Information processing device, input control method and program

Abstract

To provide an approach capable of contributing to improvement in KPI even in a whole time-series range through an input stage and an output stage to / from an AI engine.SOLUTION: According to one preferred aspect of the present invention, an information processing device using an AI engine, includes a calculation unit, and a storage unit. The calculation unit executes an emphasized KPI reception unit that receives emphasized KPI information designated by a user, and a variable selection unit that selects an input variable to be input to the AI engine on the basis of the emphasized KPI information.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a technology for controlling input to an AI engine, and in particular to an input procedure generation device, a method for generating an input procedure for an AI engine based on KPIs, and a repair recommendation system. [Background technology]

[0002] In recent years, with the development of AI (Artificial Intelligence) technology, its practical application is expanding to various fields. For example, in the industrial field, a system (called a "repair recommendation system") is being realized that, when a failure occurs in an asset such as a machine, facility, or vehicle, an AI engine learns repair history information that collects pairs of information on past failures related to the asset and information on repairs performed on the asset, and then recommends an appropriate repair method to a maintenance worker when the failure occurs (this system is called a "repair recommendation system").

[0003] In this trend, what is important for AI engines is not just improving the accuracy of answers, such as accuracy and F-measure, which are common in the field of machine learning, but also how much they can improve various KPIs (Key Performance Indicators) and KGIs (Key Goal Indicators) related to the user's business. For example, in a repair recommendation system, it is important to reduce KPIs such as the time from when an asset malfunctions to when the cause of the asset malfunction is identified and repaired (called the malfunction repair time), as well as the cost of identifying the asset malfunction and repairing it.

[0004] In contrast to this, in Patent Document 1, a failure simulation using machine learning is performed for maintenance methods with different conditions, and the conditions that result in the best KPI are determined in the simulation results. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 2019-133412 Summary of the Invention [Problem to be solved by the invention]

[0006] Here, the use of an AI engine is divided into "input" and "output" as a time series. For example, in the case of an AI engine for a repair recommendation system, the time series range shared between the AI ​​engine and the business is from "investigating the cause of the failure (input)" to "recommending the cause of the failure (output)." This time series range is also the range in which the AI ​​engine can contribute to improving the KPIs of the user's business.

[0007] However, the scope in which conventional technology contributes to improving KPIs is limited to "fault cause recommendation (output)" and does not consider other time series ranges or the entire time series range when proposing KPI improvements, resulting in missed opportunities for KPI improvement using AI engines.

[0008] For example, in a repair recommendation system, if reducing "failure repair time" is a key KPI, only the AI ​​engine's output, i.e., "failure cause recommendation (output)," contributes to improving the KPI; the AI ​​engine's input, i.e., "failure cause investigation (input)," contributes nothing to improving the KPI. Furthermore, not considering the relationship between the AI ​​engine's input and the KPI can undermine the validity of the AI ​​engine's output and ultimately hinder the customer's KPI improvement. For example, suppose an AI engine prioritizes reducing "failure repair time" and outputs a repair method that completes the work one hour faster than usual. However, if the input procedure to the AI ​​engine does not take the KPI into account or if the input takes an hour longer than usual because another KPI is prioritized, the one-hour reduction achieved in the output will be offset by the input, resulting in the customer's KPI being ignored. As in the example above, to evaluate whether an AI engine is truly contributing to improving a customer's KPI, it is necessary to evaluate the entire time series, from input to output. However, conventionally, these KPIs were evaluated only for the time series range of output or input only, and no consideration was given to whether the KPIs aimed at improving input and output were consistent. As a result, there was a problem in that the AI ​​engine may not be able to achieve improvements in the customer's KPIs throughout the entire time series range. Conventional technology did not focus on this issue, and the issue itself is novel.

[0009] The objective of the present invention is to provide a method that can contribute to improving KPIs over the entire time series range, throughout the input and output stages to the AI ​​engine. [Means for solving the problem]

[0010] A preferred aspect of the present invention is an information processing device that uses an AI engine, comprising a calculation unit and a memory unit, wherein the calculation unit executes a key KPI reception unit that receives key KPI information specified by a user, and a variable selection unit that selects input variables to be input to the AI ​​engine based on the key KPI information.

[0011] Another preferred aspect of the present invention is an input control method for performing input from an information processing device to an AI engine represented by a network having causes and items as random variables and defining conditional dependencies between the causes and the items, the input control method executing a key KPI acceptance process that accepts key KPI information specified by a user, and a variable selection process that selects input variables to be input to the AI ​​engine based on the key KPI information. Another preferred aspect of the present invention is a program for providing input from an information processing device to an AI engine represented by a network having causes and items as random variables and defining conditional dependencies between the causes and the items, the program causing the information processing device to execute a key KPI acceptance process that accepts key KPI information specified by a user, and a variable selection process that selects input variables to be input to the AI ​​engine based on the key KPI information. [Effects of the Invention]

[0012] It can also contribute to improving KPIs across the entire time series range through the input and output stages to the AI ​​engine. [Brief explanation of the drawings]

[0013] [Figure 1] An illustration showing the time series of "input" and "output" in an AI engine. [Figure 2] FIG. 1 is a block diagram showing the configuration of a repair recommendation system according to an embodiment of the present invention. [Figure 3] FIG. 2 is a block diagram of a hardware configuration according to the present embodiment. [Figure 4]FIG. 2 is a structural diagram showing AI engine information in a network according to the present embodiment. [Figure 5] FIG. 10 is a table showing fault cause information in AI engine information in this embodiment. [Figure 6] FIG. 10 is a table showing survey item information in AI engine information in this embodiment. [Figure 7] FIG. 10 is a table showing causal relationship information between failure causes and investigation items in AI engine information in this embodiment. [Figure 8A] FIG. 10 is a table showing probability distribution information in AI engine information in this embodiment. [Figure 8B] FIG. 10 is a table showing probability distribution information in AI engine information in this embodiment. [Figure 9] FIG. 10 is a table showing variable condition information for KPIs in the variable selection information according to the present embodiment. [Figure 10] FIG. 4 is a table showing variable information for variable condition information in variable selection information according to the present embodiment. [Figure 11] FIG. 4 is a table showing input procedure generation method selection information in the present embodiment. [Figure 12] FIG. 4 is a table showing recommendation determination information according to the present embodiment. [Figure 13] FIG. 10 is a table showing key KPI information in the present embodiment. [Figure 14] FIG. 4 is a table showing input procedure information according to the embodiment. [Figure 15] FIG. 4 is a table showing survey item response information in the present embodiment. [Figure 16] FIG. 4 is a table showing recommendation result information in the present embodiment. [Figure 17] 3 shows a processing flow of a registration phase in this embodiment. [Figure 18] 3 shows a processing flow of a recommendation phase in this embodiment. [Figure 19] FIG. 10 is an image diagram of an AI engine information registration screen according to the present embodiment. [Figure 20] FIG. 10 is an image diagram of a variable selection information registration screen according to the present embodiment. [Figure 21]FIG. 10 is an image diagram of an input procedure generation method selection information registration screen according to the embodiment. [Figure 22] FIG. 10 is an image diagram of a recommendation determination information registration screen according to the present embodiment. [Figure 23] FIG. 10 is an image diagram of a key KPI reception screen in this embodiment. [Figure 24] FIG. 3 is an image diagram of an input reception screen according to the present embodiment. [Figure 25] FIG. 10 is an image diagram of a recommendation result display screen in this embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0014] Hereinafter, the embodiments will be described in detail with reference to the drawings. However, the present invention should not be interpreted as being limited to the description of the embodiments shown below. Those skilled in the art will easily understand that the specific configuration can be changed within the scope of the idea or purpose of the present invention.

[0015] In the configuration of the invention described below, the same parts or parts having similar functions are denoted by the same reference numerals in different drawings, and redundant explanations may be omitted.

[0016] When there are multiple elements having the same or similar functions, they may be described using the same reference numeral with different subscripts. However, when there is no need to distinguish between multiple elements, the subscripts may be omitted.

[0017] The designations "first," "second," "third," etc. in this specification are used to identify components and do not necessarily limit the number, order, or content thereof. Furthermore, numbers used to identify components are used in different contexts, and numbers used in one context do not necessarily indicate the same configuration in another context. Furthermore, this does not prevent a component identified by a certain number from also serving the function of a component identified by another number.

[0018] The position, size, shape, range, etc. of each component shown in the drawings, etc. may not represent the actual position, size, shape, range, etc. in order to facilitate understanding of the invention. Therefore, the present invention is not necessarily limited to the position, size, shape, range, etc. disclosed in the drawings, etc. Publications, patents, and patent applications cited in this specification constitute part of the description of this specification in their entirety.

[0019] Representative embodiments for carrying out the present invention will be described below with reference to the drawings as appropriate. In this embodiment, an input procedure generation device used in a repair recommendation system that recommends an appropriate failure cause class based on an AI engine when an asset listed above malfunctions or breaks down will be described in detail. A failure cause class is a number that uniquely determines the cause of the failure and the details of the repair work, and is often compiled as failure cause information that links the name of the failure cause and the name of the repair work.

[0020] One embodiment will be described. A preferred embodiment of the repair recommendation system includes an AI engine management unit that manages input variable groups, output variable groups, and information indicating their causal relationships and probability distributions, a variable selection information management unit that manages variable selection information that selects input variables based on Key Performance Indicators (KPIs) related to the user's business, a key KPI reception unit that inputs key KPI information that indicates KPIs that the user considers important, a variable selection unit that selects input variable groups, an input procedure generation unit that generates input procedure information that indicates the order of input variables to be input to the AI ​​engine, an input reception unit that allows the user to input input variable values ​​based on the input procedure information, a recommendation probability calculation unit that calculates the probability of an output variable using the received input variable values, the selected input variable group, and the AI ​​engine information, and a recommendation result display unit that displays the calculated probability of the output variable.

[0021] In this system, the variable selection unit selects a group of input variables based on key KPI information and variable selection information, and the input procedure generation unit generates input procedure information indicating the order of the input variables based on the selected group of input variables, key KPI information, and AI engine information.

[0022] In this specification, KPI is a broad concept that refers to an index that indicates any value standard that a user considers important. By generating input procedures for an AI engine based on the KPIs that a user considers important, KPIs can be improved from input to output. Also, different KPIs can be improved for input and output. [Example]

[0023] <1. Overview> Figure 1 shows the time series of "input" and "output" in the AI ​​engine of the repair recommendation system. Figure 1 shows the relationship between the time series range 1 of "failure cause investigation (input)" and "failure cause recommendation (output)", the AI ​​engine 2 that outputs the failure cause based on the input information, the information inputter 3 that inputs information about the failed asset to the AI ​​engine, and the output result user 4 that carries out repairs based on the output results from the AI ​​engine.

[0024] Here, the AI ​​engine can contribute to improving the KPIs in the work of the information inputter 3 and the output result user 4 within the time series range 1. However, in the conventional AI engine, the "fault cause recommendation (output)" only produces output results that match the KPIs of the output result user 4, resulting in missed opportunities for the AI ​​engine to improve the KPIs.

[0025] In this regard, in this embodiment, by recommending to a person inputting information to the AI ​​engine an input procedure that matches the KPI of that person, it is possible to improve the KPI over the entire time series range 1. Furthermore, it is possible to improve different KPIs for the information input person 3 and the output result user 4.

[0026] In this embodiment, we will explain a configuration that realizes a recommendation system that can improve KPIs in a seamless manner from input to output by generating input procedure information for an AI engine based on KPIs that users consider important.

[0027] In addition, in FIG. 1, the information inputter 3 and the output result user 4 are shown as different persons, but they may be the same person.

[0028] The registration phase and recommendation phase of the repair recommendation system will be described with reference to Figure 2. The registration phase refers to the flow from registering the AI ​​engine, registering the variable selection information management unit, and registering the recommendation judgment information. The recommendation phase refers to the flow from requesting repairs from the asset owner to the call center, the call center starting to use the repair recommendation system, the repair recommendation system recommending input procedures to the call center, the call center collecting information in accordance with the input procedures, and recommending the cause of the failure based on the input information. An overview of the repair recommendation system 10 will be explained, divided into the registration phase and the recommendation phase.

[0029] The assets 20 are devices, equipment, vehicles, etc. The asset owner 15 is the owner or manager of the asset 20, and requests repairs from the call center 16 when a malfunction occurs in the asset 20. In response to the repair request from the asset owner 15, the call center 16 repeatedly inquires about the status of the asset 20 from the asset owner 15 and identifies the cause of the malfunction. If the identified cause of the malfunction is one that the asset owner 15 can repair on its own, the call center 16 responds with a repair method. If the cause of the malfunction cannot be identified or the malfunction is one that the asset owner 15 cannot fix, the case is handed over to the maintenance execution team 17. The maintenance execution team 17 visits the facility or factory where the asset 20 is installed to identify the cause of the malfunction and perform repairs.

[0030] <1-1. Registration Phase> In Figure 2, first, in the registration phase, the administrator 14 sends, as AI engine information, input variables (hereinafter referred to as "investigation items") representing the state of the asset 20, output variables (hereinafter referred to as "failure causes") representing information on the cause of failure of the asset 20, the causal relationship between the investigation items and the cause of failure, and a probability distribution expressing the causal relationship in terms of probability, to the repair recommendation system 10 via the administrator terminal 11.

[0031] The repair recommendation system 10 receives information sent from the administrator at the AI ​​engine information registration unit 1041, and manages the information as AI engine information at the AI ​​engine information management unit 1011. The AI ​​engine information will be described later with reference to FIGS.

[0032] Next, the administrator 14 sends to the repair recommendation system 10 via the administrator terminal 11 the KPI for each user, the work of investigating the cause of the failure in order to improve that KPI, variable condition information for the KPI that associates the conditions to be applied to repair the cause of the failure, and variable information for the variable conditions that summarizes each investigation item and which variable condition information the cause of the failure satisfies. The repair recommendation system 10 receives the information sent from the administrator in the variable selection information registration unit 1042, and manages the variable condition information for the KPI and the variable information for the variable conditions as variable selection information in the variable selection information management unit 1012. The variable selection information will be explained later using Figures 9 and 10.

[0033] Then, the administrator 14 sends input procedure generation method selection information summarizing which input procedure generation method is suitable for the input conditions to the repair recommendation system 10 via the administrator terminal 11. Here, the input procedure generation method is a method of ranking which survey item answers should be entered. The repair recommendation system 10 receives the input procedure generation method selection information in the input procedure generation method selection information registration unit 1043, and manages the input procedure generation method selection information in the input procedure generation method selection information management unit 1013. The input procedure generation method selection information will be described later using FIG. 11.

[0034] Thereafter, the administrator 14 sends to the repair recommendation system 10 via the administrator terminal 11 a recommendation probability threshold indicating the probability of a failure cause calculated by the AI ​​engine based on the user's responses to the survey items at which the cause must be presented to the user as a failure cause, and an investigation count threshold indicating how many times the user is required to respond to additional survey items if the probability of each failure cause is lower than the recommendation probability threshold. The repair recommendation system 10 receives the information sent from the administrator in the recommendation determination information registration unit 1044, and manages the recommendation probability threshold and the investigation count threshold as recommendation determination information in the recommendation determination information management unit 1014. The recommendation determination information will be described later using FIG. 12.

[0035] <1-2. Recommendation Phase> Next, in the recommendation phase, when a failure occurs in the asset 20, the asset owner 15 transmits a repair request for the asset 20 and the KPIs that are considered important for the repair (referred to as "key KPIs") to the call center 16 via the counter terminal 13. The call center 16 sends the KPIs that the asset owner 15 considers important and the KPIs that the call center 16 considers important to the repair recommendation system 10 via the user terminal 12. The repair recommendation system 10 receives the information sent from the call center 16 in the key KPI receiving unit 1021.

[0036] Next, the variable selection unit 1022 extracts a group of input variable conditions and a group of output variable conditions corresponding to the KPIs from the variable condition information for the KPIs managed by the variable selection information management unit 1012 and the key KPIs sent from the call center 16. After that, from the variable information for the variable conditions managed by the variable selection information management unit 1012, a group of investigation items and a group of failure causes that satisfy the group of variable conditions are selected.

[0037] Next, the input procedure generation method selection unit 1023 selects one input procedure generation method from the input procedure generation method selection information and the input variable condition group managed by the input procedure generation method selection information management unit 1013 .

[0038] Next, the input procedure generation unit 1024 ranks the survey items to be answered based on the survey items and causes of failure selected by the variable selection unit 1022, the input procedure generation method selected by the input procedure generation method selection unit 1023, and the AI ​​engine information.

[0039] Next, the input receiving unit 1031 presents the survey items ordered by the input procedure generation unit 1024 to the call center 16 via the user terminal 12. The call center 16 inquires about the status of the asset 20 from the asset owner 15 via the counter terminal 13 based on the presented survey items. The asset owner 15 investigates the status of the asset 20 and transmits the response result to the call center 16 via the counter terminal 13. The call center 16 inputs the response result to the input receiving unit 1031.

[0040] Then, the recommendation probability calculation unit 1032 calculates the probability of each failure cause based on the answers to the survey items received by the input reception unit 1031.

[0041] Thereafter, the recommendation determination unit 1033 determines whether to recommend, continue input, or terminate based on the answers to the survey items received by the input unit, the calculated probability of each failure cause, the survey items selected by the variable selection unit 1022, and the recommendation probability threshold and survey count threshold managed by the recommendation determination information management unit 1014.

[0042] If a recommendation is made, the recommendation result display unit 1034 recommends the failure cause information of the most probable failure cause to the call center 16. Based on the recommended failure cause information, the call center 16 transmits the failure cause information to the asset owner 15. The asset owner 15 repairs the asset 20 based on the transmitted failure cause information.

[0043] If input is to be continued, the process returns to the input procedure generation unit 1024, and the system re-prioritizes which survey items should be answered based on the cause of the failure selected by the variable selection unit 1022, the input procedure generation method selected by the input procedure generation method selection unit 1023, the AI ​​engine information, and the answers to the survey items received by the input reception unit 1031.

[0044] Thereafter, the input receiving unit 1031 presents the survey items ordered by the input procedure generating unit 1024 to the call center 16 again via the user terminal 12, and prompts for additional responses. If the survey is to be terminated, the recommendation result display unit 1034 recommends to the call center 16 that the survey be handed over to the maintenance execution team 17.

[0045] The call center 16 requests repairs from the maintenance execution team 17. The maintenance execution team 17 visits the facility or factory where the asset 20 is installed and sends the KPIs that the asset owner 15 considers important and the key KPIs of the maintenance execution team 17 to the repair recommendation system 10. Thereafter, the maintenance execution team 17 uses the repair recommendation system 10 in the same way as the call center 16 to investigate the cause of the failure of the asset 20 and carry out repairs.

[0046] <2. System Configuration> <2-1. Functional Blocks> The configuration of the system according to this embodiment will be described with reference to Fig. 2. This system comprises, as its components, a repair recommendation system 10, an administrator terminal 11 operated by an administrator, user terminals 12 operated by a call center 16 and a maintenance execution team 17, assets 20 owned by an asset owner 15, and a contact terminal 13 through which calls are made between the call center and the asset owner.

[0047] These components are interconnected by wired or wireless communication lines 18. The communication lines 18 themselves are composed of telephone lines, LANs (Local Area Networks), WANs (Wide Area Networks), etc. The above components are merely examples, and the number of components may be increased or decreased. For example, for distributed processing, the repair recommendation system 10 may be divided into multiple parts.

[0048] The following describes in detail the repair recommendation system 10. The repair recommendation system 10 includes a management unit 101, an input procedure generation device 102, a recommendation unit 103, and a registration unit 104.

[0049] The management unit 101 includes a variable selection information management unit 1012 , an input procedure generation method selection information management unit 1013 , an AI engine information management unit 1011 , and a recommendation determination information management unit 1014 .

[0050] The input procedure generation device 102 includes a key KPI reception unit 1021 , a variable selection unit 1022 , an input procedure generation method selection unit 1023 , and an input procedure generation unit 1024 .

[0051] The recommendation unit 103 includes an input receiving unit 1031 , a recommendation probability calculation unit 1032 , a recommendation determination unit 1033 , and a recommendation result display unit 1034 .

[0052] The registration unit 104 includes an AI engine information registration unit 1041, a variable selection information registration unit 1042, an input procedure generation method selection information registration unit 1043, and a recommendation determination information registration unit 1044. Note that in the recommendation phase, for example, the registration unit can be omitted.

[0053] <2-2. Functions and Hardware> Fig. 3 shows an example of the hardware configuration of the repair recommendation system 10. Next, the correspondence between functions and hardware will be explained with reference to Fig. 2 and Fig. 3. Fig. 2 shows an example of the functional configuration of the repair recommendation system 10. The hardware of the repair recommendation system 10 is configured by a computer such as a server device, for example.

[0054] The management unit 101, input procedure generation device 102, recommendation unit 103, and registration unit 104 of the repair recommendation system 10 shown in Figure 2 include a CPU (Central Processing Unit) 1H101, a ROM (Read Only Memory) 1H102, a RAM (Random Access Memory) 1H103, an external storage device 1H104, a communication I / F (Interface) 1H105, an external input device 1H106 represented by a mouse, keyboard, etc., and an external output device 1H107 represented by a display, etc., shown in Figure 3.

[0055] The CPU 1H101 loads a program stored in the ROM 1H102 or the external storage device 1H104 into the RAM 1H103 and controls the communication I / F 1H105, the external input device 1H106, and the external output device 1H107, thereby realizing various functions.

[0056] In this embodiment, functions such as calculation and control in a computer are realized by a processor such as CPU 1H101 executing a program stored in a storage device such as ROM 1H102 or external storage device 1H104, thereby performing predetermined processing in cooperation with other hardware. A program executed by a computer or the like, its function, or means for realizing the function may be referred to as a "function," "means," "part," "unit," "module," "model," or the like.

[0057] The repair recommendation system 10 may be configured with a single computer, or any part may be configured with other computers connected via a network. The concept of the invention is equivalent and remains unchanged. In addition, functions equivalent to those configured with software in this embodiment can also be realized with circuits (hardware) such as FPGAs (Field Programmable Gate Arrays) and ASICs (Application Specific Integrated Circuits).

[0058] As described above, the repair recommendation system using the embodiment includes a computer including a storage device for storing information and one or more processors connected to the storage device. The storage device of the computer stores AI engine information using a Bayesian network and variable selection information for selecting input variables based on KPIs related to the user's business. The processor of the computer performs the following processes (X1) to (X6). (X1) A first process of inputting key KPI information indicating KPIs that a user considers important; (X2) A second process of selecting a set of input variables using the key KPI information and the variable selection information; (X3) a third process of generating input procedure information indicating the order of input variables to be input to the AI ​​engine using the selected input variables and AI engine information; (X4) A fourth process of inputting values ​​of input variables using input procedure information; (X5) A fifth process for calculating the probability of an output variable using the values ​​of the input variables input in the fourth process, the selected input variable group, and AI engine information; (X6) The sixth process displays the probability of the output variable calculated in the fifth process.

[0059] <2-3. Data Structure> <2-3-1. AI engine information> 4 to 8, the AI ​​engine information managed by the AI ​​engine information management unit 1011 of the management unit of the repair recommendation system 10 will be described. Although not particularly limited, in this embodiment, a Bayesian network is used for the AI ​​engine. A Bayesian network is a type of graphical model that can visually describe the structure of a probabilistic model.

[0060] FIG. 4 shows an outline of a Bayesian network used in the AI ​​engine of the repair recommendation system 10 in this embodiment. There are two types of random variables in the Bayesian network: failure causes 1B101 and investigation items 1B102, and the conditional dependence (probabilistic dependency) between the failure causes and investigation items is represented by arrows 1B103. The Bayesian network in FIG. 4 can be defined by the data structures in FIGS. 5 to 8. In the AI ​​engine, the investigation items are used as input variables, and the failure causes are used as output variables. Details will be explained below.

[0061] 5, the failure cause information 1T1 in the AI ​​engine information 1T will be described. The failure cause information includes a failure cause name 1T11 that describes the name of the failure cause and a repair method name 1T12 that describes the name of the repair method. In this embodiment, the failure cause information includes the above items, but it may also include items related to repair work, such as a repair type linked to a repair procedure manual, a file or URL (Uniform Resource Locator) for the repair procedure manual, or some of the above items.

[0062] The investigation item information 1T2 in the AI ​​engine information 1T will be described using Figure 6. The investigation item information includes an investigation item name 1T21 that describes the name of the investigation item. In this embodiment, the investigation item information includes the above items, but it may also include items related to investigation work, such as investigation procedures linked to troubleshooting flows, or parts required for investigation, or it may include some of the above items.

[0063] The causal relationship information 1T3 between failure causes and investigation items in the AI ​​engine information will be explained using Figure 7. The causal relationship information 1T3 includes a failure cause name 1T31 that describes the failure cause that is the parent of the investigation item, and an investigation item name 1T32 that describes the child investigation item.

[0064] The probability distribution information 1T4 in the AI ​​engine information will be explained using Figures 8A and 8B. Figure 8A shows the probability distribution 1T41 of each failure cause in the probability distribution information 1T4. Figure 8B shows the probability distribution 1T42 of each investigation item in the probability distribution information 1T4.

[0065] The probability distribution 1T41 of the failure cause in FIG. 8A includes an item 1T411 for recording the probability that the failure cause does not occur when a failure occurs in an asset, and an item 1T412 for recording the probability that the failure cause occurs.

[0066] Item 1T422 in Figure 8B is a joint probability distribution that describes the probability of a survey item's response to the state of the parent failure cause of the survey item. The probability distribution of a survey item includes item 1T421, which describes the state of the failure cause, item 1T422, which describes the probability that the survey item's response will be YES, and item 1T423, which describes the probability that the survey item's response will be NO. There can be any number of items 1T421, and the state of the failure cause can be expressed by a combination of the states indicated by any number of items 1T421.

[0067] In this embodiment, the answers to the survey items are binary, YES and NO, but there may be more than two answers, such as three values, YES, NO, and unknown, or four values, green on, red on, flashing, and off.

[0068] In this embodiment, it is assumed that the AI ​​engine information 1T is created by an administrator from design and maintenance information related to the asset, such as product specifications, maintenance procedures, FMEA (Failure Mode and Effects Analysis), and FT (Fault Tree). However, it may also be created using a Bayesian network structural learning algorithm, such as the K2 algorithm, from a repair history that collects pairs of responses to survey items that occurred when an asset failed and the cause of the failure at that time.

[0069] Furthermore, the description format of the AI ​​engine information 1T shown in FIGS. 5 to 8 is a commonly known description format for a Bayesian network, and other formats for describing a Bayesian network may also be used.

[0070] The AI ​​engine may also be a simplified model of a Bayesian network such as Naive Bayes, or an approximation model of a Bayesian network such as a Noisy-max model.

[0071] <2-3-2. Variable selection information> The variable selection information managed by the variable selection information management unit 1012 of the management unit of the repair recommendation system 10 will be described with reference to FIGS.

[0072] 9 shows variable condition information 1D1 for KPIs in variable selection information 1D. The variable condition information 1D1 for KPIs includes a list of users 1D101, a list of KPIs for each user 1D102, a list of input conditions 1D103, a list of output conditions 1D104, and an entry field 1D105.

[0073] In entry field 1D105, mark whether the corresponding KPI can be improved. For example, if the input condition is "quickly investigate the cause of failure," mark the KPIs of reducing business downtime for asset owners, reducing investigation time for call centers, and reducing investigation time for maintenance teams as being able to be improved.

[0074] In this embodiment, "◯" is used to indicate that improvement is possible, and "-" is used to indicate that improvement is not possible or that the condition is irrelevant, but other symbols may be used, or a numerical value indicating the degree of influence may be used. The variable condition information for a KPI may be managed separately for input and output, or may be for input only.

[0075] In the example of FIG. 9, conditions are defined for each asset owner, call center, and maintenance execution team, but different conditions may be defined for each of multiple asset owners, for example.

[0076] 10 shows variable information 1D2 for the variable conditions in variable selection information 1D. The variable information 1D2 for the variable conditions includes a list 1D201 of input and output conditions, a list 1D202 of investigation item names, a list 1D203 of fault cause names, and an entry field 1D204. In the entry field 1D204, a mark indicating whether or not the corresponding input condition is met is entered.

[0077] For example, if the investigation item name is "Is the casing LED lit green?", it is marked as meeting the input conditions of investigating the cause of the failure without a jig, investigating the cause of the failure without stopping business operations, and investigating the cause of the failure safely.

[0078] In this embodiment, "◯" is marked if improvement is possible, and "-" is marked if improvement is not possible or irrelevant, but other symbols may be used, or a numerical value indicating the degree of impact on the KPI may be used. Variable information for variable conditions may be managed separately for input and output, or may be input only. The input conditions and output conditions listed in the list of input conditions and the list of output conditions may only be partial. The investigation items and failure causes listed in the list of investigation item names and the list of failure cause names may only be partial.

[0079] In this embodiment, a hierarchical structure is used in which variable conditions are linked to key KPI information, and variables for investigation items and failure causes are linked to the variable conditions, but it is also possible to link variables to key KPI information.Other hierarchical structures are also possible.

[0080] <2-3-3. Input procedure generation method selection information> 11, a description will be given of the input procedure generation method selection information 1S managed by the input procedure generation method selection information management unit 1013 of the management unit 101 of the repair recommendation system 10. The input procedure generation method selection information 1S describes which input procedure generation method is to be used to rank the survey items for the input conditions.

[0081] The input procedure generation method selection information 1S includes an input condition 1S1 that describes the input conditions necessary for selecting an input procedure generation method, and an input procedure generation method name 1S2 that describes the name of the input procedure generation method that ranks the survey items.

[0082] In this embodiment, an input procedure generation method for ordering a plurality of items is selected based on a combination of input conditions. That is, an input procedure generation method is selected based on two input conditions, "quickly investigate the cause of the failure" and "reliably investigate the cause of the failure." However, the method may be selected based on one input condition or a combination of three or more input conditions.

[0083] <2-3-4. Recommendation judgment information> 12, the recommendation determination information 1X managed by the recommendation determination information management unit 1014 of the management unit 101 of the repair recommendation system 10 will be described. The recommendation determination information includes a recommendation probability threshold 1X1, which indicates the probability of a failure cause calculated by the AI ​​engine based on the user's responses to survey items if the cause is to be presented to the user as a failure cause, and a survey count threshold 1X2, which indicates how many times the user is required to respond to additional survey items if the probability of each failure cause is lower than the recommendation probability threshold.

[0084] <2-3-5. Priority KPI information> 13, the key KPI information 1R1 that the key KPI receiving unit 1021 of the input procedure generation device 102 of the repair recommendation system 10 receives from the call center 16 via the user terminal 12 will be described. The key KPI information 1R1 includes a user name 1R101 related to asset repair and a key KPI 1R102 that describes the user's key KPI. The key KPI information 1R1 is either collected and stored in advance by the call center 16 from each user, or collected from each user at the start of the recommendation phase.

[0085] <2-3-6. Input procedure information> 14, the input procedure information 1R2 that the input receiving unit 1031 of the recommendation unit of the repair recommendation system 10 presents to the call center 16 via the user terminal 12 will be described. The input procedure information 1R2 includes an input order 1R201 indicating which survey items should be surveyed, a survey item name 1R202, and a score 1R203 that is the basis for ranking.

[0086] <2-3-7. Survey item response information> 15, the following describes the survey item answer information 1R3 that the call center 16 inputs to the input receiving unit 1031 of the recommendation unit 103 of the repair recommendation system 10 via the user terminal 12. The survey item answer information 1R3 includes a survey item name 1R301 indicating the name of the survey item conducted on the asset and an answer 1R302 indicating the answer to the survey item. The survey item answer information 1R3 becomes an input variable to be input to the AI ​​engine 2.

[0087] <2-3-8.Recommendation result information> 16, the recommendation result information 1R4 that the recommendation result display unit 1034 of the recommendation unit 103 of the repair recommendation system 10 recommends to the call center 16 via the user terminal 12 will be described. The recommendation result information 1R4 includes a recommendation order 1R401 indicating the order of the recommended failure causes, a failure cause name 1R402, a repair method name 1R403, and a recommendation probability 1R404 indicating the probability of the failure cause.

[0088] <3. Registration Phase Processing Flow> The processing flow of the registration phase will be described with reference to FIG. First, the AI ​​engine information registration unit 1041 registers the AI ​​engine information 1T input by the administrator 14 via the administrator terminal 11 in the AI ​​engine information management unit 1011 (step 1F101).

[0089] Next, the variable selection information registration unit 1042 registers the variable condition information 1D1 for the KPI and the variable information 1D2 for the variable condition, which have been input by the manager 14 via the manager terminal 11, in the variable selection information management unit 1012 (step 1F102).

[0090] Thereafter, the input procedure generation method selection information registration unit 1043 registers the input procedure generation method selection information 1S input by the administrator 14 via the administrator terminal 11 in the input procedure generation method selection information management unit 1013 (step 1F103).

[0091] Then, the recommendation determination information registration unit 1044 registers the recommendation determination information 1X input by the administrator 14 via the administrator terminal 11 in the recommendation determination information management unit 1014 (step 1F104).

[0092] Note that the order of steps 1F101, 1F102, 1F103, and 1F104 may be reversed, and steps may be skipped if the AI ​​engine information 1T, variable selection information 1D, input procedure generation method selection information 1S, and recommendation judgment information 1X have been registered in advance in each management unit.

[0093] <4. Recommendation Phase Processing Flow> The processing flow of the recommendation phase will be described with reference to FIG. First, the key KPI receiving unit 1021 receives the key KPI information 1R1 (FIG. 13) from the call center 16 via the user terminal 12 (step 1F201).

[0094] Next, the variable selection unit 1022 extracts a group of input conditions and a group of output conditions marked for the key KPI from the variable condition information 1D1 (FIG. 9) and key KPI information 1R1 (FIG. 13) for the KPI managed by the variable selection information management unit 1012. For example, if the key KPI information is "reducing the asset owner's scheduled work hours" and "reducing the rate of requests for visits by the call center maintenance execution team," the group of input conditions will be "quickly investigate the cause of the failure," "investigate the cause without stopping operations," and "investigate the cause of the failure without using a jig," and the group of output conditions will be "repair the cause of the failure without stopping operations" and "repair the cause of the failure without using replacement parts."

[0095] Thereafter, a group of investigation item names and a group of failure cause names marked for the group of input conditions and the group of output conditions are selected from the variable information 1D2 (FIG. 10) for the variable conditions managed by the variable selection information management unit 1012 and the extracted group of input conditions and group of output conditions. For example, in the case of the group of input conditions and group of output conditions extracted in the example above, the group of investigation item names would be "Is the housing LED lit green?", "Is E11 being output?", etc., and the group of failure cause names would be "Poor cable contact" and "Insufficient memory capacity" (step 1F202).

[0096] Next, the input procedure generation method selection unit 1023 selects an input procedure generation method from the input procedure generation method selection information 1S (FIG. 11) managed by the input procedure generation method selection information management unit 1013 and the extracted input condition group. For example, in the above example, the input condition is "quickly investigate the cause of the failure," so the "failure cause identification-oriented input procedure generation method" is selected (step 1F203).

[0097] Next, based on the AI ​​engine information 1T (Figures 5 to 8B) managed by the AI ​​engine information management unit 1011, the group of survey item names selected by the variable selection unit 1022, the group of fault cause names, and the input procedure generation method selected by the input procedure generation method selection unit 1023, the survey items are ranked to determine which survey items should be answered, and input procedure information 1R2 (Figure 14) is generated (step 1F204).

[0098] In this embodiment, there are two methods for generating input procedure information: a fault cause identification-oriented input procedure generation method and a fault cause isolation-oriented input procedure generation method. Each input procedure generation method will be described in detail below.

[0099] The failure cause identification-focused input procedure generation method is an input procedure generation method that can reduce the number of times a survey item needs to be answered by assigning a high score to survey items that increase the probability of a failure cause that has a high probability of occurring when a response to the survey item is obtained. However, the failure cause identification-focused input procedure generation method may leave many failure causes that have a non-0% probability of occurring at the time of recommendation, and may not reliably identify the cause of the failure. For this reason, it is suitable for situations such as when an asset failure has caused the asset owner's business to be suspended.

[0100] A specific method of assigning scores is, for example, the sum of squares of the conditional probability of each recommendation probability when a response to the survey item is obtained. If the number of survey item name groups selected by the variable selection unit 1022 is I, the number of failure cause name groups selected by the variable selection unit 1022 is C, and each survey item name is survey item i (=1, 2, ..., I), and each failure cause name is failure cause c (=1, 2, ..., C), the score of survey item i is Score of survey item i = (probability that failure cause 1 occurs when a response to survey item i is obtained)^2 + (probability that failure cause 2 occurs when a response to survey item i is obtained)^2 + ... + (probability that failure cause C occurs when a response to survey item i is obtained)^2 This allows a higher score to be assigned to an investigation item that increases the probability of a high probability failure cause.

[0101] On the other hand, the failure cause isolation focused input procedure generation method is an input procedure generation method that can reliably identify the cause of a failure by assigning a higher score to survey items that have a higher probability of occurring when answers to the survey items are obtained, and a lower probability to failure causes that have a lower probability of occurring. However, the failure cause isolation focused input procedure generation method may require a large number of answers to the survey items. Therefore, it is suitable for situations where the asset failure is minor and the asset owner's business is not stopped.

[0102] A specific method of assigning a score is, for example, the sum of the average information amount of each recommendation probability when a response to the survey item is obtained multiplied by -1. If the number of survey item name groups selected by the variable selection unit 1022 is I, the number of failure cause name groups selected by the variable selection unit 1022 is C, and each survey item name is survey item i (=1, 2, ..., I) and each failure cause name is failure cause c (=1, 2, ..., C), the score of survey item i is Score of survey item i = -1 × {(average amount of information about failure cause 1 when a response to survey item i is obtained) - (average amount of information about failure cause 2 when a response to survey item i is obtained) - ... - (average amount of information about failure cause C when a response to survey item i is obtained)} It is calculated as follows.

[0103] Because the average information volume is a convex function that takes its minimum value when the probability of occurrence is 100% or 0%, it is multiplied by -1. This allows a higher score to be assigned to survey items that increase the probability of relatively high-probability failure causes and decrease the probability of relatively low-probability failure causes.

[0104] The probability calculation in this embodiment uses Loopy belief propagation, Markov chain Monte Carlo methods, etc., which are widely known as inference algorithms for Bayesian networks. Note that the input procedure generation method may be other than the fault cause identification-focused input procedure generation method and the fault cause isolation-focused input procedure generation method.

[0105] After the survey items and their scores are calculated using the input procedure generation method, the survey items are sorted and ranked based on the scores to generate input procedure information 1R2 (FIG. 14).

[0106] As described above, the input procedure generation device 102 can generate input procedure information for an AI engine based on KPIs that the user considers important.

[0107] Next, the input receiving unit 1031 displays the name of the input procedure generation method selected by the input procedure generation method selection unit 1023 and the input procedure information 1R2 (Figure 14) generated by the input procedure generation unit 1024 to the call center 16 via the user terminal 12, and receives survey item response information 1R3 (Figure 15) from the call center 16 via the user terminal 12 (step 1F205).

[0108] Then, the recommendation probability calculation unit 1032 calculates the probability that the cause of the failure will occur when the answer to the survey item received by the input receiving unit 1031 is obtained (step 1F206).

[0109] Thereafter, the recommendation determination unit 1033 determines whether to recommend, continue, or terminate based on the answers to the survey items received by the input receiving unit 1031, the probability of each cause of failure occurring calculated by the recommendation probability calculation unit 1032, the survey items selected by the variable selection unit 1022, the recommendation probability threshold managed by the recommendation determination information management unit 1014, and the survey count threshold managed by the recommendation determination information management unit 1014 (Figure 12) (step 1F207).

[0110] If the highest probability of each failure cause occurring is equal to or greater than the recommendation probability threshold, a recommendation judgment is made. If the highest probability of each failure cause occurring is less than the recommendation probability threshold, the number of times the survey items have been answered is less than the survey count threshold, and not all of the survey items selected in the variable selection section have been answered, a continuation judgment is made. If the highest probability of each failure cause occurring is less than the recommendation probability threshold, and the number of times the survey items have been answered is equal to or greater than the survey count threshold, or all of the survey items selected in the variable selection section have been answered, a termination judgment is made.

[0111] If the determination is to continue, the process returns to step 1F204. At this time, in steps 1F204 and 1F205, the probability is calculated using the survey item response information 1R3 received by the input receiving unit 1031 as prior information.

[0112] Thereafter, recommendation result information 1R4 is created based on each recommendation probability calculated in step 1F206 and the fault cause information, and is displayed at the call center 16 via the user terminal 12 together with the determination result in step 1F207.

[0113] As described above, by adopting a configuration in which input variables are selected based on the KPIs that the user considers important, KPIs can be improved even when input to the AI ​​engine.In addition, by generating input procedure information that specifies the input order of input variables to the AI ​​engine based on the KPIs that the user considers important, a recommendation system can be realized that can improve KPIs in a seamless manner from input to output.

[0114] In this embodiment, it is assumed that the next input procedure is generated again after obtaining an answer to one survey item, but it is also possible to calculate input procedures for answer patterns of all survey items together in advance.

[0115] <5. User Interface> An example of a GUI (Graphical User Interface) displayed on the external output device 1H107, for example, a display device, during data input / output in this embodiment will be described below.

[0116] The AI ​​engine information registration screen 1G1 of the AI ​​engine information registration unit 1041 will be described using Figure 19. The AI ​​engine information registration screen 1G1 includes an AI engine information file transmission form 1G101 and a register button 1G102. The AI ​​engine information file transmission form 1G101 is used to specify a file that compiles files in CSV (Comma Separated Value) format that describe failure cause information, investigation item information, causal relationship information between the failure cause and investigation items, and probability distribution information in the AI ​​engine information. Note that any format that can express the AI ​​engine structure shown in Figure 4, such as a format that displays an empty table for input, may be used. The administrator 14 can send the entered data to the AI ​​engine information registration unit 1041 by pressing the register button 1G102.

[0117] The variable selection information registration screen 1G2 of the variable selection information registration unit 1042 will be described using FIG. 20. The variable selection information registration screen 1G2 includes a variable condition information file transmission form 1G201 for KPI, a variable information file transmission form 1G202 for variable conditions, and a register button 1G203. A CSV-formatted file containing variable condition information for the KPI is specified in the variable condition information file transmission form 1G201 for KPI. A CSV-formatted file containing variable information for the variable conditions is specified in the variable information file transmission form 1G202 for variable conditions. Note that any format may be used as long as it can express variable condition information for KPIs and variable information for variable conditions, such as a format that displays an empty table for input. The administrator can transmit the input data to the variable selection information registration unit 1042 by pressing the register button 1G203.

[0118] An input procedure generation method selection information registration screen 1G3 of the input procedure generation method selection information registration unit 1043 will be described using FIG. 21. The input procedure generation method selection information registration screen 1G3 includes an input procedure generation method selection information file transmission form 1G301 and a register button 1G302. A CSV-formatted file containing input procedure generation method selection information is specified in the input procedure generation method selection information file transmission form 1G301. Note that any format that can express the input procedure generation method selection information may be used, such as a format that displays an empty table for input. The administrator 14 can transmit the input data to the input procedure generation method selection information registration unit 1043 by pressing the register button 1G302.

[0119] The recommendation judgment information registration screen 1G4 of the recommendation judgment information registration unit 1044 will be described using FIG. 22. The recommendation judgment information registration screen 1G4 includes a recommendation judgment information file transmission form 1G401 and a register button 1G402. A CSV-formatted file containing recommendation judgment information is specified in the recommendation judgment information file transmission form 1G401. Note that any format that can express recommendation judgment information may be used, such as a format that displays an empty table for input. The administrator 14 can transmit the entered data to the recommendation judgment information registration unit 1044 by pressing the register button 1G402.

[0120] 23, the key KPI reception screen 1G5 of the key KPI reception unit 1021 will be described. The key KPI reception screen 1G5 includes a user select box 1G501, a key KPI select box 1G502, an add button 1G503, and a register button 1G504. The name of a user related to asset repair is input into the user select box 1G501. This item displays a list 1D101 of users of variable condition information 1D1 for KPIs managed by the variable selection information management unit 1012 in pull-down format.

[0121] The call center and the maintenance execution team select a user displayed in a pull-down format. The key KPI of the user entered in the user select box 1G501 is input into the key KPI select box 1G502. In this item, a list 1D102 of KPIs of variable condition information 1D1 for KPIs managed by the variable selection information management unit 1012 is displayed in a pull-down format. The call center and the maintenance execution team select a KPI displayed in a pull-down format.

[0122] The add button 1G503 can increase the number of input fields when there are multiple users. The call center and maintenance execution team can increase the user select box 1G501 and the key KPI select box 1G502 by pressing the add button 1G503. The call center and maintenance execution team can send the entered data to the key KPI reception unit 1021 by pressing the register button 1G504.

[0123] 24, the input acceptance screen 1G6 of the input acceptance unit 1031 will be described. The input acceptance screen 1G6 includes a survey item name display area 1G601, an answer button 1G602, an input procedure generation method name display area 1G603, an input procedure information display area 1G604, and a supplemental information area display switch button 1G605. The survey item name display area 1G601 displays the survey item name with the highest input priority in the input procedure information 1R2.

[0124] The call center and the maintenance execution team can send survey item response information 1R3 to the input receiving unit 1031 by pressing the response button 1G602 to which the response has been made. The input procedure generation method name display area 1G603 displays the name of the input procedure generation method selected by the input procedure generation method selection unit 1023. The input procedure information display area 1G604 displays the input procedure information 1R2.

[0125] By selecting the survey item name in the input procedure information display area 1G604, the call center and the maintenance execution team can display the selected survey item name and answer button 1G602 in the survey item name display area 1G601, and select an answer.

[0126] In this embodiment, the input procedure information display area 1G604 displays the top three survey item names, but it may also display all survey item names, or it may display survey item names whose scores exceed a predetermined value. The call center and maintenance execution team can display or hide the input procedure generation method name display area 1G603 and the input procedure information display area 1G604 by pressing the supplemental information area display switch button 1G605.

[0127] 25, a recommendation result display screen 1G7 of the recommendation result display unit 1034 will be described. The recommendation result display screen 1G7 includes a recommendation decision display area 1G71, a recommendation result information display area 1G72, and a supplemental information display area 1G73.

[0128] The recommendation determination display area 1G71 displays the recommendation determination of the recommendation determination unit 1033, either "recommended" or "ended." The recommendation result information display area 1G72 displays recommendation result information 1R4. In this embodiment, the recommendation result information display area 1G72 displays the top three names of fault causes, the corresponding repair names, and the recommendation probability, but it may also display all names of fault causes, or it may display the names of survey items whose recommendation probability exceeds a predetermined value. The supplemental information display area 1G73 displays predetermined supplemental information regarding the content displayed in the recommendation determination display area 1G71, and predetermined supplemental information regarding the content displayed in the recommendation result information display area 1G72.

[0129] In the field of machine learning, when a dataset is available, methods are devised to improve the KPIs related to the user's output by increasing the accuracy of the AI ​​engine, such as by adding noise to the dataset or by increasing the number of datasets. However, there are no innovations in terms of generating input procedures for the AI ​​engine based on the user's key KPIs in order for the AI ​​engine to improve the KPIs related to the user's input. As described above, according to this embodiment, input procedure information for an AI engine can be generated based on the user's key KPIs.

[0130] In addition, the repair recommendation system using the above input procedure information can improve the user's key KPIs in a seamless manner from input to output. Also, different key KPIs can be improved at the input and output.

[0131] (II) Supplementary Note The above-described embodiment includes, for example, the following contents. In the above-described embodiment, the example is described as being applied to a repair recommendation system, but the example is not limited to this and can be widely applied to various other systems, devices, methods, and programs.

[0132] In the above-described embodiments, some or all of the programs may be installed on a computer from a program source. The program source may be, for example, a program distribution server connected via a network or a computer-readable recording medium (e.g., a non-transitory recording medium). Also, in the above description, two or more programs may be realized as one program, or one program may be realized as two or more programs.

[0133] Furthermore, in the above-described embodiments, the configuration of each table is an example, and one table may be divided into two or more tables, or all or part of two or more tables may be one table.

[0134] Furthermore, in the above-described embodiment, the screens shown and described are merely examples, and any design may be used as long as the information received is the same.

[0135] Furthermore, the screens shown and described in the above-described embodiment are merely examples, and any design may be used as long as the recommended information is the same.

[0136] In the above-described embodiment, the output of information is not limited to display on a display screen, but may be audio output from a speaker, output to a file, printed on paper or the like by a printer, projected onto a screen or the like by a projector, or in other forms.

[0137] The above-described embodiment solves a new problem by considering improvements in customer KPIs, such as repair time and estimation accuracy, even when inputting data into an AI engine. According to the above embodiment, input procedures for the AI ​​engine can be generated based on KPIs that users consider important, enabling the realization of an efficient recommendation system, which reduces energy consumption, reduces carbon emissions, prevents global warming, and contributes to the realization of a sustainable society. [Explanation of symbols]

[0138] 10...Repair recommendation system, 102...Input procedure generation device, 1021...Key KPI reception unit, 1022...Variable selection unit, 1023...Input procedure generation method selection unit, 1024...Input procedure generation unit

Claims

1. An information processing device that uses an AI engine, comprising a calculation unit and a storage unit, The calculation unit a key KPI reception unit that receives key KPI information designated by a user; a variable selection unit that selects input variables to be input to the AI ​​engine based on the key KPI information; Perform the functions of The AI ​​engine is The method is represented by a network that uses at least one selected from a Bayesian network, a Naive Bayes model, and a Noisy-max model, has causes as random variables that are output variables, and items and answers to the items as random variables that are input variables, each of which has a node, and defines conditional dependencies connecting the nodes of the causes and the items; The storage unit First data that stores the key KPI information and input conditions in association with each other; second data that associates and stores the key KPI information with the items; third data for storing a plurality of combinations of the input conditions and a plurality of input procedure generation methods, which are rules for ordering the items, in association with each other; The variable selection unit Identifying the plurality of input conditions linked to the key KPI information by referring to the first data; Identifying the plurality of items linked to the key KPI information by referring to the second data; an input procedure generation method selection unit; The input procedure generation method selection unit selecting the input procedure generation method based on the combination of the plurality of input conditions identified by the variable selection unit with reference to the third data; an input procedure generation unit; the input procedure generation unit generates input procedure information for determining an order of the plurality of items identified by the variable selection unit based on the input procedure generation method; An input receiving unit is provided, the input receiving unit transmits the input procedure information generated by the input procedure generating unit via an external terminal, and receives the items and answers to the items corresponding to the transmission via the external terminal; A recommendation probability calculation unit is provided, the recommendation probability calculation unit uses the items and answers to the items received by the input reception unit as input variables of the AI ​​engine to calculate the probability of the output variable being the cause using the AI ​​engine; 1. An information processing device comprising:

2. the input procedure generation method selection unit selects from at least two types of input procedure generation methods; One of the two types is a cause identification focused input procedure generation method that calculates the scores of the multiple items identified by the variable selection unit as the sum of squares of the probability of each cause when the answer corresponding to the item is obtained, and determines the order of items to be used as input variables in descending order of the scores.

2. The information processing device according to claim 1.

3. the input procedure generation method selection unit selects from at least two types of input procedure generation methods; One of the two types is a cause isolation focused input procedure generation method that calculates the scores of the plurality of items identified by the variable selection unit by multiplying the sum of the average information amount of each cause when the answer corresponding to the item is obtained by (-1), and determines the order of the items to be used as input variables in descending order of the scores.

2. The information processing device according to claim 1.

4. There are multiple users, and each user specifies the key KPI information.

2. The information processing device according to claim 1.

5. An input control method using an information processing device that utilizes an AI engine, using a calculation unit and a storage unit, The calculation unit a key KPI reception unit that receives key KPI information designated by a user; a variable selection unit that selects input variables to be input to the AI ​​engine based on the key KPI information; Perform the functions of The AI ​​engine is The method is represented by a network that uses at least one selected from a Bayesian network, a naive Bayes model, and a Noisy-max model, has causes as random variables that are output variables, and items and answers to the items as random variables that are input variables, each of which has a node, and defines conditional dependencies connecting the nodes of the causes and the items; The storage unit First data that stores the key KPI information and input conditions in association with each other; second data that associates and stores the key KPI information with the items; third data for storing a plurality of combinations of the input conditions and a plurality of input procedure generation methods, which are rules for ordering the items, in association with each other; The variable selection unit Identifying the plurality of input conditions linked to the key KPI information by referring to the first data; Identifying the plurality of items linked to the key KPI information by referring to the second data; the information processing device includes an input procedure generation method selection unit, The input procedure generation method selection unit selecting the input procedure generation method based on the combination of the plurality of input conditions identified by the variable selection unit with reference to the third data; the information processing device includes an input procedure generation unit, the input procedure generation unit generates input procedure information for determining an order of the plurality of items identified by the variable selection unit based on the input procedure generation method; the information processing device includes an input receiving unit, the input receiving unit transmits the input procedure information generated by the input procedure generating unit via an external terminal, and receives the items and answers to the items corresponding to the transmission via the external terminal; the information processing device includes a recommendation probability calculation unit, the recommendation probability calculation unit uses the items and answers to the items received by the input reception unit as input variables of the AI ​​engine to calculate the probability of the output variable being the cause using the AI ​​engine; An input control method comprising:

6. the input procedure generation method selection unit selects from at least two types of input procedure generation methods; One of the two types is a cause identification focused input procedure generation method that calculates the scores of the multiple items identified by the variable selection unit as the sum of squares of the probability of each cause when the answer corresponding to the item is obtained, and determines the order of items to be used as input variables in descending order of the scores.

6. The input control method according to claim 5.

7. the input procedure generation method selection unit selects from at least two types of input procedure generation methods; One of the two types is a cause isolation focused input procedure generation method that calculates the scores of the plurality of items identified by the variable selection unit by multiplying the sum of the average information amount of each cause when the answer corresponding to the item is obtained by (-1), and determines the order of the items to be used as input variables in descending order of the scores.

6. The input control method according to claim 5.

8. There are multiple users, and each user specifies the key KPI information.

6. The input control method according to claim 5.

9. A program for inputting data to an AI engine from an information processing device having a calculation unit and a storage unit, The calculation unit, a key KPI reception unit that receives key KPI information designated by a user; a variable selection unit that selects input variables to be input to the AI ​​engine based on the key KPI information; Execute the function of The AI ​​engine is The method is represented by a network that uses at least one selected from a Bayesian network, a Naive Bayes model, and a Noisy-max model, has causes as random variables that are output variables, and items and answers to the items as random variables that are input variables, each of which has a node, and defines conditional dependencies connecting the nodes of the causes and the items; The storage unit First data that stores the key KPI information and input conditions in association with each other; second data that associates and stores the key KPI information with the items; third data for storing a plurality of combinations of the input conditions and a plurality of input procedure generation methods, which are rules for ordering the items, in association with each other; The variable selection unit Identifying the plurality of input conditions linked to the key KPI information by referring to the first data; Identifying the plurality of items linked to the key KPI information by referring to the second data; Execute the function of the input procedure generation method selection unit, The input procedure generation method selection unit selecting the input procedure generation method based on the combination of the plurality of input conditions identified by the variable selection unit with reference to the third data; Execute the function of the input procedure generation unit, the input procedure generation unit generates input procedure information for determining an order of the plurality of items identified by the variable selection unit based on the input procedure generation method; Execute the function of the input reception unit, the input receiving unit transmits the input procedure information generated by the input procedure generating unit via an external terminal, and receives the items and answers to the items corresponding to the transmission via the external terminal; Execute the function of the recommendation probability calculation unit, the recommendation probability calculation unit uses the items and answers to the items received by the input reception unit as input variables of the AI ​​engine to calculate the probability of the output variable being the cause using the AI ​​engine; A program characterized by:

10. the input procedure generation method selection unit selects from at least two types of input procedure generation methods; One of the two types is a cause identification focused input procedure generation method that calculates the scores of the multiple items identified by the variable selection unit as the sum of squares of the probability of each cause when the answer corresponding to the item is obtained, and determines the order of items to be used as input variables in descending order of the scores. The program according to claim 9.

11. the input procedure generation method selection unit selects from at least two types of input procedure generation methods; One of the two types is a cause isolation focused input procedure generation method that calculates the scores of the plurality of items identified by the variable selection unit by multiplying the sum of the average information amount of each cause when the answer corresponding to the item is obtained by (-1), and determines the order of the items to be used as input variables in descending order of the scores. The program according to claim 9.

12. There are multiple users, and each user specifies the key KPI information. The program according to claim 9.

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