210 and of any alteration of the production logs.)
Although implied, Unagami does not expressly disclose the following limitations, which however, are taught by Satoh,
…dummy operating status data…(in at least [0004] a simulation using a mathematical model is also helpful when a data obtaining period is insufficient with respect to actually obtained observation data, or observation data includes a missing value due to a sensor failure [0127] FIG. 4C is a diagram illustrating a structure of the prediction values-and-second-parameters storage unit 325, the first parameters storage unit 341, or the prediction values-and-second-parameters storage unit 342, according to the present example embodiment. [0128] The prediction values-and-second-parameters storage unit 325, the first parameters storage unit 341, or the prediction values-and-second-parameters storage unit 342 stores updated prediction values 452, updated first parameters values 453, and updated second parameters values 454 in association with a simulation ID 451. The storage units store a simulation result when a simulation ends. [0130] FIG. 4D is a diagram illustrating a structure of a simulation processing table 460 according to the second example embodiment of the present invention. The simulation processing table 460 is a table used by the simulation device 200 while executing a simulation.)
The reason and rationale to combine Unagami and Satoh are the same as recited above.
As per Claim 15, Unagami teaches: (Currently Amended) A non-transitory computer-readable recording medium having recorded thereon a program for causing a computer to execute the anomaly determination method according to claim 1. (in at least [0020] an information processing apparatus, an information processing method, and a recording medium having a program recorded therein that can detect the presence of irregularity in a production log without stopping the operation of the manufacturing line. [0143][0144])
As per Claim 7 for a system(see at least Unagami [0027]), substantially recite the subject matter of Claim 1 and are rejected based on the same reasoning and rationale.
As per Claim 9, 10 for a system(see at least Unagami [0027]), substantially recite the subject matter of Claim 2, 8 and are rejected based on the same reasoning and rationale.
As per Claim 12 for a method (see at least Unagami [0021]), substantially recite the subject matter of Claim 2 and are rejected based on the same reasoning and rationale.
As per Claim 16, 17, 18 for a method (see at least Unagami [0020]), substantially recite the subject matter of Claim 15 and are rejected based on the same reasoning and rationale.
Conclusion
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/PO HAN LEE/Examiner, Art Unit 3623