system

Abstract

The system according to the embodiment aims to communicate customer usage status and complaints to new employees in an easy-to-understand manner. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects customer usage status or complaints. The analysis unit analyzes the information collected by the collection unit. The generation unit creates a cue card based on the information analyzed by the analysis unit. The provision unit provides the cue card created by the generation unit to new employees.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology lacks a means to effectively communicate customer usage status and complaints to new employees, and there is room for improvement.

[0005] The system according to the embodiment aims to communicate customer usage status and complaints to new employees in an easy-to-understand manner. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects customer usage status or complaints. The analysis unit analyzes the information collected by the collection unit. The generation unit creates a cue card based on the information analyzed by the analysis unit. The provision unit provides the cue card created by the generation unit to new employees. [Effects of the Invention]

[0007] The system according to the embodiment can clearly communicate customer usage status and complaints to new employees. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention is a system for creating cue cards for mobile proposal methods. This system collects customer usage data and complaints, and a generation AI creates cue cards for new employees, making it easier for new employees to understand the customer's situation. For example, when collecting customer usage data and complaints, detailed data is collected, such as the types of services the customers use and the points they are dissatisfied with. Next, based on the collected information, the generation AI creates cue cards for new employees. The generation AI analyzes the collected data and creates cue cards in a format that is easy for new employees to understand. For example, the generation AI organizes the customer's usage data and complaints in bullet points and writes proposals in response to them. The created cue cards can be used as a reference when new employees make proposals to customers. This makes it easier for new employees to understand the customer's usage data and complaints and make appropriate proposals. For example, if a customer feels that an app is running slowly, the system can make proposals to resolve the issue. This system makes it easier for new employees to understand the customer's usage data and complaints and make appropriate proposals. This is expected to improve customer satisfaction and promote service usage. The system makes it easier for new employees to understand the customer's usage data and complaints and make appropriate proposals.

[0029] A cue card generation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects customer usage status or complaints. The collection unit can collect customer usage status or complaints using, for example, a questionnaire or a feedback form. The collection unit can also analyze customers' social media activities and collect related information. The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the collected information using, for example, natural language processing or machine learning. The generation unit creates a cue card based on the information analyzed by the analysis unit. The generation unit can organize customer usage status and complaints in bullet points or a flowchart format to create a cue card for new employees. The provision unit provides the cue card created by the generation unit to new employees. The provision unit can update the cue card based on, for example, periodically collected data. The provision unit can also adjust the format and content of the cue card to make it easier for new employees to use. As a result, the cue card generation system according to an embodiment collects and analyzes customer usage status and complaints, creates a cue card, and provides it to new employees, making it easier for new employees to understand the customer's situation.

[0030] The collection unit can collect customer usage status or complaints using a questionnaire or feedback form. The collection unit collects customer usage status or complaints using, for example, a questionnaire. For example, the questionnaire includes questions asking what kind of service the customer is using and what aspects the customer is dissatisfied with. The collection unit can also collect customer usage status or complaints using a feedback form. For example, the feedback form has a section where the customer can write down any complaints or areas for improvement they have about the service. In this way, customer usage status and complaints can be effectively collected by using the questionnaire or feedback form.

[0031] The analysis unit can analyze the collected information using natural language processing or machine learning. The analysis unit analyzes the collected information using, for example, natural language processing technology. For example, the analysis unit divides text data using morphological analysis and analyzes the meaning of each word. The analysis unit can also analyze sentence structure using grammatical analysis to understand the relevance of information. Furthermore, the analysis unit can analyze the collected information using a machine learning algorithm. For example, the analysis unit can group data using K-means clustering to identify patterns of customer usage and dissatisfaction. This allows the collected information to be analyzed with high accuracy using natural language processing or machine learning.

[0032] The generation unit can organize customer usage status or complaints in bullet points or flow chart format to create a cue card for new employees. The generation unit, for example, organizes customer usage status and complaints in bullet points. For example, the generation unit creates a cue card in a format that is easy for new employees to understand by bullet pointing out services that customers frequently use and complaints they have. The generation unit can also organize customer usage status and complaints in flowchart format. For example, the generation unit organizes customer usage status and complaints in a visually easy-to-understand manner in a flowchart so that new employees can refer to it when making proposals. In this way, customer usage status and complaints can be clearly organized to create a cue card that is easy for new employees to understand.

[0033] The providing unit can update the cue card based on data collected periodically. The providing unit updates the cue card based on data collected weekly, for example. For example, the providing unit analyzes the results of weekly data collection and reflects new information and changes in the cue card. The providing unit can also update the cue card based on data collected monthly. For example, the providing unit reviews the contents of the cue card based on the results of monthly data collection and reflects the latest information. In this way, by regularly updating the cue card, it is possible to provide a cue card that reflects the latest information.

[0034] The providing unit can adjust the format or content of the cue card to make it easier for newcomers to use the cue card. For example, the providing unit adjusts the font size of the cue card. For example, the providing unit increases the font size so that it is easier for newcomers to read. The providing unit can also adjust the color of the cue card. For example, the providing unit uses a specific color to highlight important information. Furthermore, the providing unit can adjust the level of detail of the information on the cue card. For example, the providing unit summarizes the information succinctly so that it is easier for newcomers to understand. This makes it easier for newcomers to use the cue card, thereby improving the quality of suggestions.

[0035] The collection unit can analyze the customer's past feedback history and select the optimal collection method. The collection unit, for example, optimizes the questionnaire format based on feedback provided by the customer in the past. For example, the collection unit analyzes the past feedback history and selects a questionnaire format that is easy for the customer to respond to. The collection unit can also prioritize collection of specific questions from the customer's past feedback history. For example, the collection unit prioritizes collection of questions that the customer is interested in based on the past feedback history. The collection unit can also analyze the customer's feedback history and select the most effective collection method (email, telephone, etc.). For example, the collection unit selects the collection method that is easiest for the customer to respond to based on the past feedback history. In this way, the optimal collection method can be selected by analyzing the past feedback history.

[0036] The collection unit can filter the data based on the customer's current usage status and areas of interest when collecting the data. For example, the collection unit prioritizes collecting information related to services currently being used by the customer. For example, the collection unit filters and collects information related to services frequently used by the customer. The collection unit can also filter and collect related information based on the customer's areas of interest. For example, the collection unit prioritizes collecting information related to topics in which the customer is interested. Furthermore, the collection unit can monitor the customer's usage status in real time and dynamically filter and collect related information. For example, the collection unit monitors the customer's usage status in real time and prioritizes collecting information related to frequently used services. In this way, highly relevant information can be collected by filtering the information based on the customer's usage status and areas of interest.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the customer. For example, if the customer is in a specific area, the collection unit prioritizes collecting information related to that area. For example, the collection unit collects information related to services that the customer uses in a specific area. The collection unit can also collect area-specific problems based on the customer's geographical location information. For example, the collection unit collects information related to problems occurring in a specific area. Furthermore, if the customer is traveling, the collection unit can prioritize collecting information related to the customer's destination. For example, the collection unit collects information related to services that the customer may use at the customer's destination. In this way, area-specific problems can be effectively collected by taking into account the customer's geographical location information.

[0038] At the time of collection, the collection unit can analyze the social media activities of customers and collect related information. The collection unit, for example, collects complaints posted by customers on social media. For example, the collection unit collects information related to complaints posted by customers on social media. The collection unit can also collect topics of interest from the social media activities of customers. For example, the collection unit collects related data based on information shared by customers on social media. The collection unit can also collect related data based on information shared by customers on social media. For example, the collection unit collects related data based on information shared by customers on social media. In this way, topics of interest can be effectively collected by analyzing the social media activities of customers.

[0039] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the customer's past feedback history. The analysis unit, for example, optimizes the analysis algorithm based on the customer's past feedback history. For example, the analysis unit analyzes the past feedback history and prioritizes analysis of data that the customer is interested in. The analysis unit can also extract specific patterns from the customer's past feedback history and reflect them in the analysis. For example, the analysis unit extracts patterns that the customer is interested in based on the past feedback history and reflects them in the analysis. Furthermore, the analysis unit can also improve the accuracy of the analysis results by referring to the customer's feedback history. For example, the analysis unit improves the accuracy of the analysis results based on the past feedback history. In this way, the accuracy of the analysis can be improved by referring to the past feedback history.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the customer's usage status and the category of dissatisfaction. The analysis unit selects the optimal analysis algorithm depending on, for example, the customer's usage status. For example, the analysis unit applies a specific analysis algorithm to analyze data related to a service that the customer frequently uses. The analysis unit can also apply a specific analysis algorithm based on the category of customer dissatisfaction. For example, the analysis unit applies a specific analysis algorithm to analyze data related to a service that the customer is dissatisfied with. The analysis unit can also combine the customer's usage status and the category of dissatisfaction to apply the optimal analysis algorithm. For example, the analysis unit selects the optimal analysis algorithm based on the customer's usage status and the category of dissatisfaction. In this way, by applying the optimal analysis algorithm depending on the usage status and the category of dissatisfaction, the accuracy of the analysis can be improved.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the geographical location information of the customer. For example, if the customer is in a specific region, the analysis unit prioritizes analyzing data related to that region. For example, the analysis unit analyzes data related to services used by the customer in a specific region. The analysis unit can also analyze region-specific issues based on the customer's geographical location information. For example, the analysis unit analyzes data related to issues occurring in a specific region. Furthermore, if the customer is traveling, the analysis unit can prioritize analyzing data related to the customer's destination. For example, the analysis unit analyzes data related to services the customer may use at the destination. In this way, region-specific issues can be effectively analyzed by taking into account the geographical location information.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the customer's social media activity. The analysis unit, for example, analyzes complaints posted by customers on social media. For example, the analysis unit analyzes data related to complaints posted by customers on social media. The analysis unit can also analyze topics of interest from the customer's social media activity. For example, the analysis unit analyzes related data based on information shared by customers on social media. The analysis unit can also analyze related data based on information shared by customers on social media. For example, the analysis unit analyzes related data based on information shared by customers on social media. This makes it possible to effectively analyze topics of interest by referring to social media activity.

[0043] The generation unit can adjust the level of detail of the cue card based on the customer's usage status and the importance of the dissatisfaction when generating the cue card. For example, if the customer's dissatisfaction is serious, the generation unit generates a cue card including a detailed solution. For example, if the customer has serious dissatisfaction, the generation unit creates a cue card including a detailed solution for resolving the dissatisfaction. The generation unit can also generate a cue card including a detailed explanation when the customer's usage status is complex. For example, if the customer has a complex usage status, the generation unit creates a cue card including a detailed explanation for understanding the situation. Furthermore, the generation unit can also generate a concise cue card when the customer's dissatisfaction is minor. For example, if the customer has minor dissatisfaction, the generation unit creates a concise cue card for resolving the dissatisfaction. In this way, by adjusting the level of detail of the cue card based on the usage status and the importance of the dissatisfaction, it is possible to provide a cue card with an appropriate amount of information.

[0044] The generation unit can apply different generation algorithms depending on the customer's usage status and the category of dissatisfaction when generating the cue card. The generation unit selects the optimal generation algorithm depending on, for example, the customer's usage status. For example, the generation unit applies a specific generation algorithm based on data related to services frequently used by the customer. The generation unit can also apply a specific generation algorithm based on the category of dissatisfaction of the customer. For example, the generation unit applies a specific generation algorithm based on data related to services about which the customer is dissatisfied. The generation unit can also apply the optimal generation algorithm by combining the customer's usage status and the category of dissatisfaction. For example, the generation unit selects the optimal generation algorithm based on the customer's usage status and the category of dissatisfaction. In this way, the accuracy of the cue card can be improved by applying the optimal generation algorithm depending on the usage status and the category of dissatisfaction.

[0045] At the time of generation, the generation unit can determine the priority of cue cards based on the timing of customer feedback submission. For example, if a customer has recently submitted feedback, the generation unit preferentially generates cue cards based on that feedback. For example, the generation unit preferentially creates cue cards based on feedback recently submitted by the customer. The generation unit can also determine the priority of cue cards based on feedback previously submitted by the customer. For example, the generation unit determines the priority of cue cards based on feedback previously submitted by the customer. Furthermore, the generation unit can generate cue cards at the optimal timing, taking into account the timing of customer feedback submission. For example, the generation unit creates cue cards at the optimal timing based on the timing of customer feedback submission. In this way, by determining the priority of cue cards based on the timing of feedback submission, it is possible to provide timely cue cards.

[0046] The generation unit can adjust the order of the cue cards at the time of generation based on the relevance of the customer's usage status and dissatisfaction. For example, the generation unit prioritizes writing the most relevant information on the cue cards based on the customer's usage status. For example, the generation unit prioritizes writing information related to services that the customer uses frequently on the cue cards. The generation unit can also adjust the order of the cue cards taking into account the relevance of the customer's dissatisfaction. For example, the generation unit prioritizes writing information related to services that the customer is dissatisfied with on the cue cards. Furthermore, the generation unit can combine the relevance of the customer's usage status and dissatisfaction to generate the cue cards in an optimal order. For example, the generation unit creates the cue cards in an optimal order based on the relevance of the customer's usage status and dissatisfaction. In this way, by adjusting the order of the cue cards based on the relevance of the usage status and dissatisfaction, an effective cue card can be provided.

[0047] When providing the information, the providing unit can select the optimal providing method by referring to the new employee's past usage history. The providing unit selects the optimal providing method, for example, based on the format of cue cards used by the new employee in the past. For example, the providing unit selects the optimal providing method based on the format of cue cards used by the new employee in the past. The providing unit can also preferentially select a specific providing method based on the new employee's past usage history. For example, the providing unit selects the optimal providing method based on the content of cue cards used by the new employee in the past. Furthermore, the providing unit can also select the optimal providing method based on the new employee's past usage history. For example, the providing unit selects the optimal providing method based on the content of cue cards used by the new employee in the past. In this way, the optimal providing method can be selected by referring to the new employee's past usage history.

[0048] The providing unit can customize the content of the cue card according to the new employee's level of understanding when providing it. For example, if the new employee has a high level of understanding, the providing unit provides a cue card from which detailed explanations are omitted. For example, if the new employee has a high level of understanding, the providing unit provides a cue card from which detailed explanations are omitted. Furthermore, if the new employee has a low level of understanding, the providing unit can also provide a cue card including detailed explanations. For example, if the new employee has a low level of understanding, the providing unit provides a cue card including detailed explanations. Furthermore, the providing unit can dynamically customize the content of the cue card according to the new employee's level of understanding. For example, the providing unit dynamically customizes the content of the cue card according to the new employee's level of understanding. In this way, by customizing the content of the cue card according to the new employee's level of understanding, an effective cue card can be provided.

[0049] The providing unit can select the optimal providing method by taking into consideration the geographical location information of the newcomer when providing the information. For example, if the newcomer is in a specific area, the providing unit provides a cue card including information related to the area. For example, if the newcomer is in a specific area, the providing unit provides a cue card including information related to the area. The providing unit can also provide a cue card including problems specific to the area based on the geographical location information of the newcomer. For example, if the newcomer is in a specific area, the providing unit provides a cue card including information related to the area. Furthermore, if the newcomer is traveling, the providing unit can also provide a cue card including information related to the destination. For example, if the newcomer is traveling, the providing unit provides a cue card including information related to the destination. In this way, by taking into consideration the geographical location information of the newcomer, problems specific to the area can be effectively provided.

[0050] The provision unit can adjust the method of providing the cue card by analyzing the newcomer's social media activity at the time of provision. The provision unit, for example, adjusts the method of providing the cue card based on information posted by the newcomer on social media. For example, the provision unit adjusts the method of providing the cue card based on information posted by the newcomer on social media. The provision unit can also provide a cue card including topics of interest from the newcomer's social media activity. For example, the provision unit provides a cue card including information related to topics in which the newcomer has shown interest on social media. The provision unit can also adjust the method of providing the cue card based on information shared by the newcomer on social media. For example, the provision unit adjusts the method of providing the cue card based on information shared by the newcomer on social media. In this way, topics of interest can be effectively provided by analyzing the newcomer's social media activity.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The provision unit can select the optimal provision method by referring to the new employee's past usage history. For example, the provision unit selects the optimal provision method based on the format of cue cards used by the new employee in the past. For example, the provision unit selects the optimal provision method based on the format of cue cards used by the new employee in the past. The provision unit can also preferentially select a specific provision method based on the new employee's past usage history. For example, the provision unit selects the optimal provision method based on the content of cue cards used by the new employee in the past. The provision unit can also select the optimal provision method based on the new employee's past usage history. For example, the provision unit selects the optimal provision method based on the content of cue cards used by the new employee in the past. In this way, the optimal provision method can be selected by referring to the new employee's past usage history.

[0053] The collection unit can analyze the customer's past feedback history and select the optimal collection method. For example, the collection unit optimizes the questionnaire format based on feedback provided by the customer in the past. For example, the collection unit analyzes the past feedback history and selects a questionnaire format that is easy for the customer to respond to. The collection unit can also prioritize collection of specific questions from the customer's past feedback history. For example, the collection unit prioritizes collection of questions that the customer is interested in based on the past feedback history. Furthermore, the collection unit can analyze the customer's feedback history and select the most effective collection method (email, telephone, etc.). For example, the collection unit selects the collection method that is easiest for the customer to respond to based on the past feedback history. In this way, the optimal collection method can be selected by analyzing the past feedback history.

[0054] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the customer's past feedback history. For example, the analysis unit optimizes the analysis algorithm based on the customer's past feedback history. For example, the analysis unit analyzes the past feedback history and prioritizes analysis of data that the customer is interested in. The analysis unit can also extract specific patterns from the customer's past feedback history and reflect them in the analysis. For example, the analysis unit extracts patterns that the customer is interested in based on the past feedback history and reflects them in the analysis. Furthermore, the analysis unit can also improve the accuracy of the analysis results by referring to the customer's feedback history. For example, the analysis unit improves the accuracy of the analysis results based on the past feedback history. In this way, the accuracy of the analysis can be improved by referring to the past feedback history.

[0055] The generation unit can adjust the level of detail of the cue card based on the customer's usage status and the importance of the dissatisfaction when generating the cue card. For example, if the customer's dissatisfaction is serious, the generation unit generates a cue card including detailed solutions. For example, if the customer has serious dissatisfaction, the generation unit creates a cue card including detailed solutions for resolving the dissatisfaction. The generation unit can also generate a cue card including detailed explanations when the customer's usage status is complex. For example, if the customer has a complex usage situation, the generation unit creates a cue card including detailed explanations for understanding the situation. Furthermore, the generation unit can also generate a concise cue card when the customer's dissatisfaction is minor. For example, if the customer has minor dissatisfaction, the generation unit creates a concise cue card for resolving the dissatisfaction. In this way, by adjusting the level of detail of the cue card based on the usage status and the importance of the dissatisfaction, it is possible to provide a cue card with an appropriate amount of information.

[0056] The providing unit can select the optimal providing method by taking into consideration the geographical location information of the newcomer when providing the information. For example, if the newcomer is in a specific area, the providing unit can provide a cue card including information related to the area. For example, if the newcomer is in a specific area, the providing unit can provide a cue card including information related to the area. The providing unit can also provide a cue card including problems specific to the area based on the geographical location information of the newcomer. For example, if the newcomer is in a specific area, the providing unit can provide a cue card including information related to the area. Furthermore, if the newcomer is traveling, the providing unit can also provide a cue card including information related to the destination. For example, if the newcomer is traveling, the providing unit can provide a cue card including information related to the destination. In this way, by taking into consideration the geographical location information of the newcomer, problems specific to the area can be effectively provided.

[0057] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the social media activities of customers. For example, the analysis unit analyzes complaints posted by customers on social media. For example, the analysis unit analyzes data related to complaints posted by customers on social media. The analysis unit can also analyze topics of interest from the social media activities of customers. For example, the analysis unit analyzes related data based on information shared by customers on social media. The analysis unit can also analyze related data based on information shared by customers on social media. For example, the analysis unit analyzes related data based on information shared by customers on social media. This makes it possible to effectively analyze topics of interest by referring to social media activities.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The collection unit collects customer usage or complaints. The collection unit can collect customer usage or complaints using, for example, a questionnaire or feedback form. The collection unit can also analyze customers' social media activities and collect related information. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the collected information using, for example, natural language processing or machine learning. Step 3: The generation unit creates a cue card based on the information analyzed by the analysis unit. For example, the generation unit can organize customer usage status and complaints in bullet points or flowchart format to create a cue card for new employees. Step 4: The providing unit provides the newcomer with the cue card created by the generating unit. The providing unit can, for example, update the cue card based on periodically collected data. The providing unit can also adjust the format and content of the cue card to make it easier for the newcomer to use.

[0060] (Example 2) A system according to an embodiment of the present invention is a system for creating cue cards for mobile proposal methods. This system collects customer usage data and complaints, and a generation AI creates cue cards for new employees, making it easier for new employees to understand the customer's situation. For example, when collecting customer usage data and complaints, detailed data is collected, such as the types of services the customers use and the points they are dissatisfied with. Next, based on the collected information, the generation AI creates cue cards for new employees. The generation AI analyzes the collected data and creates cue cards in a format that is easy for new employees to understand. For example, the generation AI organizes the customer's usage data and complaints in bullet points and writes proposals in response to them. The created cue cards can be used as a reference when new employees make proposals to customers. This makes it easier for new employees to understand the customer's usage data and complaints and make appropriate proposals. For example, if a customer feels that an app is running slowly, the system can make proposals to resolve the issue. This system makes it easier for new employees to understand the customer's usage data and complaints and make appropriate proposals. This is expected to improve customer satisfaction and promote service usage. The system makes it easier for new employees to understand the customer's usage data and complaints and make appropriate proposals.

[0061] A cue card generation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects customer usage status or complaints. The collection unit can collect customer usage status or complaints using, for example, a questionnaire or a feedback form. The collection unit can also analyze customers' social media activities and collect related information. The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the collected information using, for example, natural language processing or machine learning. The generation unit creates a cue card based on the information analyzed by the analysis unit. The generation unit can organize customer usage status and complaints in bullet points or a flowchart format to create a cue card for new employees. The provision unit provides the cue card created by the generation unit to new employees. The provision unit can update the cue card based on, for example, periodically collected data. The provision unit can also adjust the format and content of the cue card to make it easier for new employees to use. As a result, the cue card generation system according to an embodiment collects and analyzes customer usage status and complaints, creates a cue card, and provides it to new employees, making it easier for new employees to understand the customer's situation.

[0062] The collection unit can collect customer usage status or complaints using a questionnaire or feedback form. The collection unit collects customer usage status or complaints using, for example, a questionnaire. For example, the questionnaire includes questions asking what kind of service the customer is using and what aspects the customer is dissatisfied with. The collection unit can also collect customer usage status or complaints using a feedback form. For example, the feedback form has a section where the customer can write down any complaints or areas for improvement they have about the service. In this way, customer usage status and complaints can be effectively collected by using the questionnaire or feedback form.

[0063] The analysis unit can analyze the collected information using natural language processing or machine learning. The analysis unit analyzes the collected information using, for example, natural language processing technology. For example, the analysis unit divides text data using morphological analysis and analyzes the meaning of each word. The analysis unit can also analyze sentence structure using grammatical analysis to understand the relevance of information. Furthermore, the analysis unit can analyze the collected information using a machine learning algorithm. For example, the analysis unit can group data using K-means clustering to identify patterns of customer usage and dissatisfaction. This allows the collected information to be analyzed with high accuracy using natural language processing or machine learning.

[0064] The generation unit can organize customer usage status or complaints in bullet points or flow chart format to create a cue card for new employees. The generation unit, for example, organizes customer usage status and complaints in bullet points. For example, the generation unit creates a cue card in a format that is easy for new employees to understand by bullet pointing out services that customers frequently use and complaints they have. The generation unit can also organize customer usage status and complaints in flowchart format. For example, the generation unit organizes customer usage status and complaints in a visually easy-to-understand manner in a flowchart so that new employees can refer to it when making proposals. In this way, customer usage status and complaints can be clearly organized to create a cue card that is easy for new employees to understand.

[0065] The providing unit can update the cue card based on data collected periodically. The providing unit updates the cue card based on data collected weekly, for example. For example, the providing unit analyzes the results of weekly data collection and reflects new information and changes in the cue card. The providing unit can also update the cue card based on data collected monthly. For example, the providing unit reviews the contents of the cue card based on the results of monthly data collection and reflects the latest information. In this way, by regularly updating the cue card, it is possible to provide a cue card that reflects the latest information.

[0066] The providing unit can adjust the format or content of the cue card to make it easier for newcomers to use the cue card. For example, the providing unit adjusts the font size of the cue card. For example, the providing unit increases the font size so that it is easier for newcomers to read. The providing unit can also adjust the color of the cue card. For example, the providing unit uses a specific color to highlight important information. Furthermore, the providing unit can adjust the level of detail of the information on the cue card. For example, the providing unit summarizes the information succinctly so that it is easier for newcomers to understand. This makes it easier for newcomers to use the cue card, thereby improving the quality of suggestions.

[0067] The collection unit can estimate the customer's emotions and determine the priority of information to be collected based on the estimated customer emotions. For example, if a customer is dissatisfied, the collection unit prioritizes collecting information related to the dissatisfaction. For example, if a customer is dissatisfied with a particular service, the collection unit prioritizes collecting information related to the service. Furthermore, if a customer is satisfied, the collection unit can also prioritize collecting information to maintain the customer's satisfaction. For example, the collection unit collects information related to a service with which the customer is satisfied and makes suggestions to maintain the satisfaction. Furthermore, if a customer is excited, the collection unit can identify the cause of the excitement and prioritize collecting related information. For example, the collection unit collects information related to a service or function that the customer is excited about and makes suggestions to maintain the excitement. In this way, by determining the priority of information based on the customer's emotions, more important information can be preferentially collected.

[0068] The collection unit can analyze the customer's past feedback history and select the optimal collection method. The collection unit, for example, optimizes the questionnaire format based on feedback provided by the customer in the past. For example, the collection unit analyzes the past feedback history and selects a questionnaire format that is easy for the customer to respond to. The collection unit can also prioritize collection of specific questions from the customer's past feedback history. For example, the collection unit prioritizes collection of questions that the customer is interested in based on the past feedback history. The collection unit can also analyze the customer's feedback history and select the most effective collection method (email, telephone, etc.). For example, the collection unit selects the collection method that is easiest for the customer to respond to based on the past feedback history. In this way, the optimal collection method can be selected by analyzing the past feedback history.

[0069] The collection unit can filter the data based on the customer's current usage status and areas of interest when collecting the data. For example, the collection unit prioritizes collecting information related to services currently being used by the customer. For example, the collection unit filters and collects information related to services frequently used by the customer. The collection unit can also filter and collect related information based on the customer's areas of interest. For example, the collection unit prioritizes collecting information related to topics in which the customer is interested. Furthermore, the collection unit can monitor the customer's usage status in real time and dynamically filter and collect related information. For example, the collection unit monitors the customer's usage status in real time and prioritizes collecting information related to frequently used services. In this way, highly relevant information can be collected by filtering the information based on the customer's usage status and areas of interest.

[0070] The collection unit can estimate the customer's emotions and adjust the timing of collection based on the estimated customer's emotions. For example, if the customer is relaxed, the collection unit selects the timing to request feedback. For example, the collection unit requests feedback during a time period when the customer is relaxed. Furthermore, if the customer is busy, the collection unit can postpone the timing of requesting feedback. For example, the collection unit requests feedback by avoiding a time period when the customer is busy. Furthermore, if the customer is excited, the collection unit can request feedback after the customer has calmed down. For example, the collection unit requests feedback by avoiding situations where the customer is excited. In this way, by adjusting the timing of collection based on the customer's emotions, information can be collected at an appropriate time.

[0071] When collecting data, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the customer. For example, if the customer is in a specific area, the collection unit prioritizes collecting information related to that area. For example, the collection unit collects information related to services that the customer uses in a specific area. The collection unit can also collect area-specific problems based on the customer's geographical location information. For example, the collection unit collects information related to problems occurring in a specific area. Furthermore, if the customer is traveling, the collection unit can prioritize collecting information related to the customer's destination. For example, the collection unit collects information related to services that the customer may use at the customer's destination. In this way, area-specific problems can be effectively collected by taking into account the customer's geographical location information.

[0072] At the time of collection, the collection unit can analyze the social media activities of customers and collect related information. The collection unit, for example, collects complaints posted by customers on social media. For example, the collection unit collects information related to complaints posted by customers on social media. The collection unit can also collect topics of interest from the social media activities of customers. For example, the collection unit collects related data based on information shared by customers on social media. The collection unit can also collect related data based on information shared by customers on social media. For example, the collection unit collects related data based on information shared by customers on social media. In this way, topics of interest can be effectively collected by analyzing the social media activities of customers.

[0073] The analysis unit can estimate the customer's emotions and determine the priority of analysis based on the estimated customer's emotions. For example, if a customer is dissatisfied, the analysis unit prioritizes analyzing data related to the dissatisfaction. For example, if a customer is dissatisfied with a particular service, the analysis unit prioritizes analyzing data related to that service. Furthermore, if a customer is satisfied, the analysis unit can also prioritize analyzing data for maintaining that satisfaction. For example, the analysis unit prioritizes analyzing data related to services with which the customer is satisfied. Furthermore, if a customer is excited, the analysis unit can also prioritize analyzing data for identifying the cause of the excitement. For example, the analysis unit prioritizes analyzing data related to services or functions that the customer is excited about. In this way, by determining the priority of analysis based on the customer's emotions, important data can be analyzed preferentially.

[0074] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the customer's past feedback history. The analysis unit, for example, optimizes the analysis algorithm based on the customer's past feedback history. For example, the analysis unit analyzes the past feedback history and prioritizes analysis of data that the customer is interested in. The analysis unit can also extract specific patterns from the customer's past feedback history and reflect them in the analysis. For example, the analysis unit extracts patterns that the customer is interested in based on the past feedback history and reflects them in the analysis. Furthermore, the analysis unit can also improve the accuracy of the analysis results by referring to the customer's feedback history. For example, the analysis unit improves the accuracy of the analysis results based on the past feedback history. In this way, the accuracy of the analysis can be improved by referring to the past feedback history.

[0075] During analysis, the analysis unit can apply different analysis algorithms depending on the customer's usage status and the category of dissatisfaction. The analysis unit selects the optimal analysis algorithm depending on, for example, the customer's usage status. For example, the analysis unit applies a specific analysis algorithm to analyze data related to a service that the customer frequently uses. The analysis unit can also apply a specific analysis algorithm based on the category of customer dissatisfaction. For example, the analysis unit applies a specific analysis algorithm to analyze data related to a service that the customer is dissatisfied with. The analysis unit can also combine the customer's usage status and the category of dissatisfaction to apply the optimal analysis algorithm. For example, the analysis unit selects the optimal analysis algorithm based on the customer's usage status and the category of dissatisfaction. In this way, by applying the optimal analysis algorithm depending on the usage status and the category of dissatisfaction, the accuracy of the analysis can be improved.

[0076] The analysis unit can estimate the customer's emotions and adjust the timing of the analysis based on the estimated customer's emotions. For example, if the customer is relaxed, the analysis unit performs the analysis quickly. For example, the analysis unit performs the analysis during a time period when the customer is relaxed. Furthermore, if the customer is busy, the analysis unit can postpone the timing of the analysis. For example, the analysis unit performs the analysis by avoiding a time period when the customer is busy. Furthermore, if the customer is excited, the analysis unit can perform the analysis after the customer has calmed down. For example, the analysis unit performs the analysis by avoiding a situation when the customer is excited. In this way, by adjusting the timing of the analysis based on the customer's emotions, the analysis can be performed at an appropriate time.

[0077] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the geographical location information of the customer. For example, if the customer is in a specific region, the analysis unit prioritizes analyzing data related to that region. For example, the analysis unit analyzes data related to services used by the customer in a specific region. The analysis unit can also analyze region-specific issues based on the customer's geographical location information. For example, the analysis unit analyzes data related to issues occurring in a specific region. Furthermore, if the customer is traveling, the analysis unit can prioritize analyzing data related to the customer's destination. For example, the analysis unit analyzes data related to services the customer may use at the destination. In this way, region-specific issues can be effectively analyzed by taking into account the geographical location information.

[0078] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the customer's social media activity. The analysis unit, for example, analyzes complaints posted by customers on social media. For example, the analysis unit analyzes data related to complaints posted by customers on social media. The analysis unit can also analyze topics of interest from the customer's social media activity. For example, the analysis unit analyzes related data based on information shared by customers on social media. The analysis unit can also analyze related data based on information shared by customers on social media. For example, the analysis unit analyzes related data based on information shared by customers on social media. This makes it possible to effectively analyze topics of interest by referring to social media activity.

[0079] The generation unit can estimate the customer's emotions and adjust the way the cue card is expressed based on the estimated customer's emotions. For example, if the customer is relaxed, the generation unit generates a cue card using a soft expression. For example, the generation unit creates a cue card using a soft expression to match the situation in which the customer is relaxed. Furthermore, if the customer is dissatisfied, the generation unit can generate a cue card that emphasizes a specific solution. For example, the generation unit creates a cue card that emphasizes a specific solution to match the situation in which the customer is dissatisfied. Furthermore, if the customer is excited, the generation unit can generate a cue card using a visually stimulating design. For example, the generation unit creates a cue card using a visually stimulating design to match the situation in which the customer is excited. In this way, by adjusting the way the cue card is expressed based on the customer's emotions, it is possible to provide a more effective cue card.

[0080] The generation unit can adjust the level of detail of the cue card based on the customer's usage status and the importance of the dissatisfaction when generating the cue card. For example, if the customer's dissatisfaction is serious, the generation unit generates a cue card including a detailed solution. For example, if the customer has serious dissatisfaction, the generation unit creates a cue card including a detailed solution for resolving the dissatisfaction. The generation unit can also generate a cue card including a detailed explanation when the customer's usage status is complex. For example, if the customer has a complex usage status, the generation unit creates a cue card including a detailed explanation for understanding the situation. Furthermore, the generation unit can also generate a concise cue card when the customer's dissatisfaction is minor. For example, if the customer has minor dissatisfaction, the generation unit creates a concise cue card for resolving the dissatisfaction. In this way, by adjusting the level of detail of the cue card based on the usage status and the importance of the dissatisfaction, it is possible to provide a cue card with an appropriate amount of information.

[0081] The generation unit can apply different generation algorithms depending on the customer's usage status and the category of dissatisfaction when generating the cue card. The generation unit selects the optimal generation algorithm depending on, for example, the customer's usage status. For example, the generation unit applies a specific generation algorithm based on data related to services frequently used by the customer. The generation unit can also apply a specific generation algorithm based on the category of dissatisfaction of the customer. For example, the generation unit applies a specific generation algorithm based on data related to services about which the customer is dissatisfied. The generation unit can also apply the optimal generation algorithm by combining the customer's usage status and the category of dissatisfaction. For example, the generation unit selects the optimal generation algorithm based on the customer's usage status and the category of dissatisfaction. In this way, the accuracy of the cue card can be improved by applying the optimal generation algorithm depending on the usage status and the category of dissatisfaction.

[0082] The generation unit can estimate the customer's emotions and adjust the length of the cue card based on the estimated customer's emotions. For example, if the customer is in a hurry, the generation unit generates a cue card that is short and to the point. For example, the generation unit creates a cue card that is short and to the point to suit the situation where the customer is in a hurry. The generation unit can also generate a longer cue card that includes detailed explanations to suit the situation where the customer is relaxed. For example, the generation unit creates a longer cue card that includes detailed explanations to suit the situation where the customer is relaxed. Furthermore, the generation unit can also generate a cue card that adds visually stimulating effects to the customer when the customer is excited. For example, the generation unit creates a cue card that adds visually stimulating effects to suit the situation where the customer is excited. In this way, by adjusting the length of the cue card based on the customer's emotions, it is possible to provide a cue card of an appropriate length.

[0083] At the time of generation, the generation unit can determine the priority of cue cards based on the timing of customer feedback submission. For example, if a customer has recently submitted feedback, the generation unit preferentially generates cue cards based on that feedback. For example, the generation unit preferentially creates cue cards based on feedback recently submitted by the customer. The generation unit can also determine the priority of cue cards based on feedback previously submitted by the customer. For example, the generation unit determines the priority of cue cards based on feedback previously submitted by the customer. Furthermore, the generation unit can generate cue cards at the optimal timing, taking into account the timing of customer feedback submission. For example, the generation unit creates cue cards at the optimal timing based on the timing of customer feedback submission. In this way, by determining the priority of cue cards based on the timing of feedback submission, it is possible to provide timely cue cards.

[0084] The generation unit can adjust the order of the cue cards at the time of generation based on the relevance of the customer's usage status and dissatisfaction. For example, the generation unit prioritizes writing the most relevant information on the cue cards based on the customer's usage status. For example, the generation unit prioritizes writing information related to services that the customer uses frequently on the cue cards. The generation unit can also adjust the order of the cue cards taking into account the relevance of the customer's dissatisfaction. For example, the generation unit prioritizes writing information related to services that the customer is dissatisfied with on the cue cards. Furthermore, the generation unit can combine the relevance of the customer's usage status and dissatisfaction to generate the cue cards in an optimal order. For example, the generation unit creates the cue cards in an optimal order based on the relevance of the customer's usage status and dissatisfaction. In this way, by adjusting the order of the cue cards based on the relevance of the usage status and dissatisfaction, an effective cue card can be provided.

[0085] The providing unit can estimate the customer's emotions and adjust the method of providing the cue card based on the estimated customer's emotions. For example, when the customer is relaxed, the providing unit provides the cue card using gentle expressions. For example, the providing unit provides the cue card using gentle expressions to match the situation in which the customer is relaxed. Furthermore, when the customer is dissatisfied, the providing unit can also provide the cue card emphasizing a specific solution. For example, the providing unit provides the cue card emphasizing a specific solution to match the situation in which the customer is dissatisfied. Furthermore, when the customer is excited, the providing unit can also provide the cue card using a visually stimulating design. For example, the providing unit provides the cue card using a visually stimulating design to match the situation in which the customer is excited. In this way, by adjusting the method of providing the cue card based on the customer's emotions, it is possible to provide more effective cue cards.

[0086] When providing the information, the providing unit can select the optimal providing method by referring to the new employee's past usage history. The providing unit selects the optimal providing method, for example, based on the format of cue cards used by the new employee in the past. For example, the providing unit selects the optimal providing method based on the format of cue cards used by the new employee in the past. The providing unit can also preferentially select a specific providing method based on the new employee's past usage history. For example, the providing unit selects the optimal providing method based on the content of cue cards used by the new employee in the past. Furthermore, the providing unit can also select the optimal providing method based on the new employee's past usage history. For example, the providing unit selects the optimal providing method based on the content of cue cards used by the new employee in the past. In this way, the optimal providing method can be selected by referring to the new employee's past usage history.

[0087] The providing unit can customize the content of the cue card according to the new employee's level of understanding when providing it. For example, if the new employee has a high level of understanding, the providing unit provides a cue card from which detailed explanations are omitted. For example, if the new employee has a high level of understanding, the providing unit provides a cue card from which detailed explanations are omitted. Furthermore, if the new employee has a low level of understanding, the providing unit can also provide a cue card including detailed explanations. For example, if the new employee has a low level of understanding, the providing unit provides a cue card including detailed explanations. Furthermore, the providing unit can dynamically customize the content of the cue card according to the new employee's level of understanding. For example, the providing unit dynamically customizes the content of the cue card according to the new employee's level of understanding. In this way, by customizing the content of the cue card according to the new employee's level of understanding, an effective cue card can be provided.

[0088] The providing unit can estimate the emotions of the customer and adjust the timing of providing the cue card based on the estimated emotions of the customer. For example, if the customer is relaxed, the providing unit selects the timing of providing the cue card. For example, the providing unit provides the cue card during a time period when the customer is relaxed. Furthermore, if the customer is busy, the providing unit can postpone the timing of providing the cue card. For example, the providing unit provides the cue card avoiding a time period when the customer is busy. Furthermore, if the customer is excited, the providing unit can provide the cue card after the customer has calmed down. For example, the providing unit provides the cue card avoiding a situation when the customer is excited. In this way, by adjusting the timing of providing the cue card based on the emotions of the customer, the cue card can be provided at an appropriate time.

[0089] The providing unit can select the optimal providing method by taking into consideration the geographical location information of the newcomer when providing the information. For example, if the newcomer is in a specific area, the providing unit provides a cue card including information related to the area. For example, if the newcomer is in a specific area, the providing unit provides a cue card including information related to the area. The providing unit can also provide a cue card including problems specific to the area based on the geographical location information of the newcomer. For example, if the newcomer is in a specific area, the providing unit provides a cue card including information related to the area. Furthermore, if the newcomer is traveling, the providing unit can also provide a cue card including information related to the destination. For example, if the newcomer is traveling, the providing unit provides a cue card including information related to the destination. In this way, by taking into consideration the geographical location information of the newcomer, problems specific to the area can be effectively provided.

[0090] The provision unit can adjust the method of providing the cue card by analyzing the newcomer's social media activity at the time of provision. The provision unit, for example, adjusts the method of providing the cue card based on information posted by the newcomer on social media. For example, the provision unit adjusts the method of providing the cue card based on information posted by the newcomer on social media. The provision unit can also provide a cue card including topics of interest from the newcomer's social media activity. For example, the provision unit provides a cue card including information related to topics in which the newcomer has shown interest on social media. The provision unit can also adjust the method of providing the cue card based on information shared by the newcomer on social media. For example, the provision unit adjusts the method of providing the cue card based on information shared by the newcomer on social media. In this way, topics of interest can be effectively provided by analyzing the newcomer's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects customer usage status and complaints using the camera 42 and microphone 38B of the smart device 14 and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a cue card based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the created cue card to the new employee. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects customer usage status and complaints using the camera 42 and microphone 238 of the smart glasses 214 and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a cue card based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the created cue card to the new employee. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects customer usage status and complaints using the camera 42 and microphone 238 of the headset-type terminal 314, and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a cue card based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and provides the created cue card to the new employee. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects customer usage status and complaints using the camera 42 and microphone 238 of the robot 414, and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a cue card based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the created cue card to the new employee.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The analysis unit can estimate the customer's emotions and determine the priority of analysis based on the estimated customer's emotions. For example, if a customer is dissatisfied, the analysis unit prioritizes analyzing data related to the dissatisfaction. For example, if a customer is dissatisfied with a particular service, the analysis unit prioritizes analyzing data related to that service. In addition, if a customer is satisfied, the analysis unit can also prioritize analyzing data for maintaining that satisfaction. For example, the analysis unit prioritizes analyzing data related to services with which the customer is satisfied. Furthermore, if a customer is excited, the analysis unit can also prioritize analyzing data for identifying the cause of the excitement. For example, the analysis unit prioritizes analyzing data related to services or functions that the customer is excited about. In this way, by determining the priority of analysis based on the customer's emotions, important data can be analyzed preferentially.

[0093] The provision unit can select the optimal provision method by referring to the new employee's past usage history. For example, the provision unit selects the optimal provision method based on the format of cue cards used by the new employee in the past. For example, the provision unit selects the optimal provision method based on the format of cue cards used by the new employee in the past. The provision unit can also preferentially select a specific provision method based on the new employee's past usage history. For example, the provision unit selects the optimal provision method based on the content of cue cards used by the new employee in the past. The provision unit can also select the optimal provision method based on the new employee's past usage history. For example, the provision unit selects the optimal provision method based on the content of cue cards used by the new employee in the past. In this way, the optimal provision method can be selected by referring to the new employee's past usage history.

[0094] The collection unit can estimate the customer's emotions and determine the priority of information to be collected based on the estimated customer emotions. For example, if a customer is dissatisfied, the collection unit prioritizes collecting information related to the dissatisfaction. For example, if a customer is dissatisfied with a particular service, the collection unit prioritizes collecting information related to that service. Furthermore, if a customer is satisfied, the collection unit can also prioritize collecting information to maintain that satisfaction. For example, the collection unit collects information related to services that the customer is satisfied with and makes suggestions to maintain that satisfaction. Furthermore, if a customer is excited, the collection unit can identify the cause of the excitement and prioritize collecting related information. For example, the collection unit collects information related to services or features that the customer is excited about and makes suggestions to maintain that excitement. In this way, by determining the priority of information based on the customer's emotions, more important information can be preferentially collected.

[0095] The generation unit can estimate the customer's emotions and adjust the way the cue card is expressed based on the estimated customer's emotions. For example, if the customer is relaxed, the generation unit generates a cue card using a gentle expression. For example, the generation unit creates a cue card using a gentle expression to match the situation in which the customer is relaxed. Furthermore, if the customer is dissatisfied, the generation unit can generate a cue card that emphasizes a specific solution. For example, the generation unit creates a cue card that emphasizes a specific solution to match the situation in which the customer is dissatisfied. Furthermore, if the customer is excited, the generation unit can generate a cue card using a visually stimulating design. For example, the generation unit creates a cue card using a visually stimulating design to match the situation in which the customer is excited. In this way, by adjusting the way the cue card is expressed based on the customer's emotions, it is possible to provide a more effective cue card.

[0096] The providing unit can estimate the emotions of the customer and adjust the timing of providing the cue card based on the estimated emotions of the customer. For example, if the customer is relaxed, the timing of providing the cue card is selected. For example, the providing unit provides the cue card during a time period when the customer is relaxed. Furthermore, if the customer is busy, the providing unit can postpone the timing of providing the cue card. For example, the providing unit provides the cue card avoiding a time period when the customer is busy. Furthermore, if the customer is excited, the providing unit can provide the cue card after the customer has calmed down. For example, the providing unit provides the cue card avoiding a situation in which the customer is excited. In this way, by adjusting the timing of providing the cue card based on the emotions of the customer, the cue card can be provided at an appropriate time.

[0097] The collection unit can analyze the customer's past feedback history and select the optimal collection method. For example, the collection unit optimizes the questionnaire format based on feedback provided by the customer in the past. For example, the collection unit analyzes the past feedback history and selects a questionnaire format that is easy for the customer to respond to. The collection unit can also prioritize collection of specific questions from the customer's past feedback history. For example, the collection unit prioritizes collection of questions that the customer is interested in based on the past feedback history. Furthermore, the collection unit can analyze the customer's feedback history and select the most effective collection method (email, telephone, etc.). For example, the collection unit selects the collection method that is easiest for the customer to respond to based on the past feedback history. In this way, the optimal collection method can be selected by analyzing the past feedback history.

[0098] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the customer's past feedback history. For example, the analysis unit optimizes the analysis algorithm based on the customer's past feedback history. For example, the analysis unit analyzes the past feedback history and prioritizes analysis of data that the customer is interested in. The analysis unit can also extract specific patterns from the customer's past feedback history and reflect them in the analysis. For example, the analysis unit extracts patterns that the customer is interested in based on the past feedback history and reflects them in the analysis. Furthermore, the analysis unit can also improve the accuracy of the analysis results by referring to the customer's feedback history. For example, the analysis unit improves the accuracy of the analysis results based on the past feedback history. In this way, the accuracy of the analysis can be improved by referring to the past feedback history.

[0099] The generation unit can adjust the level of detail of the cue card based on the customer's usage status and the importance of the dissatisfaction when generating the cue card. For example, if the customer's dissatisfaction is serious, the generation unit generates a cue card including detailed solutions. For example, if the customer has serious dissatisfaction, the generation unit creates a cue card including detailed solutions for resolving the dissatisfaction. The generation unit can also generate a cue card including detailed explanations when the customer's usage status is complex. For example, if the customer has a complex usage situation, the generation unit creates a cue card including detailed explanations for understanding the situation. Furthermore, the generation unit can also generate a concise cue card when the customer's dissatisfaction is minor. For example, if the customer has minor dissatisfaction, the generation unit creates a concise cue card for resolving the dissatisfaction. In this way, by adjusting the level of detail of the cue card based on the usage status and the importance of the dissatisfaction, it is possible to provide a cue card with an appropriate amount of information.

[0100] The providing unit can select the optimal providing method by taking into consideration the geographical location information of the newcomer when providing the information. For example, if the newcomer is in a specific area, the providing unit can provide a cue card including information related to the area. For example, if the newcomer is in a specific area, the providing unit can provide a cue card including information related to the area. The providing unit can also provide a cue card including problems specific to the area based on the geographical location information of the newcomer. For example, if the newcomer is in a specific area, the providing unit can provide a cue card including information related to the area. Furthermore, if the newcomer is traveling, the providing unit can also provide a cue card including information related to the destination. For example, if the newcomer is traveling, the providing unit can provide a cue card including information related to the destination. In this way, by taking into consideration the geographical location information of the newcomer, problems specific to the area can be effectively provided.

[0101] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the social media activities of customers. For example, the analysis unit analyzes complaints posted by customers on social media. For example, the analysis unit analyzes data related to complaints posted by customers on social media. The analysis unit can also analyze topics of interest from the social media activities of customers. For example, the analysis unit analyzes related data based on information shared by customers on social media. The analysis unit can also analyze related data based on information shared by customers on social media. For example, the analysis unit analyzes related data based on information shared by customers on social media. This makes it possible to effectively analyze topics of interest by referring to social media activities.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The collection unit collects customer usage or complaints. The collection unit can collect customer usage or complaints using, for example, a questionnaire or feedback form. The collection unit can also analyze customers' social media activities and collect related information. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the collected information using, for example, natural language processing or machine learning. Step 3: The generation unit creates a cue card based on the information analyzed by the analysis unit. For example, the generation unit can organize customer usage status and complaints in bullet points or flowchart format to create a cue card for new employees. Step 4: The providing unit provides the newcomer with the cue card created by the generating unit. The providing unit can, for example, update the cue card based on periodically collected data. The providing unit can also adjust the format and content of the cue card to make it easier for the newcomer to use.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, etc., and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device, etc.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0148] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0160] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0165] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0166] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0167] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0168] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0169] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0170] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects customer usage status or complaints; an analysis unit that analyzes the information collected by the collection unit; a generation unit that generates a cue card based on the information analyzed by the analysis unit; a providing unit that provides the newcomer with the cue card created by the generating unit; A system characterized by:

2. The collecting unit Use surveys or feedback forms to gather customer feedback or complaints 2. The system of claim 1.

3. The analysis unit Analyze the collected information using natural language processing or machine learning 2. The system of claim 1.

4. The generation unit Organize customer usage or complaints in bullet points or flowchart format to create a cheat sheet for new employees 2. The system of claim 1.

5. The providing unit Update your cheat sheet based on regularly collected data 2. The system of claim 1.

6. The providing unit Adjust the format or content of the cue cards to make them more accessible to newcomers.

2. The system of claim 1.

7. The collecting unit Estimate customer sentiment and prioritize the information to be collected based on the estimated sentiment 2. The system of claim 1.

8. The collecting unit Analyze customer feedback history and choose the best collection method 2. The system of claim 1.

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