Setting support device, setting support system, setting support method, and program

Abstract

To provide a setting support device, a setting support system, a setting support method, and a program allowing a user to easily resolve a setting problem.SOLUTION: Provided is a setting support device having: a setting problem acquisition unit that acquires a setting problem that is a problem related to the settings of a device; a device parameter acquisition unit that acquires a device parameter including the settings of the device; a calculation unit that calculates, by using a learning model, a corresponding parameter corresponding to the setting problem on the basis of the setting problem and the device parameter; and an output unit that outputs the corresponding parameter. Also provided are a setting support method and a program.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a setting support device, a setting support system, a setting support method, and a program that can easily solve setting problems. [Background technology]

[0002] For devices that require user configuration, there is a large gap between the user's problem, such as poor radio reception on a smartphone, and the solution, such as changing specific wireless communication parameters on the device. This gap exists because users do not know which configuration parameters need to be selected and changed to solve the device's problem. On the other hand, configuring devices to solve their problems is impossible without taking into account the settings within each device.

[0003] Patent Document 1 discloses the following estimation device, etc. The specification of this invention states that "the estimation unit 11 functions as estimation means for estimating an evaluation score (a relative value of communication quality after the base station parameter change to the communication quality before the base station parameter change) indicating the degree of improvement in communication quality when the base station parameters of the estimation target base station are changed from those before the setting change to those after the setting change, based on communication quality measurement information and base station parameter setting information of the estimation target base station acquired by the information acquisition unit 13. Specifically, the estimation unit 11 receives as input communication quality measurement information and base station parameter setting information of the estimation target base station before the setting change, and calculates, for each of six types of base station parameter sets Set0 to SetE described later, each evaluation score (a relative value of communication quality after the base station parameter change to the communication quality before the base station parameter change) after the setting change to the base station parameter set." [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2020-178266 Summary of the Invention [Problem to be solved by the invention]

[0005] The disclosures of the above prior art documents are incorporated herein by reference. The following analysis has been carried out by the present inventors.

[0006] As described above, in the invention disclosed in Patent Document 1, the estimation unit estimates an evaluation score indicating the degree of improvement in communication quality after changing a base station parameter set, which is a combination of base station parameters, based on communication quality measurement information and base station parameter setting information, thereby identifying a base station parameter set that will improve communication quality from among multiple parameter sets. In other words, it is possible to identify parameters whose settings should be changed.

[0007] However, the invention disclosed in Patent Document 1 does not accept problems that each device (base station) has, and does not provide a problem-solving means that allows a user to easily input a problem and output setting changes that correspond to the problem.

[0008] Therefore, in one aspect of the present invention, it is an object to provide a setting support device, a setting support system, a setting support method, and a program that enable a user to easily solve setting problems. [Means for solving the problem]

[0009] According to a first aspect of the present invention, there is provided a setting assistance device having a setting problem acquisition unit that acquires a setting problem that is a problem related to the setting of a device, a device parameter acquisition unit that acquires device parameters including the setting of the device, a calculation unit that calculates corresponding parameters corresponding to the setting problem based on the setting problem and the device parameters, and an output unit that outputs the corresponding parameters.

[0010] According to a second aspect of the present invention, there is provided a setting support system including a setting device and a setting support server device, wherein the setting device has a setting problem acquisition unit that acquires a setting problem that is a problem related to setting, a device parameter acquisition unit that acquires device parameters that include the settings of the setting device, and an input data transmission unit that transmits the setting problem and the device parameters, the setting support server device has a calculation unit that calculates corresponding parameters corresponding to the setting problem based on the setting problem and the device parameters using a learning model, and a corresponding parameter transmission unit that transmits the corresponding parameters, and the setting device further has an output unit that outputs the corresponding parameters.

[0011] According to a third aspect of the present invention, there is provided a setting support method for executing the following steps by a computer, the setting support method including: a step of acquiring a setting problem that is a problem related to the setting of a device; a step of acquiring device parameters including the setting of the device; a step of calculating corresponding parameters corresponding to the setting problem based on the setting problem and the device parameters using a learning model; and a step of outputting the corresponding parameters.

[0012] According to a fourth aspect of the present invention, there is provided a program for causing a computer to execute the following processes: a process for acquiring a setting problem, which is a problem related to device settings; a process for acquiring device parameters including the device settings; a process for calculating corresponding parameters corresponding to the setting problem based on the setting problem and the device parameters using a learning model; and a process for outputting the corresponding parameters.

[0013] The program can be recorded on a computer-readable storage medium. The storage medium can be a non-transient medium such as a semiconductor memory, a hard disk, a magnetic recording medium, or an optical recording medium. The present invention can also be embodied as a computer program product. [Effects of the Invention]

[0014] According to each aspect of the present invention, it is possible to provide a setting support device, a setting support system, a setting support method, and a program that enable a user to easily solve a setting problem. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a block diagram illustrating an example of a configuration of a setting support device according to the present disclosure. [Figure 2] FIG. 1 is a diagram illustrating an overview of processing in a setting support device according to the present disclosure. [Figure 3] 1 is a block diagram illustrating an example of a configuration of a setting support device according to the present disclosure. [Figure 4] 10 is a flowchart illustrating an operation of the setting support device of the present disclosure. [Figure 5] 1 is a block diagram illustrating an example of a hardware configuration of a setting assistance device according to the present disclosure. [Figure 6] 1 is a block diagram illustrating an example of a configuration of a setting support device according to the present disclosure. [Figure 7] 10 is a flowchart illustrating an operation of the setting support device of the present disclosure. [Figure 8] FIG. 1 is a diagram illustrating an overview of processing of a setting support system according to the present disclosure. [Figure 9] 10 is a flowchart illustrating the operation of the setting support system of the present disclosure. [Figure 10] 10 is a flowchart for explaining the operation of the learning model update process after outputting corresponding parameters in the setting support system of the present disclosure. [Figure 11] FIG. 1 is a diagram illustrating an overview of an embodiment of a setting support system according to the present disclosure. [Figure 12] FIG. 1 illustrates an example of the configuration of an embodiment of a setting support system according to the present disclosure. [Figure 13] FIG. 10 is a diagram illustrating a learning data set in an embodiment of the setting support system of the present disclosure. [Figure 14] FIG. 10 is a sequence diagram for explaining the operation of an embodiment of the setting support system of the present disclosure. [Figure 15] 10 is a flowchart illustrating an operation during continuous learning of an embodiment of the setting support system of the present disclosure. [Figure 16] FIG. 10 is a diagram illustrating a configuration in which the configuration support system according to an embodiment of the present disclosure is completed within a wireless device. [Figure 17] FIG. 10 is a diagram illustrating a configuration in which only continuous learning is performed outside the wireless device in an embodiment of the setting support system of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0016] First, an overview of one embodiment will be described. Note that the reference numerals in the drawings attached to this overview are attached to each element as an example for convenience to facilitate understanding, and the description of this overview is not intended to be limiting in any way. In this disclosure, the drawings relate to one or more embodiments.

[0017] 1 is a block diagram showing an example of the configuration of a setting support device according to the present disclosure. The setting support device 10 according to the present disclosure includes a setting problem acquisition unit 11, a device parameter acquisition unit 12, a calculation unit 13, and an output unit 14.

[0018] The setting problem acquisition unit 11 acquires a setting problem that is a problem regarding device settings. The device parameter acquisition unit 12 acquires device parameters including the device settings. The calculation unit 13 calculates corresponding parameters corresponding to the setting problem using a learning model based on the setting problem and the device parameters, and the output unit outputs the corresponding parameters.

[0019] In this way, the setting assistance device disclosed herein accepts from the user a setting problem with the device for which setting assistance is being provided (for example, in the case of a communications device, audio interruptions, noise, etc.), and then acquires device parameters, which are the current setting parameters of the device. Using a learning model based on the setting problem and the device parameters, it is possible to calculate and output corresponding parameters, which are setting parameters that correspond to the setting problem. Therefore, even a user with little knowledge about device settings, such as an end user, can input a setting problem into the device and learn corresponding parameters that will assist in solving the setting problem.

[0020] [First embodiment] [Processing Overview] FIG. 2 is a diagram illustrating an overview of the processing in the setting support device of the present disclosure. As shown in this diagram, there are a setting support device 10, a user 1, and a device 2 to be supported (hereinafter referred to as the target device 2). The target device 2 and the setting support device 10 may be integrated or may be separate devices. The user 1 is the user of the target device 2. The setting support device 10 acquires a setting problem for the target device 2 from the user 1 in natural language. The setting support device 10 also acquires device setting parameters Sp related to the setting problem from the target device 2. The setting support device 10 uses learning models 3-5 to calculate corresponding parameters Sa corresponding to the acquired setting problem. The number of elements of the acquired setting parameters Sp and the calculated corresponding parameters Sa may differ (i.e., n ≠ m in FIG. 2).

[0021] The learning model 3-5 performs learning using corresponding parameters as training signals in advance, and inputs a setting problem in natural language and setting parameters of the device related to the setting problem. Note that the example in Figure 2 shows a schematic representation of a neural network model, and the number of elements in the model (shown by circles) and the mode of coupling between elements (shown by wires between elements) are not limited to those shown in Figure 2.

[0022] [Device configuration] 3 is a block diagram showing an example of the configuration of the setting support device 10 of the present disclosure. As shown in this figure, the setting support device 10 of the present disclosure includes a setting problem acquisition unit 11, a device parameter acquisition unit 12, a calculation unit 13, an output unit 14, and a learning model generation unit 15.

[0023] The configuration problem acquisition unit 11 acquires a configuration problem related to the configuration of the device. In the case of a communication device, a "configuration problem" is a sentence, word, phrase, or the like that indicates an inconvenient phenomenon for the user, such as "audio cuts out," "audio is distorted," or "noise is present." The configuration assistance device 10 of the present disclosure executes the following processes to resolve the configuration problem, and calculates and outputs corresponding parameters as a solution. As described above, this unit may acquire the configuration problem in natural language.

[0024] Furthermore, the hypothesis problem acquisition unit 11 may convert the hypothesis problems written in natural language into vector expressions to create similarities between the hypothesis problems. To generate the vector expressions, Word2Vec or Doc2Vec, which can convert sentences of any length into vectors of a fixed length, may be used.

[0025] The device parameter acquisition unit 12 acquires device parameters including device settings. In Fig. 2, the device parameters indicating the current settings of the target device 2 are acquired. That is, the setting values ​​are acquired and converted appropriately into a form suitable for calculation by the calculation unit 13, which will be described later. Specifically, the actual measured values ​​are subjected to, for example, normalization, and categorical data is converted using, for example, a quantification method such as one-hot encoding.

[0026] The calculation unit 13 calculates corresponding parameters corresponding to the set problem based on the set problem and the device parameters using a learning model. The "learning model" is a model for calculating corresponding parameters learned by the learning model generation unit 15 described below. "Calculation" means inputting the set problem and the device parameter values ​​into the learning model and obtaining the corresponding parameter values ​​that are the calculation results of the learning model.

[0027] As shown in FIG. 2, a user 1 inputs information in a natural language, and a target device 2 inputs the setting parameters Sp and calculates corresponding parameters Sa using a learning model 3-5.

[0028] The output unit 14 outputs the corresponding parameters, and performs processing such as displaying the parameters on a display device such as a display, or transmitting the corresponding parameters to another device via communication.

[0029] The learning model generation unit 15 uses the setting problem and the device parameters as input data, and generates a learning model trained using learning data in which the setting problem and corresponding parameters corresponding to the device parameters are used as training data. While the learning models 3-5 in FIG. 2 are shown in a schematic diagram with a neural network in mind, other supervised or unsupervised models can also be used. While FIG. 2 illustrates three models, Models 3-5, they may also be integrated into a single model. That is, Models 3-5 may be combined from the beginning and used for learning, or Models 3 and 4 may be trained first, and then Model 5 may be combined and used for learning.

[0030] For example, during learning, unsupervised learning may be performed in which text input as learning data is vectorized using the aforementioned Doc2Vec or the vector representation of device parameters is subjected to a dimension reduction algorithm such as principal component analysis to reduce the dimension of the vector. In this case, a principal component with a predetermined cumulative contribution rate may be selected, and the dimension-reduced learning data may be output and used as the input value of Model 5 to train Model 5 anew.

[0031] Furthermore, in the learning, the parameter values ​​themselves may be given as training data, or the training data may indicate which parameters are related. That is, the vector of the corresponding parameter Sa in FIG. 2, which is the calculation result of the calculation unit 13, may take the value of 0 or 1. In this case, the user can know which parameter to change as a setting item, rather than the parameter value.

[0032] [Explanation of operation] 4 is a flowchart for explaining the operation of the setting assistance device 10 of the present disclosure. As shown in this figure, when the setting assistance device 10 starts operation, it accepts and acquires a setting problem from the user (step S41). Next, it acquires device parameters including device settings from the target device (step S42). Based on the setting problem and the device parameters, it calculates corresponding parameters corresponding to the setting problem using a learning model (step S43). Finally, it outputs the calculated corresponding parameters (step S44).

[0033] [Hardware configuration] Next, a description will be given of the hardware configuration of the setting support device according to the first embodiment. Fig. 5 is a block diagram showing an example of the hardware configuration of the setting support device 10 according to the first embodiment.

[0034] The setting support device 10 can be configured by an information processing device (computer) and has the configuration exemplified in Fig. 5. For example, the setting support device 10 includes a CPU (Central Processing Unit) 51, memory 52, an input / output interface 53, and a NIC (Network Interface Card) 54 as a communication means, which are interconnected by an internal bus 55.

[0035] However, the configuration shown in Fig. 5 is not intended to limit the hardware configuration of the devices that make up the setting support device 10. Each setting support device 10 may include hardware not shown, and may not include the input / output interface 53 as necessary. Furthermore, the number of CPUs and the like included in the setting support device 10 is not intended to be limited to the example shown in Fig. 5, and for example, each device may include multiple CPUs.

[0036] The memory 52 is a RAM (Random Access Memory), a ROM (Read Only Memory), or an auxiliary storage device (such as a hard disk).

[0037] The input / output interface 53 is a means for interfacing with a display device and an input device (not shown). The display device is, for example, a liquid crystal display. The input device is, for example, a device that accepts user operations, such as a keyboard or a mouse.

[0038] When calculating corresponding parameters, the functions of the setting support device 10 are realized by processing modules including a setting problem acquisition program, a device parameter acquisition program, a calculation program, an output program, and a group of parameters of a trained learning model stored in the memory 52, etc. When generating a learning model, the functions are realized by a learning model generation program and a dataset consisting of input learning data and teacher data.

[0039] The processing modules are implemented, for example, by the CPU 51 executing a program stored in the memory 52. ​​The program can be updated by downloading it via a network or by using a storage medium storing the program. The processing modules may also be implemented by semiconductor chips. That is, it is sufficient if there is some means for executing the functions performed by the processing modules using some kind of hardware and / or software.

[0040] [Hardware operation: learning] First, a learning model is generated in the setting assistance device 10. A learning model generation program is called from the memory 52 and executed by the CPU 51. The program sequentially reads learning data sets stored in the memory 52 and adjusts the parameters of the learning model using the input learning data and teacher data. The parameters may be adjusted using a loss function such as RSS (Residual Sum of Squares), an existing technology. If the model is a neural network model with multiple layers, a method such as error propagation may be used.

[0041] [Hardware operation: calculation time] After the learning is completed, in the setting assistance device 10, the setting problem acquisition program is called from the memory 52 and is put into an execution state by the CPU 51. The program acquires the setting problem via the input / output interface 53 and the NIC 54. If the setting problem is voice, a voice recognition process is executed here to convert the voice into text. The text setting problem is vectorized using a program of existing technology and temporarily stored in the memory 52. ​​Next, the device parameter acquisition program is called from the memory 52 and is put into an execution state by the CPU 51. The program acquires device parameters, which are setting parameters for the target device. Specifically, the program may send a request to the target device via the NIC 54 and acquire the parameters in response. The acquired parameters are temporarily stored in the memory 52.

[0042] Next, the calculation program is called from memory 52 and is put into execution state by CPU 51. This program reads the trained learning model stored in memory 52. ​​Next, the program executes calculation processing using the set problem (vector) and device parameters (vector) temporarily stored in memory 52 as input. At this time, the input data is subjected to processing such as standardization as appropriate. As a result of the calculation processing, the corresponding parameters (vector) are calculated. Next, the output program is called from memory 52 and is put into execution state by CPU 51. This program outputs the corresponding parameters via NIC 54 or input / output interface 53.

[0043] [Effect description] As described above, the setting assistance device 10 of the present disclosure can acquire setting problems that a user has with a target device, and calculate corresponding parameters for solving the setting problems based on the setting problems and the device parameters of the target device. Therefore, it is possible to provide a setting assistance device, setting assistance system, setting assistance method, and program that enable a user to easily perform optimal settings based on the corresponding parameters output.

[0044] [Second embodiment] In the second embodiment, based on the first embodiment, a setting support device is provided that can further update an already learned learning model based on the determination result of whether or not the corresponding parameters have been reflected in the target device.

[0045] [Device configuration] 6 is a block diagram showing an example of the configuration of the setting support device 10 of the present disclosure. The setting support device 10 of the present disclosure includes a setting problem acquisition unit 11, a device parameter acquisition unit 12, a calculation unit 13, an output unit 14, and a learning model generation unit 15. These components have already been described in the above embodiment, so description thereof will be omitted. The setting support device 10 of this embodiment is characterized in that it further includes a determination unit 16, and that the learning model generation unit updates the learning model in accordance with the determination result of the determination unit 16.

[0046] The judgment unit 16 judges whether the corresponding parameters have been reflected in the device. The corresponding parameters calculated by the learning model and output by the output unit 14 are changed by user operation. Here, the judgment unit 16 compares the calculated and output corresponding parameters with the latest device parameters acquired by the device parameter acquisition unit 12, and judges whether they match in part or in whole. If the judgment result shows a match, it is determined that the corresponding parameters have been reflected in the device parameters of the target device, and sends a trigger to update the learning model to the learning model generation unit 15.

[0047] The learning model generation unit 15 updates the learning model according to the judgment result of the judgment unit 16. Specifically, for example, if it is judged as a result of the judgment that the corresponding parameter has been reflected as a device parameter in the target device, the learning model may be updated based on the corresponding parameter, the setting problem that is the input data from which the corresponding parameter was calculated, and the device parameters. Specifically, so-called online learning may be performed using, for example, the setting problem, the device parameters, and the calculated corresponding parameter. Here, if the weight between elements of the learning model is Wn, the value of the loss function is l, and the learning rate is α, then

number

[0048] [Explanation of operation] 7 is a flowchart for explaining the operation of the setting support device 10 of the present disclosure. Note that the process from the start of operation to the output of the corresponding parameters (steps S71 to S74, not shown) has already been explained in FIG. 4 (steps S41 to S44), so the description will be omitted. When the corresponding parameters are output, it is determined whether the corresponding parameters have been reflected in the device (step S75). If it is determined that the corresponding parameters have been reflected (step S75, Y), the learning model is updated based on the corresponding parameters determined to have been reflected in the device, the setting problem that is the input data from which the corresponding parameters were calculated, and the device parameters (step S76). If it is determined that the corresponding parameters have not been reflected (step S75, N), the series of processes ends.

[0049] [Effect description] The setting support device 10 of the present disclosure can update the learning model based on whether the outputted corresponding parameters are reflected in the device parameters. This makes it possible to generate a learning model that is more optimized for the setting problem, and to output more appropriate corresponding parameters to the user.

[0050] [Third embodiment] In the third embodiment, a setting support system including a setting device 20 and a setting support server device 30 will be described.

[0051] [Processing Configuration] FIG. 8 is a diagram illustrating an overview of the processing of the setting support system of the present disclosure. As shown in this diagram, the setting support system includes a setting device 20 and a setting support server device 30. The device to be set and the setting device 20 may be integrated or separate devices. The setting device 20 includes a setting problem acquisition unit 11, a device parameter acquisition unit 12, an output unit 14, a determination unit 16, and an input data transmission unit 17. The setting support server device 30 includes a calculation unit 13, a learning model generation unit 15, and a corresponding parameter transmission unit 18. The setting support system of the present disclosure is characterized in that the setting device 20 includes the input data transmission unit 17, and the setting support server device 30 includes the corresponding parameter transmission unit 18. Other constituent elements have been described in the above embodiment, so description thereof will be omitted.

[0052] Here, in the setting support system of the present disclosure, a plurality of setting devices may be connected to one setting support server device 30 and may share a learning model.

[0053] The input data transmission unit 17 transmits the setting questions and device parameters acquired by the setting device 20 to the setting assistance server device 30.

[0054] The corresponding parameter transmission unit 18 transmits the corresponding parameters calculated by the setting support server device 30 to the setting device 20 .

[0055] [Explanation of operation] 9 is a flowchart for explaining the operation of the setting support system of the present disclosure. As shown in this figure, the setting device 20 acquires a setting problem (step S91). Next, device parameters are acquired (step S92). The acquired setting problem and device parameters are transmitted (step S93). Thereafter, the setting support server device 30 uses a learning model to calculate corresponding parameters corresponding to the setting problem based on the received setting problem and the device parameters (step S94). The calculated corresponding parameters are transmitted (step S95). The setting device 20 receives this and outputs the corresponding parameters (step S96).

[0056] 10 is a flowchart for explaining the operation of the learning model update process after the corresponding parameters are output in the setting support system of the present disclosure. As shown in this figure, after the corresponding parameters are output in the setting device 20, it is determined whether the corresponding parameters have been reflected in the device (step S1017). If it is determined that the corresponding parameters have been reflected (step S1017, Y), the setting support server device 30 updates the learning model based on the corresponding parameters determined to have been reflected in the device, the setting problem that is the input data from which the corresponding parameters were calculated, and the device parameters (step S1018). If it is determined that the corresponding parameters have not been reflected (step S1017, N), the series of processes ends.

[0057] [Effect description] According to the setting support system of the present disclosure, it is possible to calculate corresponding parameters that correspond to setting problems in cooperation with both the setting device 20 and the setting support server device 30. Since the calculation process of corresponding parameters is centralized in the setting support server device 30, even when the processing capacity of the setting device 20 is limited, such as in a small mobile terminal, the setting device 20 can make an inquiry to the setting support server device, allowing for rapid processing.

[0058] In addition, multiple setting devices 20 can be connected to the setting support server device 30, and by generating and updating a learning model using multiple setting devices 20 and the setting support server device 30, it is possible to provide a more versatile learning model.

[0059] [Example] An embodiment of the setting support system of the present disclosure will be described below. The following is an embodiment of the setting support system in which the setting device is a wireless device.

[0060] [Outline of the Example] FIG. 11 is a diagram illustrating an overview of an embodiment of the configuration support system of the present disclosure. As shown in this diagram, a user inquires of a wireless device (corresponding to a configuration device) that the line is unstable. At this time, the band steering is set to OFF as a device parameter in the wireless device. The wireless device sends the user's inquiry (wireless quality problem) and the device parameters to an analysis server. The analysis server classifies the query using a machine learning model and returns the optimal setting result to the wireless device. The wireless device presents the optimal setting, with band steering set to ON, to the user.

[0061] [Configuration of the Example] 12 is a diagram showing an example of the configuration of an embodiment of a setting support system according to the present disclosure. As shown in this diagram, the setting support function of the setting support system according to the present invention is mainly realized by a wireless device 101 and a machine learning model in an analysis server 201. Depending on the capacity of the machine learning model, it may be possible to operate it by incorporating it into the wireless device 101.

[0062] There are six functions related to the invention in the wireless device 101, numbered 102 to 107. The natural language input function 102 accepts wireless quality problems from users in natural language format and transmits them to the analysis server 201 for classification. Note that if the user inputs text using a screen, it is transmitted directly to the analysis server 201, and if the user inputs voice, it is converted from voice waveform to text format and then transmitted to the analysis server 201 in text format.

[0063] The device parameter acquisition function 103 acquires the parameters of the wireless device (radio field strength, bandwidth, interference information, device settings, etc.) This acquired data is also sent to the analysis server 201 for classification.

[0064] The setting suggestion function 104 presents to the user the contents of the optimal settings classified by the machine learning model and the wireless quality problems that will be solved by those settings.

[0065] The setting selection / complementary input function 105 allows the user to select whether the presented setting contents should be reflected in the device, and allows the user to input a small amount of complementary information (such as a password) as necessary.

[0066] The setting reflection function 106 reflects the optimum settings classified based on the received supplementary items.

[0067] The wireless communication quality acquisition function 107 has a function to acquire the current wireless communication quality (communication stability, delay, packet loss, etc.) and the wireless quality before the settings were reflected from the past communication quality log. The acquired wireless communication quality is then sent to the continuous learning function 207 of the analysis server 201.

[0068] The functions related to the invention in the analysis server 201 are mainly the machine learning model 202 and the continuous learning function 207.

[0069] The internal structure of the machine learning model 202 is divided into four components, 203 to 206. The natural language processing module 203 accepts users' wireless quality problems in natural language format, interprets their meaning, and encodes them into a format that is easy for machines to use. The equipment parameter encoding module 204 encodes equipment parameters into a format that is easy for machines to use, such as numbers or vectors (one-hot encoding for categories, scaling for numbers, etc.). Note that it is necessary to decide in advance which equipment parameters to use. The representation integration module 205 inputs the outputs of the natural language processing module 203 and the equipment parameter encoding module 204 and integrates the two features.

[0070] The classification module 206 receives the output of the integration module 205 for both representations and performs classification. Multiple classification is also performed by applying a softmax function to the fully connected layer. The number of classification targets must be determined in advance during the learning stage, and the format of the objective variable (O1 in Figure 13) must also match this. Note that when the term "input / output of the machine learning model" is used in this document, it refers to the input / output of the machine learning model 202.

[0071] The continuous learning function 207 feeds back the results of the user's use of the present invention and uses them for continuous learning.

[0072] FIG. 13 is a diagram showing the learning data set in the configuration support system of the present disclosure. As shown in this figure, supervised learning is performed using two explanatory variables, the user's wireless quality problem E1 and the device parameter E2, and the optimal wireless device setting O1 as the objective variable. The configuration and correlation between them are described below.

[0073] The user's wireless quality problem E1 is part of the input data for the machine learning model of the present invention and indicates the problems and improvement requests the user has regarding wireless communication quality. These are accepted in natural language format and input to the model as explanatory variables. The role of these variables is to enable the model to classify and propose settings that correspond to the user's specific quality improvement requests.

[0074] The device parameters E2 represent information about the wireless device (such as signal strength, bandwidth, and device settings). These parameters are acquired by the wireless device and used as input data for the machine learning model. The role of the device parameters E2 is to enable the model to classify and propose appropriate settings taking into account the current state of the wireless device. This allows the model to present optimal settings tailored to each individual user's environment.

[0075] The optimal wireless equipment setting O1 is the objective variable of the machine learning model and represents the optimal setting for the wireless equipment to be selected based on the user's wireless quality problem E1 and the equipment parameters E2. The machine learning model classifies and predicts the optimal setting from these explanatory variables and presents it to the user. Continuous learning is performed taking into account whether the user has reflected the presented setting and the change in wireless quality due to the reflection of the setting, but the basic configuration is the same.

[0076] Since there is a correlation between the explanatory variables and the objective variable, this will be explained. Regarding the correlation between the user's wireless quality problem E1 and the optimal wireless device setting O1, the wireless quality problem that the user experiences (such as a decrease in communication speed or a decrease in connection stability) directly affects the selection of the optimal wireless device setting. Since a setting is required to address a specific problem, it is considered that there is a correlation between the user's wireless quality problem E1 and the optimal setting O1.

[0077] Regarding the correlation between the device parameter E2 and the optimal wireless device setting O1, device parameters (such as signal strength, bandwidth, and current settings) that reflect the current state of the wireless device also affect the determination of the optimal wireless device setting. Because it is necessary to select appropriate settings that adapt to the environment, it is thought that there is also a correlation between the device parameter E2 and the optimal setting O1.

[0078] These correlations allow the machine learning model to predict and estimate optimal wireless device settings from explanatory variables (user wireless quality issues and device parameters). As long as the training dataset maintains the correlations, the model can make accurate and effective configuration suggestions.

[0079] [Explanation of the operation of the embodiment] The overall flow of the function will be described using Figures 12 and 14. Figure 14 is a sequence diagram for explaining the operation of an embodiment of the setting support system of the present disclosure. First, the wireless device uses the natural language input function 102 to acquire wireless quality problems that the user is experiencing (step S1).

[0080] At the same time, the device parameter acquisition function 103 acquires the determined settings, status, etc. within the device (step S2).

[0081] Once the acquisition is complete, the acquired data is sent to the analysis server 201. The acquired data is then input to the natural language processing module 203 and the device parameter encoding module 204, and the optimal settings are classified using the machine learning model 202 (step S3). The classification results are used by the setting presentation function 104, which presents a summary of the corresponding settings to the user. Note that because the classification results only indicate the optimal settings, the corresponding setting summary and the like must be prepared in advance in the wireless device or the analysis server and presented using the setting presentation function 104.

[0082] After the settings are presented, the setting selection / completion input function 105 is used to ask the user whether or not to reflect the settings (step S4).

[0083] If the user selects to reflect the presented settings, the user is prompted to input necessary information such as a password, and the settings are then made using the setting reflection function 106 (step S5).

[0084] Finally, the wireless communication quality before and after the setting is reflected and whether the proposed setting has been reflected are sent as feedback to the analysis server 201 (step S6). Past communication quality is recorded in a history log or the like, so that the wireless communication quality after the setting is reflected can be obtained. In this way, appropriate settings are presented to the user based on the user's wireless quality problem and device parameters.

[0085] Next, continuous learning will be described with reference to FIG. 15. FIG. 15 is a flowchart for explaining the operation of continuous learning in an embodiment of the setting support system of the present disclosure. Continuous learning is a mechanism for maintaining or improving accuracy by re-learning data newly obtained by some function. In the present invention, when a user applies the presented optimal settings to a wireless device, a learning data set including the results is used for continuous learning (step S1501).

[0086] Next, based on the value acquired by the wireless communication quality acquisition function 107, an improvement value between 0 and 1 is calculated as the result of improvement of wireless communication quality. This is calculated for each element (delay, packet loss, etc.) representing wireless communication quality (step S1502). If the improvement value is negative, it is set to 0, and the calculation of the improvement value is determined according to each element representing wireless quality. For example, if the element ping [ms] representing delay decreases from 100 to 90, the improvement value for the latter decreases from 30 to 20 is set to be higher, and the improvement value is calculated according to the characteristics of the element.

[0087] Next, the average of the calculated multiple improvement values ​​(two values ​​if only delay and packet loss are considered) is calculated, and this score is used as the weight of the corresponding learning data set during continuous learning (step S1503). This enables the machine learning model to focus on factors that have a significant impact on wireless communication quality during learning.

[0088] Finally, the user's wireless quality problem and device parameters are used as explanatory variables, the presented optimal settings as objective variables, and the calculated weights are used for continuous learning (step S1504). A specific example is presented below. Consider a case where the user input is "wireless instability," and the output is two types, "band steering" and "change communication mode (such as turning on IEEE802.11ax settings)," with values ​​of 0.55 and 0.45, respectively, and the band steering function is presented and reflected. If, after learning with a weight of 0.1, the output is 0.6 and 0.4, then with a weight of 0.4, the output will be 0.7 and 0.3, which is a higher probability of presenting the "band steering function."

[0089] [Effect description] The configuration support system according to the embodiment of the present disclosure can present appropriate settings to users without wireless knowledge by presenting settings based on machine learning models based on input about wireless quality issues. Furthermore, by using device parameters for machine learning predictions, it is possible to address individual user issues.

[0090] [Another Example 1] In the present invention, the results obtained by the wireless device are used on the analysis server, but depending on the capacity of the entire machine learning model, it is also possible to complete the process using only the wireless device (Figure 16), or to select to perform only the learning in a separate environment (Figure 17).

[0091] When the process is completed using only the wireless device, the machine learning model and continuous learning are embedded in the wireless device without using an analysis server, as shown in Figure 16. In this case, it is necessary to reduce the performance of the natural language module, reduce the number of settings to be classified, and reduce the number of internal parameters of the entire machine learning model. Furthermore, since learning requires a large amount of computational resources, it is necessary to turn off other functions during non-use periods and to perform learning, or to prepare wireless devices with high computational processing capabilities in the first place. Furthermore, since there is a lack of training data, it is difficult to improve accuracy through continuous learning.

[0092] When classification is performed within the wireless device and learning is performed in a separate environment, the machine learning model is embedded in the wireless device as shown in Figure 17, and the continuous learning function is used on the analysis server. In this case, although not as important as the above, attention must be paid to capacity and computational resources. Furthermore, updates to the machine learning model 202 after continuous learning can be incorporated into a firmware update.

[0093] [Another Example 2] In the present invention, when a user inputs a wireless quality problem, a corresponding setting is presented. If the input is in natural language that has nothing to do with wireless quality, a mechanism that can respond appropriately will be described as another embodiment 2.

[0094] The training dataset is constructed so that the machine learning model can respond appropriately even when the user enters natural language input that is unrelated to wireless quality issues. Specifically, the training dataset is supplemented with natural language input that is unrelated to wireless quality (e.g., weather information or general conversations) and the classification result in that case, "inappropriate settings." By including such data in the training dataset, the machine learning model acquires the ability to respond appropriately to input that is unrelated to wireless quality issues and determine that "inappropriate settings are not appropriate." As a result, when the user enters unrelated content, the model does not make setting suggestions, preventing unrelated setting changes.

[0095] We will now explain the correlation between "inappropriate settings" and "wireless quality problems." It can be said that a certain correlation exists between "inappropriate settings" and "wireless quality problems." However, this correlation does not indicate a relationship in which the two directly affect each other, but rather an inverse relationship. Specifically, the classification result of "inappropriate settings" is applied to natural language input that is unrelated to wireless quality problems. In other words, when "inappropriate settings" is selected, it indicates that the presence of wireless quality problems is low or not related at all. Therefore, "inappropriate settings" and the presence of wireless quality problems have an inverse relationship with each other.

[0096] A part or all of the above disclosure may also be described as follows, but is not limited to the following. [Appendix 1] This is the same as the setting support device according to the first aspect described above. [Appendix 2] Preferably, the setting support device according to claim 1 further comprises a learning model generation unit that generates a learning model trained using learning data in which a setting problem and device parameters are used as input data and corresponding parameters corresponding to the setting problem and the device parameters are used as training data, and the calculation unit calculates the corresponding parameters using the learning model. [Appendix 3] Preferably, the setting support device according to claim 1 or 2, wherein the setting question acquisition unit acquires setting questions in natural language. [Appendix 4] The setting support device of Appendix 3, which is dependent on Appendix 2, preferably comprises the setting problem acquisition unit vectorizing the setting problem in natural language, and the learning model generation unit generating the setting problem as a vectorized setting problem. [Appendix 5] Preferably, the setting support device of Appendix 2 further comprises a judgment unit that judges whether the corresponding parameters have been reflected in the device, and the learning model generation unit updates the learning model according to the judgment result of the judgment unit. [Appendix 6] Preferably, the learning model generation unit updates the learning model based on the corresponding parameters determined to be reflected in the device, the setting problem which is the input data for calculating the corresponding parameters, and the device parameters. [Appendix 7] This is the same as the setting support system according to the second aspect described above. [Appendix 8] The setting support system of Appendix 7, wherein the setting support server device further has a learning model generation unit that uses a setting problem and device parameters as input data and generates a learning model trained using learning data that uses corresponding parameters corresponding to the setting problem as teacher data, and the calculation unit calculates the corresponding parameters using the learning model. [Appendix 9] This is the same as the setting support method according to the third aspect described above. [Appendix 10] This is the same as the program related to the fourth perspective mentioned above. [Appendix 11] The setting support device of Appendix 3, wherein the learning model calculation unit further uses a setting problem unrelated to the device parameters and corresponding parameters as input data and generates a learning model trained using learning data with an output indicating that there is no appropriate setting as training data. Note that Supplements 9 and 10 can be expanded into Supplements 2 to 6, just like Supplement 1.

[0097] The disclosures of the above-cited patent documents and other documents are incorporated herein by reference. Modifications and adjustments of the embodiments and examples are possible within the scope of the entire disclosure of the present invention (including the scope of the claims), and further based on the basic technical concept thereof. Furthermore, various combinations and selections of the various disclosed elements (including each element of each claim, each element of each embodiment or example, each element of each drawing, etc.) are possible within the scope of the entire disclosure of the present invention. In other words, the present invention naturally includes various modifications and alterations that would be possible by a person skilled in the art in accordance with the entire disclosure and technical concept, including the scope of the claims. In particular, with regard to the numerical ranges set forth herein, any numerical value or subrange included within the range should be construed as being specifically set forth, even if not otherwise specified. [Explanation of symbols]

[0098] 1: User 2: Target device 3-5: Model, learning model 10: Setting support device 11:Setting problem acquisition part 12: Device parameter acquisition unit 13: Calculation section 14: Output section 15: Learning model generation unit 16: Judgment department 17: Input data transmission unit 18: Corresponding parameter transmission unit 20: Setting device 30: Setting support server device 51: CPU 52: Memory 53: Input / output interface 54:NIC 55: Internal bus 101: Radio equipment 102: Natural language input function 103: Device parameter acquisition function 104: Setting suggestion function 105: Setting selection and input completion function 106: Setting reflection function 107: Wireless communication quality acquisition function 201: Analysis Server 202: Machine Learning Models 203: Natural Language Processing Module 204: Device parameter encoding module 205: Integration module for both representations 206: Classification module 207:Continuous learning function E1: Wireless quality problem E2: Equipment parameters O1: Wireless device settings

Claims

1. A setting problem acquisition unit that acquires setting problems that are problems related to the settings of a communication device, which indicate events that are inconvenient for a user; a device parameter acquisition unit that acquires device parameters including settings of the communication device; a calculation unit that calculates corresponding parameters corresponding to the set problem based on the set problem and the device parameters using a learning model; an output unit that outputs the corresponding parameters; A setting support device having the above configuration.

2. The method further comprises a learning model generation unit that generates a learning model trained using learning data in which the setting problem and device parameters are used as input data, and corresponding parameters corresponding to the setting problem and the device parameters are used as training data; the calculation unit calculates the corresponding parameters using the learning model; The setting support device according to claim 1.

3. The setting support device according to claim 1 or 2, wherein the setting question acquisition unit acquires the setting questions in natural language.

4. The set problem acquisition unit acquires the set problem in natural language, the set problem acquisition unit vectorizes the set problem written in the natural language; The learning model generation unit is configured such that the set problem is a vectorized set problem. The setting support device according to claim 2.

5. a determination unit that determines whether the corresponding parameter has been reflected in the communication device; the learning model generation unit updates the learning model in accordance with the determination result of the determination unit. The setting support device according to claim 2.

6. the learning model generation unit updates the learning model based on the corresponding parameters determined to be reflected in the communication device, the set problem that is input data for calculating the corresponding parameters, and device parameters; The setting support device according to claim 5.

7. A setting support system including a setting device and a setting support server device, The setting device includes: a setting problem acquisition unit that acquires a setting problem, which is a problem related to the setting of a communication device, and which indicates an event that is inconvenient for a user; an apparatus parameter acquisition unit that acquires apparatus parameters including settings of the setting apparatus; an input data transmitting unit that transmits the setting problem and the device parameters; and The setting support server device a calculation unit that calculates corresponding parameters corresponding to the set problem based on the set problem and the device parameters using a learning model; a corresponding parameter transmitting unit that transmits the corresponding parameters; The setting device further includes: an output unit that outputs the corresponding parameters; A setting support system having the above.

8. The setting support server device The method further includes a learning model generation unit that generates a learning model trained using learning data in which the set problem and device parameters are used as input data and corresponding parameters corresponding to the set problem are used as training data, the calculation unit calculates the corresponding parameters using the learning model; The setting support system according to claim 7.

9. A setting support method for executing the following steps by a computer, acquiring a setting problem, which is a problem related to the setting of a communication device, that indicates an event that is inconvenient for a user; obtaining device parameters including settings for the communication device; calculating corresponding parameters corresponding to the set problem based on the set problem and the device parameters using a learning model; outputting the corresponding parameters; A setting support method having the above.

10. A process for acquiring a setting problem, which is a problem related to the setting of a communication device, which indicates an event that is inconvenient for a user; obtaining device parameters including settings for the communication device; a process of calculating corresponding parameters corresponding to the set problem based on the set problem and the device parameters using a learning model; a process of outputting the corresponding parameters; A program that causes a computer to execute the following.

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