DETAILED ACTION
Claims 1-18 (filed 07/09/2024) have been considered in this action. Claims 1-2 and 7-14 have been filed in the same format as originally filed. Claims 3-6 and 15 have been amended. Claims 16-18 are newly filed.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 4 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 4 recites the limitation "the process" multiple times in its only limitation. There is insufficient antecedent basis for this limitation in the claim. It is unclear which “process” is being referred to, as there is all of a first process, second process, third process, each process, etc. that are previously recited. For the sake of compact prosecution, the examiner shall consider any process to refer to “the process”.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 7 and 10 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, because the specification, while being enabling for a system which includes a computer/processor and memory, does not reasonably provide enablement for the actions performed by the invention without the use of said computer/processor and memory. The specification does not enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make or use the invention commensurate in scope with these claims.
Claims 7 and 10 are directed towards “A production management system that manages a production line”. The production management system is then claimed to recite functions such as “obtaining…”, “when it is determined…”, “anomaly determination processing…”, “outputting determination result information…” without reciting any structure that is actually capable of performing these functions. For example, at least paragraphs [0015], [0090], [0191] describe the functions performed by the production management system as having a processor and memory with instructions for performing such functionality. It would be recognized by PHOSITA that such functionality is not capable of being performed without a processor or circuitry of some kind to obtain the data, make determinations, and output a result. Accordingly, because claims 7 and 10 fail to recite any structure capable of performing these functions, it is considered that the claims are not enabling in their present form, and are rejected under 35 U.S.C. 112(a).
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-6, 8-9 and 11-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception in the form of an abstract idea without significantly more. The claims are directed to the statutory category of invention of a method.
Step 2A Prong One:
Claim(s) 1, 8, 11, and 13 are method claims directed to the abstract idea of (A) processes that are capable of being performed in the human mind with the aid of pen and paper. The acts of predicting a dummy operation timepoint and dummy wait time to form dummy status data and performing anomaly determination processing from claim 1 are directed to abstract ideas that are capable of being performed mentally. For example, the scope of the predicting steps is so broad that it encompasses every solution that uses first operating data and third operating data, as the solution is broadly recited as being “based” on this data without any defining features of how the prediction is made. Such a broad scope of claim includes performance in the mind because the imputation of time data between two points is an easy and simple process to be performed in the human mind with the aid of pen and paper (find the middle timepoint between these two time points, copy the timepoint from one of two timepoints, etc.). Likewise, the act of performing anomaly determination is so broadly claimed that it attempts to consume every solution possible, as it recites what data the anomaly determination is “based” on without any explicit recitation of a manner for achieving such function. Accordingly, the breadth of the claims recites these limitations so broadly that they must be considered as possible to be performed in the human mind. The scope of claiming what something is “based” on fails to afford much in terms of limiting what the solution is.
Step 2A Prong Two:
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception when considered individually and in combination because the additional elements, which are recited at a high level of generality, provide conventional functions that do not add meaningful limits to practicing the abstract idea.
Claims 1, 8, 11 and 13, recites, in part, the additional elements of a production line including two or more devices and processes performed at these devices, the obtaining of status data for these devices, and the outputting of the determined anomaly result information. The limitation describing the production system and how many devices/processes are incorporated is a form of extra solution activity and amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application”, (see MPEP 2106.05(h): “..vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group”). The limitations relating to the obtaining of data in the form of status data, and the outputting of the result of a mental processing step in the anomality determination result is mere instructions to apply an exception without significantly more, which amounts to little more than a recitation of the words “apply it”. The identified limitations only recite the idea of a solution or outcome without claiming details of how a solution is accomplished (see MPEP 2106.05(f): “…The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015)”.
The abstract idea described in claims 1, 8, 11 and 13 is not meaningfully different than those abstract ideas found by the courts, therefor the claim is considered to be directed to an abstract idea.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea. The claim recites the additional elements of a production line including two or more devices and processes performed at these devices, the obtaining of status data for these devices, and the outputting of the determined anomaly result information. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves another technology. Their collective functions merely provide conventional computer implementations and functions. As noted above, the solution as claimed is so broad that it attempts to cover every solution, including those performed mentally as the claims only recited what the solution is “based” on without every claiming the actual solution.
Dependent claims 2-6, 9, 12 and 14 are drawn to additional elements that fail to overcome the presumption of the claims being considered abstract ideas. These limitations are considered to be drawn to the abstract idea without adding significantly more because they recite elements that amount to insignificant extra-solution activity. For example, claims 2, 9, 12 and 14 relate to the outputting of additional information, including that a dummy variable was used, but this fails to afford any practical application to the claims.
Claims 7 and 10 are related to claims 1 and 8 respectively in that they recite similar subject matter, albeit in a different statutory category of invention of a machine. Accordingly, a similar analysis as applied to claims 1 and 8 can be applied to claims 7 and 10 under 35 U.S.C. 101.
Claims 15-18 are related to claims 1, 8, 11 and 13 in that they recite similar subject matter, albeit in a different statutory category of invention of a product of manufacture. Accordingly, a similar analysis as applied to claims 1, 8, 11 and 13 can be applied to claims 15-18 under 35 U.S.C. 101.
Claims 1-18 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Kleinikkink et al. (US 20200089205, hereinafter Kleinikkink) in view of Tsuda et al. (English translation of JP 2011233154, hereinafter Tsuda).
In regards to Claim 1, Kleinikkink teaches “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” ([0042] a system and method for managing automation stations and equipment. In one embodiment, the system and method may monitor and collect data associated with various stations and/or equipment and accumulate data over a period of time to determine statistical anomalies regarding the stations, equipment or the automation process. [0045] An automation line or production line 100 generally includes at least one automation station, or automation element, 105 (which in the current example includes four automation stations 105). As noted above, the automation stations 105 may be or include, for example, machines, sensors, devices, or equipment, or a combination of machines, devices, or equipment, or the like. Each automation station 105 may include an automation controller 110, such as a programmable logic controller (PLC) 110, which controls the automation station 105. [0046] As noted, each automation station 105 will, at least periodically, interact with at least one product being operated on within the production line, for example, processed, assembled, or the like) “from among (i) first operating status data including a first operation timepoint of the first production device and a first wait time of the first production device between the first process and the second process, (ii) second operating status data including a second operation timepoint of the second production device, a second wait time of the second production device between the first process and the second process, and a third wait time of the second production device between the second process and the third process, and (iii) third operating status data including a third operation timepoint of the third production device and a fourth wait time of the third production device between the second process and the third process, obtaining at least (i) the first operating status data and (iii) the third operating status data via a network” ([0045] Examples of operation data may include, but is not limited to, machine identification, timestamp, full machine state, environmental conditions, or any other data that could be provided in relation to a machine or automation station 105 in the production line. [0049] The system 200 may also determine automation conditions from the operation data provided by the production monitoring server 120, which may include, for example, machine stoppages, faulty part detection, out of specification operations or parts, a machine not responding or taking an action within or after a set time period; [0058] The levels of granularity could include, for example, each nest, moving element, automation station, group of stations, and the overall automation system. In some cases, the levels of granularity may also include waiting times between any of the nest, moving element, automation station, group of stations, and the overall automation system; wherein when the system has four stations it collects data from the four stations) “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” ([0042] the system and method may monitor and collect data associated with various stations and/or equipment and accumulate data over a period of time to determine statistical anomalies regarding the stations, equipment or the automation process. In some cases, the system may predict when a station or piece of equipment will require maintenance or other adjustment in order to promote functionality within a desired range. In some cases, the system and method are intended to determine any grouping of equipment or elements that function at a better or worse level than a predetermined threshold. [0058] The system 300 is intended to provide extensive granularity of data but also provide for amalgamated data to allow an end-user to get a quick overall idea of the status. The levels of granularity could include, for example, each nest, moving element, automation station, group of stations, and the overall automation system. In some cases, the levels of granularity may also include waiting times between any of the nest, moving element, automation station, group of stations, and the overall automation system.[0072] the system monitors interactions between automation equipment and components. In particular, the system collects data as to which moving elements, carriers, nest or transported parts/articles are interacting with which equipment piece or device in an automation station at each actuation. The interactions may be tracked throughout the automation or manufacturing station to determine the complete set of interactions each component has with each piece of automation equipment. It is intended that this information will be useful in determining any interactions that lead to abnormalities, even when the equipment and components are otherwise performing properly) “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” ([0074] The system monitors the interactions to determine these anomalies, at 610. If no anomaly is detected, the system will continue to monitor. If anomalies are detected, the system, at 615, will use the statistical analysis and/or machine learning techniques identified herein to determine which interaction is the specific interaction that is causing the issue. [0075] Once the specific interaction is determined, at 620, the system may notify the end user of the interaction. In some cases, the system may display a report illustrating the issue, in other cases, the user may receive an email or text to a predetermined address, or other form of communication notifying the end user of the issue).
Kleinikkink fails to teach “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 the second operation timepoint based on the first operation timepoint and the third operation timepoint, predicting a dummy wait time in the second process based on the first wait time and the fourth wait time, and generating dummy operating status data that corresponds to the second operating status data and includes the dummy operation timepoint and the dummy wait time”.
Tsuda teaches “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 the second operation timepoint based on the first operation timepoint and the third operation timepoint, predicting a dummy wait time in the second process based on the first wait time and the fourth wait time, and generating dummy operating status data that corresponds to the second operating status data and includes the dummy operation timepoint and the dummy wait time” ([page 4] in the conventional first to tenth data collection systems, when all the production apparatuses are not equipped with a data collection apparatus that collects data relating to the operation state of the production apparatus, the operation of the production apparatus is performed. The state could not be estimated. It is frequently occurring that there is a production apparatus in which the data collection apparatus cannot be installed due to the problem of securing the installation location, the problem of cost, and the like; [page 5] The analysis unit associates one or more propagation time information, which is information related to the propagation time of the influence of the stop of the one production device, with the production device in the process before and after the one production device on the production line. Store A propagation time information storage unit that, using the one or more propagation time information, a data collection system comprising a correlation means with a state corresponding to perform between the contacts related information of 2 or more production apparatuses. [page 17] The propagation time calculation unit 13447 calculates the propagation time using the association data received by the association data reception unit 13446, and obtains propagation time information. The propagation time information is information related to the propagation time of the influence of the stop of the one production apparatus 11 to the production apparatus in the process before and after the one production apparatus 11 on the production line. The production apparatuses for the preceding and following processes are one or more production apparatuses for the previous process and one or more production apparatuses for the subsequent process...The state associating unit 13449 associates information regarding the operational states of two or more production apparatuses 11 using one or more propagation time information. The information regarding the operating state is, for example, state information or contact related information; [page 47] The complementary information storage unit 4414411 is information for generating information regarding the operating state of the production apparatus 11 that is not the target of data collection, and has, for example, a complementary production apparatus identifier that identifies the production apparatus 11 that is not the target of data collection. It stores supplementary information that is information. The complementary information usually includes a complementary production device identifier and information for identifying the preceding and subsequent production devices.).
It would have been obvious to a person having ordinary skill in the art before the effective file date of the claimed invention to have modified the system that determines an anomaly using time points and wait times from at least three processing stations for determining an anomaly for a process as a whole by considering all data from all of the stations as taught by Kleinikkink, with the use of creating correlated time information that includes stoppage/wait times from process data for subsequent and/or previous processes in the production line when that data is not obtained as taught by Tsuda, because it would afford the stated benefit of Tsuda, namely that when there is a production apparatus that can’t have a sensor installed due to cost/placement restrictions, it can still have its status determined ([page 4]). Furthermore, both Kleininkink and Tsuda are in the related field of production process anomaly/abnormality determination, thus making the use of a feature from one reference more obvious to combine due to their related subject matter. By combining these elements, it can be considered taking the known use of determining wait/stop time and the timepoints for a production machine using time data from previous and subsequent processes, and using it to improve the system that determines an anomaly for a process as a whole using time data from each of four machines in a production line by replacing the data from the second machine with that determined in a known way that achieves predictable results.
In regards to Claim 15, the combination of Kleinikkink and Tsuda teaches the method as incorporated by claim 1 above. Kleinikkink teaches the use of computers/servers which inherently have memory (Fig. 2 and [0047]). Accordingly, claim 15 is rejected using similar analysis as claim 1 under 35 U.S.C. 103 in view of Kleinikkink and Tsuda.
In regards to Claim 2, the combination of Kleinikkink and Tsuda teaches the anomaly determination method as incorporated by claim 1 above. Tsuda further teaches “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 dummy operating status data in the second process” ([page 19] The analysis result output unit 1345 outputs predetermined information that is a result obtained by the analysis unit 1344. Here, the output is a concept including display on a display, printing on a printer, transmission to an external device, storage on a recording medium, and the like. Specifically, the analysis result output unit 1345, for example, outputs a graph of the operating state of the production apparatus 11 … More specifically, the analysis result output unit 1345, for each facility, for example, from the total duration of each state indicated by each state information, the occurrence rate (time ratio) of the state indicated by each state information, For example, output as a pie chart. Further, the analysis result output unit 1345 specifically outputs, for example, a histogram indicating the frequency for each duration of the state indicated by each state information. With such a histogram, the user can know the operating status of the production apparatus 11. Specifically, the analysis result output unit 1345 uses, for example, information regarding the operating state of the production apparatus 11 and state correspondence information, and the influence of the stop state of one production apparatus 11 causes the production apparatus 11 in the previous process and the subsequent process. Information indicating that the information has been propagated to, and information indicating the operation status of one or more production apparatuses 11 is output. Specifically, the analysis result output unit 1345 outputs, for example, the influence degree information acquired by the influence degree calculating unit 13440. The analysis result output unit 1345 may be considered as including or not including an output device such as a display. The analysis result output unit 1345 can be implemented by output device driver software or output device driver software and an output device; [page 44] the other analysis apparatus is configured to determine whether or not the state correlating means determines that the influence of the stop state of one production apparatus has propagated to the production apparatus in the previous process and the subsequent process. A state correspondence rule storage means for storing the state correspondence rule, wherein the state correspondence information generation means includes the state correspondence rule, the occurrence time of the stop state acquired by the state search means, and Using the recovery time and the one or more propagation time information, an analysis device that generates state correspondence information that is information indicating that the influence of the stop state of one production device has propagated to the production device in the previous process and the subsequent process It is; wherein when state information is imputed from correspondence information to the previous and subsequent processes and is displayed, thus showing the state of dummy variables).
In regards to Claim 3, the combination of Kleinikkink and Tsuda teaches the anomaly determination method as incorporated by claim 1 above. Tsuda further teaches “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 wait time during a total operating time for an entirety of the production processes” ([page 16] The operation information statistical processing unit 13444 performs statistical processing on the two or more pieces of state information acquired by the state decoding unit 13441 to obtain statistical data that is data for clarifying the operation state of the one or more production apparatuses 11. For example, the operation information statistical processing unit 13444 calculates, for each production apparatus 11, the total duration of the state indicated by each state information. The operation information statistical processing means 13444 calculates, for example, the duration and frequency of the state indicated by the status information, and determines the duration and frequency of each state of each production apparatus 11 based on the category information described later. Classify into the categories (groups) indicated by.[page 19] More specifically, the analysis result output unit 1345, for each facility, for example, from the total duration of each state indicated by each state information, the occurrence rate (time ratio) of the state indicated by each state information, For example, output as a pie chart. Further, the analysis result output unit 1345 specifically outputs, for example, a histogram indicating the frequency for each duration of the state indicated by each state information. With such a histogram, the user can know the operating status of the production apparatus 11. Specifically, the analysis result output unit 1345 uses, for example, information regarding the operating state of the production apparatus 11 and state correspondence information, and the influence of the stop state of one production apparatus 11 causes the production apparatus 11 in the previous process and the subsequent process. Information indicating that the information has been propagated to, and information indicating the operation status of one or more production apparatuses 11 is output. Specifically, the analysis result output unit 1345 outputs, for example, the influence degree information acquired by the influence degree calculating unit 13440. [page 39] First, the concept of association performed by the state association unit 13449 will be described with reference to FIG. In Figure 34, the self-stopping effect of the production apparatus "E k", before the production process of the device "E k-1" in the "E k-2" to create a situation of "full work for postprocess", in a subsequent step This indicates that the production apparatus “E k + 1 ” “E k + 2 ” creates a situation of “no work in the previous process”. The state association unit 13449 detects the influence of the stop of the one production apparatus on the operation state of the production apparatus in the preceding and succeeding processes, and associates the states of two or more production apparatuses. The self-stop in FIG. 34 means a state where the production apparatus stops due to itself such as “no parts”, “abnormal stop”, “stop”; wherein a pie chart or graph on the basis of stop/wait time for each process inherently is on the basis of total time).
In regards to Claim 4, the combination of Kleinikkink and Tsuda teaches the anomaly determination method as incorporated by claim 1 above. Tsuda further teaches “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 wait time during an operating time of the process” ([page 40] The influence degree information has, for example, a structure as shown in FIG. 36 and includes “production device” that is an identifier for identifying the production device 11 and information on a ratio indicating each operation status. The information of the ratio indicating each operation status includes information of “self-stop” and “stop due to stop of other devices”. “Self-stop” is information indicating a stop rate when the production apparatus 11 identified by “production apparatus” stops due to its own cause. “Self-stop” has three pieces of information (information on the ratio of each stop cause) “abnormal stop”, “stop”, and “no parts” depending on the cause of the stop. “Stop by stop of other apparatus” has information (for example, “E 1 ”, “E 2 ”, etc.) for identifying the other production apparatus 11 that has caused the stop. The attribute value of the attribute “E 1 ” of “stop by stop of other devices” of each production device is information indicating the ratio of the stop due to the production device “E 1 ”. The influence degree calculation means 13440 statistically analyzes the state information for each production device, and calculates the accumulated time of each state. At that time, the influence degree calculating means 13440 stops the production apparatus 11 indicated by “impact facility” in the state correspondence information of the state correspondence information management table of FIG. 35 due to the stop of the production apparatus indicated by “stop equipment”. 36, and the time is added to the attribute value corresponding to “stop facility” of “stop due to stop of other device” in FIG. Then, the influence degree calculating means 13440 generates the attribute value of each production apparatus of “stop by stop of other apparatus” in FIG. 36 using the state correspondence information of the state correspondence information management table.; wherein a self-stop when an abnormal stop is associated cause of stop is that of the process that caused the stop and the time that is added is the wait time as it is associated with the stop/wait).
In regards to Claim 5, the combination of Kleinikkink and Tsuda teaches the anomaly determination method as incorporated by claim 1 above. Kleinikkink further teaches “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” ([0074] The system monitors the interactions to determine these anomalies, at 610. If no anomaly is detected, the system will continue to monitor).
In regards to Claim 6, the combination of Kleinikkink and Tsuda teaches the anomaly determination method as incorporated by claim 1 above. Kleinikkink further teaches “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” ([0043] An automation station may include a single piece of equipment/machine in a production line, such as a press or the like, but may also include a complex system involving robots, conveyors, manipulators, and the like. [0046] As noted, each automation station 105 will, at least periodically, interact with at least one product being operated on within the production line, for example, processed, assembled, or the like. The product may be conveyed to each automation station 105, for example, using a moving element (not shown), which may include a carrier, a nest, or the like. In some cases, the product may be located on a nest associated with the moving element. Further, the automation station 105 may grip, rotate, lift, or otherwise alter the position of a product and/or nest and/or moving element once it arrives at the automation station 105. In some cases, the automation station 105 may only perform an operation on some but not all of the parts/nests associated with the moving element).
In regards to Claim 7, Kleinikkink teaches “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 production management system comprising:” ([0042] a system and method for managing automation stations and equipment. In one embodiment, the system and method may monitor and collect data associated with various stations and/or equipment and accumulate data over a period of time to determine statistical anomalies regarding the stations, equipment or the automation process. [0045] An automation line or production line 100 generally includes at least one automation station, or automation element, 105 (which in the current example includes four automation stations 105). As noted above, the automation stations 105 may be or include, for example, machines, sensors, devices, or equipment, or a combination of machines, devices, or equipment, or the like. Each automation station 105 may include an automation controller 110, such as a programmable logic controller (PLC) 110, which controls the automation station 105. [0046] As noted, each automation station 105 will, at least periodically, interact with at least one product being operated on within the production line, for example, processed, assembled, or the like) “from among (i) first operating status data including a first operation timepoint of the first production device and a first wait time of the first production device between the first process and the second process, (ii) second operating status data including a second operation timepoint of the second production device, a second wait time of the second production device between the first process and the second process, and a third wait time of the second production device between the second process and the third process, and (iii) third operating status data including a third operation timepoint of the third production device and a fourth wait time of the third production device between the second process and the third process, obtaining at least (i) the first operating status data and (iii) the third operating status data via a network” ([0045] Examples of operation data may include, but is not limited to, machine identification, timestamp, full machine state, environmental conditions, or any other data that could be provided in relation to a machine or automation station 105 in the production line. [0049] The system 200 may also determine automation conditions from the operation data provided by the production monitoring server 120, which may include, for example, machine stoppages, faulty part detection, out of specification operations or parts, a machine not responding or taking an action within or after a set time period; [0058] The levels of granularity could include, for example, each nest, moving element, automation station, group of stations, and the overall automation system. In some cases, the levels of granularity may also include waiting times between any of the nest, moving element, automation station, group of stations, and the overall automation system; wherein when the system has four stations it collects data from the four stations) “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” ([0042] the system and method may monitor and collect data associated with various stations and/or equipment and accumulate data over a period of time to determine statistical anomalies regarding the stations, equipment or the automation process. In some cases, the system may predict when a station or piece of equipment will require maintenance or other adjustment in order to promote functionality within a desired range. In some cases, the system and method are intended to determine any grouping of equipment or elements that function at a better or worse level than a predetermined threshold. [0058] The system 300 is intended to provide extensive granularity of data but also provide for amalgamated data to allow an end-user to get a quick overall idea of the status. The levels of granularity could include, for example, each nest, moving element, automation station, group of stations, and the overall automation system. In some cases, the levels of granularity may also include waiting times between any of the nest, moving element, automation station, group of stations, and the overall automation system.[0072] the system monitors interactions between automation equipment and components. In particular, the system collects data as to which moving elements, carriers, nest or transported parts/articles are interacting with which equipment piece or device in an automation station at each actuation. The interactions may be tracked throughout the automation or manufacturing station to determine the complete set of interactions each component has with each piece of automation equipment. It is intended that this information will be useful in determining any interactions that lead to abnormalities, even when the equipment and components are otherwise performing properly) “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” ([0074] The system monitors the interactions to determine these anomalies, at 610. If no anomaly is detected, the system will continue to monitor. If anomalies are detected, the system, at 615, will use the statistical analysis and/or machine learning techniques identified herein to determine which interaction is the specific interaction that is causing the issue. [0075] Once the specific interaction is determined, at 620, the system may notify the end user of the interaction. In some cases, the system may display a report illustrating the issue, in other cases, the user may receive an email or text to a predetermined address, or other form of communication notifying the end user of the issue).
Kleinikkink fails to teach “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 the second operation timepoint based on the first operation timepoint and the third operation timepoint, predicting a dummy wait time in the second process based on the first wait time and the fourth wait time, and generating dummy operating status data that corresponds to the second operating status data and includes the dummy operation timepoint and the dummy wait time”.
Tsuda teaches “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 the second operation timepoint based on the first operation timepoint and the third operation timepoint, predicting a dummy wait time in the second process based on the first wait time and the fourth wait time, and generating dummy operating status data that corresponds to the second operating status data and includes the dummy operation timepoint and the dummy wait time” ([page 4] in the conventional first to tenth data collection systems, when all the production apparatuses are not equipped with a data collection apparatus that collects data relating to the operation state of the production apparatus, the operation of the production apparatus is performed. The state could not be estimated. It is frequently occurring that there is a production apparatus in which the data collection apparatus cannot be installed due to the problem of securing the installation location, the problem of cost, and the like; [page 5] The analysis unit associates one or more propagation time information, which is information related to the propagation time of the influence of the stop of the one production device, with the production device in the process before and after the one production device on the production line. Store A propagation time information storage unit that, using the one or more propagation time information, a data collection system comprising a correlation means with a state corresponding to perform between the contacts related information of 2 or more production apparatuses. [page 17] The propagation time calculation unit 13447 calculates the propagation time using the association data received by the association data reception unit 13446, and obtains propagation time information. The propagation time information is information related to the propagation time of the influence of the stop of the one production apparatus 11 to the production apparatus in the process before and after the one production apparatus 11 on the production line. The production apparatuses for the preceding and following processes are one or more production apparatuses for the previous process and one or more production apparatuses for the subsequent process...The state associating unit 13449 associates information regarding the operational states of two or more production apparatuses 11 using one or more propagation time information. The information regarding the operating state is, for example, state information or contact related information; [page 47] The complementary information storage unit 4414411 is information for generating information regarding the operating state of the production apparatus 11 that is not the target of data collection, and has, for example, a complementary production apparatus identifier that identifies the production apparatus 11 that is not the target of data collection. It stores supplementary information that is information. The complementary information usually includes a complementary production device identifier and information for identifying the preceding and subsequent production devices).
It would have been obvious to a person having ordinary skill in the art before the effective file date of the claimed invention to have modified the system that determines an anomaly using time points and wait times from at least three processing stations for determining an anomaly for a process as a whole by considering all data from all of the stations as taught by Kleinikkink, with the use of creating correlated time information that includes stoppage/wait times from process data for subsequent and/or previous processes in the production line when that data is not obtained as taught by Tsuda, because it would afford the stated benefit of Tsuda, namely that when there is a production apparatus that can’t have a sensor installed due to cost/placement restrictions, it can still have its status determined ([page 4]). Furthermore, both Kleininkink and Tsuda are in the related field of production process anomaly/abnormality determination, thus making the use of a feature from one reference more obvious to combine due to their related subject matter. By combining these elements, it can be considered taking the known use of determining wait/stop time and the timepoints for a production machine using time data from previous and subsequent processes, and using it to improve the system that determines an anomaly for a process as a whole using time data from each of four machines in a production line by replacing the data from the second machine with that determined in a known way that achieves predictable results.
In regards to Claim 8, Kleinikkink teaches “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” ([0042] a system and method for managing automation stations and equipment. In one embodiment, the system and method may monitor and collect data associated with various stations and/or equipment and accumulate data over a period of time to determine statistical anomalies regarding the stations, equipment or the automation process. [0045] An automation line or production line 100 generally includes at least one automation station, or automation element, 105 (which in the current example includes four automation stations 105). As noted above, the automation stations 105 may be or include, for example, machines, sensors, devices, or equipment, or a combination of machines, devices, or equipment, or the like. Each automation station 105 may include an automation controller 110, such as a programmable logic controller (PLC) 110, which controls the automation station 105. [0046] As noted, each automation station 105 will, at least periodically, interact with at least one product being operated on within the production line, for example, processed, assembled, or the like) “obtaining, via a network, (i) first operating status data including a first operation timepoint of the first production device and a first wait time of the first production device between the first process and the second process and (ii) third operating status data including a third operation timepoint of the third production device and a fourth wait time of the third production device between the second process and the third process;” ([0045] Each PLC 110 is generally in communication with one or more servers or controllers, which may include a production controller 115 and may also or alternatively include a production monitoring server 120. The production controller 115 may provide direct control to and configuration of the PLCs 110 and monitor the overall production line 100. The production monitoring server 120 may monitor and process various operation data received from each PLC 110… Examples of operation data may include, but is not limited to, machine identification, timestamp, full machine state, environmental conditions, or any other data that could be provided in relation to a machine or automation station 105 in the production line. [0049] The system 200 may also determine automation conditions from the operation data provided by the production monitoring server 120, which may include, for example, machine stoppages, faulty part detection, out of specification operations or parts, a machine not responding or taking an action within or after a set time period; [0058] The levels of granularity could include, for example, each nest, moving element, automation station, group of stations, and the overall automation system. In some cases, the levels of granularity may also include waiting times between any of the nest, moving element, automation station, group of stations, and the overall automation system; wherein when the system has four stations it collects data from the four stations) “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” ([0042] the system and method may monitor and collect data associated with various stations and/or equipment and accumulate data over a period of time to determine statistical anomalies regarding the stations, equipment or the automation process. In some cases, the system may predict when a station or piece of equipment will require maintenance or other adjustment in order to promote functionality within a desired range. In some cases, the system and method are intended to determine any grouping of equipment or elements that function at a better or worse level than a predetermined threshold.[0058] The system 300 is intended to provide extensive granularity of data but also provide for amalgamated data to allow an end-user to get a quick overall idea of the status. The levels of granularity could include, for example, each nest, moving element, automation station, group of stations, and the overall automation system. In some cases, the levels of granularity may also include waiting times between any of the nest, moving element, automation station, group of stations, and the overall automation system.[0072] the system monitors interactions between automation equipment and components. In particular, the system collects data as to which moving elements, carriers, nest or transported parts/articles are interacting with which equipment piece or device in an automation station at each actuation. The interactions may be tracked throughout the automation or manufacturing station to determine the complete set of interactions each component has with each piece of automation equipment. It is intended that this information will be useful in determining any interactions that lead to abnormalities, even when the equipment and components are otherwise performing properly) “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” ([0074] The system monitors the interactions to determine these anomalies, at 610. If no anomaly is detected, the system will continue to monitor. If anomalies are detected, the system, at 615, will use the statistical analysis and/or machine learning techniques identified herein to determine which interaction is the specific interaction that is causing the issue. [0075] Once the specific interaction is determined, at 620, the system may notify the end user of the interaction. In some cases, the system may display a report illustrating the issue, in other cases, the user may receive an email or text to a predetermined address, or other form of communication notifying the end user of the issue).
Kleinikkink fails to teach “calculating a dummy operation timepoint of the second production device based on the first operation timepoint and the third operation timepoint, predicting a dummy wait time in the second process based on the first wait time and the fourth wait time, and generating dummy operating status data that includes the dummy operation timepoint and the dummy wait time”.
Tsuda teaches “calculating a dummy operation timepoint of the second production device based on the first operation timepoint and the third operation timepoint, predicting a dummy wait time in the second process based on the first wait time and the fourth wait time, and generating dummy operating status data that includes the dummy operation timepoint and the dummy wait time” ([page 4] in the conventional first to tenth data collection systems, when all the production apparatuses are not equipped with a data collection apparatus that collects data relating to the operation state of the production apparatus, the operation of the production apparatus is performed. The state could not be estimated. It is frequently occurring that there is a production apparatus in which the data collection apparatus cannot be installed due to the problem of securing the installation location, the problem of cost, and the like; [page 5] The analysis unit associates one or more propagation time information, which is information related to the propagation time of the influence of the stop of the one production device, with the production device in the process before and after the one production device on the production line. Store A propagation time information storage unit that, using the one or more propagation time information, a data collection system comprising a correlation means with a state corresponding to perform between the contacts related information of 2 or more production apparatuses. [page 17] The propagation time calculation unit 13447 calculates the propagation time using the association data received by the association data reception unit 13446, and obtains propagation time information. The propagation time information is information related to the propagation time of the influence of the stop of the one production apparatus 11 to the production apparatus in the process before and after the one production apparatus 11 on the production line. The production apparatuses for the preceding and following processes are one or more production apparatuses for the previous process and one or more production apparatuses for the subsequent process...The state associating unit 13449 associates information regarding the operational states of two or more production apparatuses 11 using one or more propagation time information. The information regarding the operating state is, for example, state information or contact related information; [page 47] The complementary information storage unit 4414411 is information for generating information regarding the operating state of the production apparatus 11 that is not the target of data collection, and has, for example, a complementary production apparatus identifier that identifies the production apparatus 11 that is not the target of data collection. It stores supplementary information that is information. The complementary information usually includes a complementary production device identifier and information for identifying the preceding and subsequent production devices).
It would have been obvious to a person having ordinary skill in the art before the effective file date of the claimed invention to have modified the system that determines an anomaly using time points and wait times from at least three processing stations for determining an anomaly for a process as a whole by considering all data from all of the stations as taught by Kleinikkink, with the use of creating correlated time information that includes stoppage/wait times from process data for subsequent and/or previous processes in the production line when that data is not obtained as taught by Tsuda, because it would afford the stated benefit of Tsuda, namely that when there is a production apparatus that can’t have a sensor installed due to cost/placement restrictions, it can still have its status determined ([page 4]). Furthermore, both Kleininkink and Tsuda are in the related field of production process anomaly/abnormality determination, thus making the use of a feature from one reference more obvious to combine due to their related subject matter. By combining these elements, it can be considered taking the known use of determining wait/stop time and the timepoints for a production machine using time data from previous and subsequent processes, and using it to improve the system that determines an anomaly for a process as a whole using time data from each of four machines in a production line by replacing the data from the second machine with that determined in a known way that achieves predictable results.
In regards to Claim 16, the combination of Kleinikkink and Tsuda teaches the method as incorporated by claim 8 above. Kleinikkink teaches the use of computers/servers which inherently have memory (Fig. 2 and [0047]). Accordingly, claim 16 is rejected using similar analysis as claim 8 under 35 U.S.C. 103 in view of Kleinikkink and Tsuda.
In regards to Claim 9, the combination of Kleinikkink and Tsuda teaches the anomaly determination method as incorporated by claim 8 above. Tsuda further teaches “The anomaly determination method according to claim 8, wherein the outputting of the determination result information includes outputting that the anomaly determination processing was performed using the dummy operating status data in the second process” ([page 19] The analysis result output unit 1345 outputs predetermined information that is a result obtained by the analysis unit 1344. Here, the output is a concept including display on a display, printing on a printer, transmission to an external device, storage on a recording medium, and the like. Specifically, the analysis result output unit 1345, for example, outputs a graph of the operating state of the production apparatus 11 … More specifically, the analysis result output unit 1345, for each facility, for example, from the total duration of each state indicated by each state information, the occurrence rate (time ratio) of the state indicated by each state information, For example, output as a pie chart. Further, the analysis result output unit 1345 specifically outputs, for example, a histogram indicating the frequency for each duration of the state indicated by each state information. With such a histogram, the user can know the operating status of the production apparatus 11. Specifically, the analysis result output unit 1345 uses, for example, information regarding the operating state of the production apparatus 11 and state correspondence information, and the influence of the stop state of one production apparatus 11 causes the production apparatus 11 in the previous process and the subsequent process. Information indicating that the information has been propagated to, and information indicating the operation status of one or more production apparatuses 11 is output. Specifically, the analysis result output unit 1345 outputs, for example, the influence degree information acquired by the influence degree calculating unit 13440. The analysis result output unit 1345 may be considered as including or not including an output device such as a display. The analysis result output unit 1345 can be implemented by output device driver software or output device driver software and an output device; [page 44] the other analysis apparatus is configured to determine whether or not the state correlating means determines that the influence of the stop state of one production apparatus has propagated to the production apparatus in the previous process and the subsequent process. A state correspondence rule storage means for storing the state correspondence rule, wherein the state correspondence information generation means includes the state correspondence rule, the occurrence time of the stop state acquired by the state search means, and Using the recovery time and the one or more propagation time information, an analysis device that generates state correspondence information that is information indicating that the influence of the stop state of one production device has propagated to the production device in the previous process and the subsequent process It is; wherein when state information is imputed from correspondence information to the previous and subsequent processes and is displayed, thus showing the state of dummy variables).
In regards to Claim 10, Kleinikkink teaches “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 production management system comprising:” ([0042] a system and method for managing automation stations and equipment. In one embodiment, the system and method may monitor and collect data associated with various stations and/or equipment and accumulate data over a period of time to determine statistical anomalies regarding the stations, equipment or the automation process. [0045] An automation line or production line 100 generally includes at least one automation station, or automation element, 105 (which in the current example includes four automation stations 105). As noted above, the automation stations 105 may be or include, for example, machines, sensors, devices, or equipment, or a combination of machines, devices, or equipment, or the like. Each automation station 105 may include an automation controller 110, such as a programmable logic controller (PLC) 110, which controls the automation station 105. [0046] As noted, each automation station 105 will, at least periodically, interact with at least one product being operated on within the production line, for example, processed, assembled, or the like) “obtaining, via a network, (i) first operating status data including a first operation timepoint of the first production device and a first wait time of the first production device between the first process and the second process and (ii) third operating status data including a third operation timepoint of the third production device and a fourth wait time of the third production device between the second process and the third process” ([0045] Each PLC 110 is generally in communication with one or more servers or controllers, which may include a production controller 115 and may also or alternatively include a production monitoring server 120. The production controller 115 may provide direct control to and configuration of the PLCs 110 and monitor the overall production line 100. The production monitoring server 120 may monitor and process various operation data received from each PLC 110… Examples of operation data may include, but is not limited to, machine identification, timestamp, full machine state, environmental conditions, or any other data that could be provided in relation to a machine or automation station 105 in the production line. [0049] The system 200 may also determine automation conditions from the operation data provided by the production monitoring server 120, which may include, for example, machine stoppages, faulty part detection, out of specification operations or parts, a machine not responding or taking an action within or after a set time period; [0058] The levels of granularity could include, for example, each nest, moving element, automation station, group of stations, and the overall automation system. In some cases, the levels of granularity may also include waiting times between any of the nest, moving element, automation station, group of stations, and the overall automation system; wherein when the system has four stations it collects data from the four stations) “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” ([0042] the system and method may monitor and collect data associated with various stations and/or equipment and accumulate data over a period of time to determine statistical anomalies regarding the stations, equipment or the automation process. In some cases, the system may predict when a station or piece of equipment will require maintenance or other adjustment in order to promote functionality within a desired range. In some cases, the system and method are intended to determine any grouping of equipment or elements that function at a better or worse level than a predetermined threshold.[0058] The system 300 is intended to provide extensive granularity of data but also provide for amalgamated data to allow an end-user to get a quick overall idea of the status. The levels of granularity could include, for example, each nest, moving element, automation station, group of stations, and the overall automation system. In some cases, the levels of granularity may also include waiting times between any of the nest, moving element, automation station, group of stations, and the overall automation system.[0072] the system monitors interactions between automation equipment and components. In particular, the system collects data as to which moving elements, carriers, nest or transported parts/articles are interacting with which equipment piece or device in an automation station at each actuation. The interactions may be tracked throughout the automation or manufacturing station to determine the complete set of interactions each component has with each piece of automation equipment. It is intended that this information will be useful in determining any interactions that lead to abnormalities, even when the equipment and components are otherwise performing properly) “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” ([0074] The system monitors the interactions to determine these anomalies, at 610. If no anomaly is detected, the system will continue to monitor. If anomalies are detected, the system, at 615, will use the statistical analysis and/or machine learning techniques identified herein to determine which interaction is the specific interaction that is causing the issue. [0075] Once the specific interaction is determined, at 620, the system may notify the end user of the interaction. In some cases, the system may display a report illustrating the issue, in other cases, the user may receive an email or text to a predetermined address, or other form of communication notifying the end user of the issue).
Kleinikkink fails to teach “calculating a dummy operation timepoint of the second production device based on the first operation timepoint and the third operation timepoint, predicting a dummy wait time in the second process based on the first wait time and the fourth wait time, and generating dummy operating status data that includes the dummy operation timepoint and the dummy wait time”.
Tsuda teaches “calculating a dummy operation timepoint of the second production device based on the first operation timepoint and the third operation timepoint, predicting a dummy wait time in the second process based on the first wait time and the fourth wait time, and generating dummy operating status data that includes the dummy operation timepoint and the dummy wait time” ([page 4] in the conventional first to tenth data collection systems, when all the production apparatuses are not equipped with a data collection apparatus that collects data relating to the operation state of the production apparatus, the operation of the production apparatus is performed. The state could not be estimated. It is frequently occurring that there is a production apparatus in which the data collection apparatus cannot be installed due to the problem of securing the installation location, the problem of cost, and the like; [page 5] The analysis unit associates one or more propagation time information, which is information related to the propagation time of the influence of the stop of the one production device, with the production device in the process before and after the one production device on the production line. Store A propagation time information storage unit that, using the one or more propagation time information, a data collection system comprising a correlation means with a state corresponding to perform between the contacts related information of 2 or more production apparatuses. [page 17] The propagation time calculation unit 13447 calculates the propagation time using the association data received by the association data reception unit 13446, and obtains propagation time information. The propagation time information is information related to the propagation time of the influence of the stop of the one production apparatus 11 to the production apparatus in the process before and after the one production apparatus 11 on the production line. The production apparatuses for the preceding and following processes are one or more production apparatuses for the previous process and one or more production apparatuses for the subsequent process...The state associating unit 13449 associates information regarding the operational states of two or more production apparatuses 11 using one or more propagation time information. The information regarding the operating state is, for example, state information or contact related information; [page 47] The complementary information storage unit 4414411 is information for generating information regarding the operating state of the production apparatus 11 that is not the target of data collection, and has, for example, a complementary production apparatus identifier that identifies the production apparatus 11 that is not the target of data collection. It stores supplementary information that is information. The complementary information usually includes a complementary production device identifier and information for identifying the preceding and subsequent production devices).
It would have been obvious to a person having ordinary skill in the art before the effective file date of the claimed invention to have modified the system that determines an anomaly using time points and wait times from at least three processing stations for determining an anomaly for a process as a whole by considering all data from all of the stations as taught by Kleinikkink, with the use of creating correlated time information that includes stoppage/wait times from process data for subsequent and/or previous processes in the production line when that data is not obtained as taught by Tsuda, because it would afford the stated benefit of Tsuda, namely that when there is a production apparatus that can’t have a sensor installed due to cost/placement restrictions, it can still have its status determined ([page 4]). Furthermore, both Kleininkink and Tsuda are in the related field of production process anomaly/abnormality determination, thus making the use of a feature from one reference more obvious to combine due to their related subject matter. By combining these elements, it can be considered taking the known use of determining wait/stop time and the timepoints for a production machine using time data from previous and subsequent processes, and using it to improve the system that determines an anomaly for a process as a whole using time data from each of four machines in a production line by replacing the data from the second machine with that determined in a known way that achieves predictable results.
In regards to Claim 11, Kleinikkink teaches “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:” ([0042] a system and method for managing automation stations and equipment. In one embodiment, the system and method may monitor and collect data associated with various stations and/or equipment and accumulate data over a period of time to determine statistical anomalies regarding the stations, equipment or the automation process. [0045] An automation line or production line 100 generally includes at least one automation station, or automation element, 105 (which in the current example includes four automation stations 105). As noted above, the automation stations 105 may be or include, for example, machines, sensors, devices, or equipment, or a combination of machines, devices, or equipment, or the like. Each automation station 105 may include an automation controller 110, such as a programmable logic controller (PLC) 110, which controls the automation station 105. [0046] As noted, each automation station 105 will, at least periodically, interact with at least one product being operated on within the production line, for example, processed, assembled, or the like; [page 27] (Step S1308) The propagation time calculation unit 13447 determines whether i is the last (a numerical value indicating the last production device in the production line or the number of production devices in the production line).(Step S1309) The propagation time calculation means 13447 calculates Bs .sub.k “propagation time to the previous process of occurrence of stop” of the corresponding production apparatus using the calculation formula read in step S1303.) “from among (i) first operating status data including a first operation timepoint of the first production device and a first wait time of the first production device between the first process and the second process and (ii) second operating status data including a second operation timepoint of the second production device and a second wait time of the second production device between the first process and the second process, obtaining at least the first operating status data via a network;” ([0045] Each PLC 110 is generally in communication with one or more servers or controllers, which may include a production controller 115 and may also or alternatively include a production monitoring server 120. The production controller 115 may provide direct control to and configuration of the PLCs 110 and monitor the overall production line 100. The production monitoring server 120 may monitor and process various operation data received from each PLC 110… Examples of operation data may include, but is not limited to, machine identification, timestamp, full machine state, environmental conditions, or any other data that could be provided in relation to a machine or automation station 105 in the production line. [0049] The system 200 may also determine automation conditions from the operation data provided by the production monitoring server 120, which may include, for example, machine stoppages, faulty part detection, out of specification operations or parts, a machine not responding or taking an action within or after a set time period; [0058] The levels of granularity could include, for example, each nest, moving element, automation station, group of stations, and the overall automation system. In some cases, the levels of granularity may also include waiting times between any of the nest, moving element, automation station, group of stations, and the overall automation system; wherein when the system has four stations it collects data from the four stations) “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” ([0042] the system and method may monitor and collect data associated with various stations and/or equipment and accumulate data over a period of time to determine statistical anomalies regarding the stations, equipment or the automation process. In some cases, the system may predict when a station or piece of equipment will require maintenance or other adjustment in order to promote functionality within a desired range. In some cases, the system and method are intended to determine any grouping of equipment or elements that function at a better or worse level than a predetermined threshold.[0058] The system 300 is intended to provide extensive granularity of data but also provide for amalgamated data to allow an end-user to get a quick overall idea of the status. The levels of granularity could include, for example, each nest, moving element, automation station, group of stations, and the overall automation system. In some cases, the levels of granularity may also include waiting times between any of the nest, moving element, automation station, group of stations, and the overall automation system.[0072] the system monitors interactions between automation equipment and components. In particular, the system collects data as to which moving elements, carriers, nest or transported parts/articles are interacting with which equipment piece or device in an automation station at each actuation. The interactions may be tracked throughout the automation or manufacturing station to determine the complete set of interactions each component has with each piece of automation equipment. It is intended that this information will be useful in determining any interactions that lead to abnormalities, even when the equipment and components are otherwise performing properly) “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” ([0074] The system monitors the interactions to determine these anomalies, at 610. If no anomaly is detected, the system will continue to monitor. If anomalies are detected, the system, at 615, will use the statistical analysis and/or machine learning techniques identified herein to determine which interaction is the specific interaction that is causing the issue. [0075] Once the specific interaction is determined, at 620, the system may notify the end user of the interaction. In some cases, the system may display a report illustrating the issue, in other cases, the user may receive an email or text to a predetermined address, or other form of communication notifying the end user of the issue).
Kleinikkink fails to teach “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 the second operation timepoint based on the first operation timepoint, predicting a dummy wait time in the second process based on the first wait time, and generating dummy operating status data that corresponds to the second operating status data and includes the dummy operation timepoint and the dummy wait time;”.
Tsuda teaches “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 the second operation timepoint based on the first operation timepoint, predicting a dummy wait time in the second process based on the first wait time, and generating dummy operating status data that corresponds to the second operating status data and includes the dummy operation timepoint and the dummy wait time;” ([page 4] in the conventional first to tenth data collection systems, when all the production apparatuses are not equipped with a data collection apparatus that collects data relating to the operation state of the production apparatus, the operation of the production apparatus is performed. The state could not be estimated. It is frequently occurring that there is a production apparatus in which the data collection apparatus cannot be installed due to the problem of securing the installation location, the problem of cost, and the like; [page 5] The analysis unit associates one or more propagation time information, which is information related to the propagation time of the influence of the stop of the one production device, with the production device in the process before and after the one production device on the production line. Store A propagation time information storage unit that, using the one or more propagation time information, a data collection system comprising a correlation means with a state corresponding to perform between the contacts related information of 2 or more production apparatuses. [page 17] The propagation time calculation unit 13447 calculates the propagation time using the association data received by the association data reception unit 13446, and obtains propagation time information. The propagation time information is information related to the propagation time of the influence of the stop of the one production apparatus 11 to the production apparatus in the process before and after the one production apparatus 11 on the production line. The production apparatuses for the preceding and following processes are one or more production apparatuses for the previous process and one or more production apparatuses for the subsequent process...The state associating unit 13449 associates information regarding the operational states of two or more production apparatuses 11 using one or more propagation time information. The information regarding the operating state is, for example, state information or contact related information; [page 47] The complementary information storage unit 4414411 is information for generating information regarding the operating state of the production apparatus 11 that is not the target of data collection, and has, for example, a complementary production apparatus identifier that identifies the production apparatus 11 that is not the target of data collection. It stores supplementary information that is information. The complementary information usually includes a complementary production device identifier and information for identifying the preceding and subsequent production devices).
It would have been obvious to a person having ordinary skill in the art before the effective file date of the claimed invention to have modified the system that determines an anomaly using time points and wait times from at least three processing stations for determining an anomaly for a process as a whole by considering all data from all of the stations as taught by Kleinikkink, with the use of creating correlated time information that includes stoppage/wait times from process data for subsequent and/or previous processes in the production line when that data is not obtained as taught by Tsuda, because it would afford the stated benefit of Tsuda, namely that when there is a production apparatus that can’t have a sensor installed due to cost/placement restrictions, it can still have its status determined ([page 4]). Furthermore, both Kleininkink and Tsuda are in the related field of production process anomaly/abnormality determination, thus making the use of a feature from one reference more obvious to combine due to their related subject matter. By combining these elements, it can be considered taking the known use of determining wait/stop time and the timepoints for a production machine using time data from previous and subsequent processes, and using it to improve the system that determines an anomaly for a process as a whole using time data from each of four machines in a production line by replacing the data from the second machine with that determined in a known way that achieves predictable results.
In regards to Claim 17, the combination of Kleinikkink and Tsuda teaches the method as incorporated by claim 11 above. Kleinikkink teaches the use of computers/servers which inherently have memory (Fig. 2 and [0047]). Accordingly, claim 17 is rejected using similar analysis as claim 11 under 35 U.S.C. 103 in view of Kleinikkink and Tsuda.
In regards to Claim 12, the combination of Kleinikkink and Tsuda teaches the anomaly determination method as incorporated by claim 11 above. Tsuda further teaches “The anomaly determination method according to claim 11, wherein the outputting of the determination result information includes outputting that the anomaly determination processing was performed using the dummy operating status data in the second process.” ([page 19] The analysis result output unit 1345 outputs predetermined information that is a result obtained by the analysis unit 1344. Here, the output is a concept including display on a display, printing on a printer, transmission to an external device, storage on a recording medium, and the like. Specifically, the analysis result output unit 1345, for example, outputs a graph of the operating state of the production apparatus 11 … More specifically, the analysis result output unit 1345, for each facility, for example, from the total duration of each state indicated by each state information, the occurrence rate (time ratio) of the state indicated by each state information, For example, output as a pie chart. Further, the analysis result output unit 1345 specifically outputs, for example, a histogram indicating the frequency for each duration of the state indicated by each state information. With such a histogram, the user can know the operating status of the production apparatus 11. Specifically, the analysis result output unit 1345 uses, for example, information regarding the operating state of the production apparatus 11 and state correspondence information, and the influence of the stop state of one production apparatus 11 causes the production apparatus 11 in the previous process and the subsequent process. Information indicating that the information has been propagated to, and information indicating the operation status of one or more production apparatuses 11 is output. Specifically, the analysis result output unit 1345 outputs, for example, the influence degree information acquired by the influence degree calculating unit 13440. The analysis result output unit 1345 may be considered as including or not including an output device such as a display. The analysis result output unit 1345 can be implemented by output device driver software or output device driver software and an output device; [page 44] the other analysis apparatus is configured to determine whether or not the state correlating means determines that the influence of the stop state of one production apparatus has propagated to the production apparatus in the previous process and the subsequent process. A state correspondence rule storage means for storing the state correspondence rule, wherein the state correspondence information generation means includes the state correspondence rule, the occurrence time of the stop state acquired by the state search means, and Using the recovery time and the one or more propagation time information, an analysis device that generates state correspondence information that is information indicating that the influence of the stop state of one production device has propagated to the production device in the previous process and the subsequent process It is; wherein when state information is imputed from correspondence information to the previous and subsequent processes and is displayed, thus showing the state of dummy variables).
In regards to Claim 13, Kleinikkink teaches “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:” ([0042] a system and method for managing automation stations and equipment. In one embodiment, the system and method may monitor and collect data associated with various stations and/or equipment and accumulate data over a period of time to determine statistical anomalies regarding the stations, equipment or the automation process. [0045] An automation line or production line 100 generally includes at least one automation station, or automation element, 105 (which in the current example includes four automation stations 105). As noted above, the automation stations 105 may be or include, for example, machines, sensors, devices, or equipment, or a combination of machines, devices, or equipment, or the like. Each automation station 105 may include an automation controller 110, such as a programmable logic controller (PLC) 110, which controls the automation station 105. [0046] As noted, each automation station 105 will, at least periodically, interact with at least one product being operated on within the production line, for example, processed, assembled, or the like; [page 27] (Step S1305) The propagation time calculation unit 13447 determines whether i is “1”. The first production apparatus 11 is the first production apparatus on the production line. If i is “1”, go to step S1306, and if i is not “1”, go to step S1308.) “from among (i) first operating status data including a first operation timepoint of the first production device and a first wait time of the first production device between the first process and the second process and (ii) second operating status data including a second operation timepoint of the second production device and a second wait time of the second production device between the first process and the second process, obtaining at least the second operating status data via a network;” ([0045] Each PLC 110 is generally in communication with one or more servers or controllers, which may include a production controller 115 and may also or alternatively include a production monitoring server 120. The production controller 115 may provide direct control to and configuration of the PLCs 110 and monitor the overall production line 100. The production monitoring server 120 may monitor and process various operation data received from each PLC 110… Examples of operation data may include, but is not limited to, machine identification, timestamp, full machine state, environmental conditions, or any other data that could be provided in relation to a machine or automation station 105 in the production line. [0049] The system 200 may also determine automation conditions from the operation data provided by the production monitoring server 120, which may include, for example, machine stoppages, faulty part detection, out of specification operations or parts, a machine not responding or taking an action within or after a set time period; [0058] The levels of granularity could include, for example, each nest, moving element, automation station, group of stations, and the overall automation system. In some cases, the levels of granularity may also include waiting times between any of the nest, moving element, automation station, group of stations, and the overall automation system; wherein when the system has four stations it collects data from the four stations) “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” ([0042] the system and method may monitor and collect data associated with various stations and/or equipment and accumulate data over a period of time to determine statistical anomalies regarding the stations, equipment or the automation process. In some cases, the system may predict when a station or piece of equipment will require maintenance or other adjustment in order to promote functionality within a desired range. In some cases, the system and method are intended to determine any grouping of equipment or elements that function at a better or worse level than a predetermined threshold.[0058] The system 300 is intended to provide extensive granularity of data but also provide for amalgamated data to allow an end-user to get a quick overall idea of the status. The levels of granularity could include, for example, each nest, moving element, automation station, group of stations, and the overall automation system. In some cases, the levels of granularity may also include waiting times between any of the nest, moving element, automation station, group of stations, and the overall automation system.[0072] the system monitors interactions between automation equipment and components. In particular, the system collects data as to which moving elements, carriers, nest or transported parts/articles are interacting with which equipment piece or device in an automation station at each actuation. The interactions may be tracked throughout the automation or manufacturing station to determine the complete set of interactions each component has with each piece of automation equipment. It is intended that this information will be useful in determining any interactions that lead to abnormalities, even when the equipment and components are otherwise performing properly) “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” ([0074] The system monitors the interactions to determine these anomalies, at 610. If no anomaly is detected, the system will continue to monitor. If anomalies are detected, the system, at 615, will use the statistical analysis and/or machine learning techniques identified herein to determine which interaction is the specific interaction that is causing the issue. [0075] Once the specific interaction is determined, at 620, the system may notify the end user of the interaction. In some cases, the system may display a report illustrating the issue, in other cases, the user may receive an email or text to a predetermined address, or other form of communication notifying the end user of the issue).
Kleinikkink fails to teach “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 the first operation timepoint based on the second operation timepoint, predicting a dummy wait time in the first process based on the second wait time, and generating dummy operating status data that corresponds to the first operating status data and includes the dummy operation timepoint and the dummy wait time;”.
Tsuda teaches “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 the first operation timepoint based on the second operation timepoint, predicting a dummy wait time in the first process based on the second wait time, and generating dummy operating status data that corresponds to the first operating status data and includes the dummy operation timepoint and the dummy wait time;” ([page 4] in the conventional first to tenth data collection systems, when all the production apparatuses are not equipped with a data collection apparatus that collects data relating to the operation state of the production apparatus, the operation of the production apparatus is performed. The state could not be estimated. It is frequently occurring that there is a production apparatus in which the data collection apparatus cannot be installed due to the problem of securing the installation location, the problem of cost, and the like; [page 5] The analysis unit associates one or more propagation time information, which is information related to the propagation time of the influence of the stop of the one production device, with the production device in the process before and after the one production device on the production line. Store A propagation time information storage unit that, using the one or more propagation time information, a data collection system comprising a correlation means with a state corresponding to perform between the contacts related information of 2 or more production apparatuses. [page 17] The propagation time calculation unit 13447 calculates the propagation time using the association data received by the association data reception unit 13446, and obtains propagation time information. The propagation time information is information related to the propagation time of the influence of the stop of the one production apparatus 11 to the production apparatus in the process before and after the one production apparatus 11 on the production line. The production apparatuses for the preceding and following processes are one or more production apparatuses for the previous process and one or more production apparatuses for the subsequent process...The state associating unit 13449 associates information regarding the operational states of two or more production apparatuses 11 using one or more propagation time information. The information regarding the operating state is, for example, state information or contact related information; [page 47] The complementary information storage unit 4414411 is information for generating information regarding the operating state of the production apparatus 11 that is not the target of data collection, and has, for example, a complementary production apparatus identifier that identifies the production apparatus 11 that is not the target of data collection. It stores supplementary information that is information. The complementary information usually includes a complementary production device identifier and information for identifying the preceding and subsequent production devices).
It would have been obvious to a person having ordinary skill in the art before the effective file date of the claimed invention to have modified the system that determines an anomaly using time points and wait times from at least three processing stations for determining an anomaly for a process as a whole by considering all data from all of the stations as taught by Kleinikkink, with the use of creating correlated time information that includes stoppage/wait times from process data for subsequent and/or previous processes in the production line when that data is not obtained as taught by Tsuda, because it would afford the stated benefit of Tsuda, namely that when there is a production apparatus that can’t have a sensor installed due to cost/placement restrictions, it can still have its status determined ([page 4]). Furthermore, both Kleininkink and Tsuda are in the related field of production process anomaly/abnormality determination, thus making the use of a feature from one reference more obvious to combine due to their related subject matter. By combining these elements, it can be considered taking the known use of determining wait/stop time and the timepoints for a production machine using time data from previous and subsequent processes, and using it to improve the system that determines an anomaly for a process as a whole using time data from each of four machines in a production line by replacing the data from the second machine with that determined in a known way that achieves predictable results.
In regards to Claim 18, the combination of Kleinikkink and Tsuda teaches the method as incorporated by claim 13 above. Kleinikkink teaches the use of computers/servers which inherently have memory (Fig. 2 and [0047]). Accordingly, claim 18 is rejected using similar analysis as claim 13 under 35 U.S.C. 103 in view of Kleinikkink and Tsuda.
In regards to Claim 14, the combination of Kleinikkink and Tsuda teaches the anomaly determination method as incorporated by claim 13 above. Tsuda further teaches “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 dummy operating status data in the first process” ([page 19] The analysis result output unit 1345 outputs predetermined information that is a result obtained by the analysis unit 1344. Here, the output is a concept including display on a display, printing on a printer, transmission to an external device, storage on a recording medium, and the like. Specifically, the analysis result output unit 1345, for example, outputs a graph of the operating state of the production apparatus 11 … More specifically, the analysis result output unit 1345, for each facility, for example, from the total duration of each state indicated by each state information, the occurrence rate (time ratio) of the state indicated by each state information, For example, output as a pie chart. Further, the analysis result output unit 1345 specifically outputs, for example, a histogram indicating the frequency for each duration of the state indicated by each state information. With such a histogram, the user can know the operating status of the production apparatus 11. Specifically, the analysis result output unit 1345 uses, for example, information regarding the operating state of the production apparatus 11 and state correspondence information, and the influence of the stop state of one production apparatus 11 causes the production apparatus 11 in the previous process and the subsequent process. Information indicating that the information has been propagated to, and information indicating the operation status of one or more production apparatuses 11 is output. Specifically, the analysis result output unit 1345 outputs, for example, the influence degree information acquired by the influence degree calculating unit 13440. The analysis result output unit 1345 may be considered as including or not including an output device such as a display. The analysis result output unit 1345 can be implemented by output device driver software or output device driver software and an output device; [page 44] the other analysis apparatus is configured to determine whether or not the state correlating means determines that the influence of the stop state of one production apparatus has propagated to the production apparatus in the previous process and the subsequent process. A state correspondence rule storage means for storing the state correspondence rule, wherein the state correspondence information generation means includes the state correspondence rule, the occurrence time of the stop state acquired by the state search means, and Using the recovery time and the one or more propagation time information, an analysis device that generates state correspondence information that is information indicating that the influence of the stop state of one production device has propagated to the production device in the previous process and the subsequent process It is; wherein when state information is imputed from correspondence information to the previous and subsequent processes and is displayed, thus showing the state of dummy variables).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
WO 2022091608 – teaches abnormality determinations of an abnormality score using upstream and downstream control devices
US 20220100188 – teaches the use of dummy data in the place of missing data for the creation of a plan of operation
US 20190378022 – teaches a method of imputing time-series information when missing values are determined
US 20150261215 – teaches the determination of abnormal conditions by building a map that links upstream and downstream processes
US 20080103715 – teaches a method for computing lost time for production processes when a stop occurs
A Deep-Learning-Based Forecasting Ensemble to Predict Missing Data for Remote Sensing Analysis – teaches imputing dummy data for time-series data
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/JONATHAN MICHAEL SKRZYCKI/Examiner, Art Unit 2116
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