System

Abstract

An object of the system according to the embodiment is to efficiently collect and analyze the company's own data and information on services, and to quickly propose a handling method.SOLUTION: A system includes a reception unit, a collection unit, an analysis unit, and a proposal unit. The reception part inputs own company data and information related to a service. The collection unit collects data based on the information input by the reception unit. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes a handling method based on the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

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] With conventional technology, it was difficult to efficiently collect and analyze information about a company's data and services, and quickly propose countermeasures.

[0005] The system according to the embodiment aims to efficiently collect and analyze company data and information related to services, and to quickly propose response methods. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, an analysis unit, and a proposal unit. The reception unit inputs information about the company's data and services. The collection unit collects data based on the information input by the reception unit. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes a response method based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect and analyze company data and information about services, and quickly propose response methods. [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 data collection and analysis system according to an embodiment of the present invention automatically collects and analyzes information about a company's data and services, enabling rapid responses to inquiries. In the data collection and analysis system, a user inputs information about the company's data and services, and the system automatically collects and analyzes the data and proposes optimal responses. For example, in a data collection and analysis system, a user inputs information about the company's database and services, including product information, customer information, and past inquiry history. This information is entered into the system. The data collection and analysis system then automatically collects related data based on the input information, such as product usage status and customer feedback. The collected data is analyzed by the system to identify the optimal response to the inquiry. Based on the analysis results, the data collection and analysis system then proposes specific response methods to the user, such as product troubleshooting methods and customer follow-up methods. This allows the user to respond to inquiries quickly and efficiently. The data collection and analysis system allows users to utilize their company's data and services to respond to inquiries quickly and efficiently. For example, appropriate responses to various inquiries, such as product troubleshooting and customer follow-up, are possible. In addition, because the system automatically collects and analyzes data, users can respond quickly and effortlessly, which is expected to improve customer satisfaction and operational efficiency.

[0029] A data collection and analysis system according to an embodiment includes a reception unit, a collection unit, an analysis unit, and a proposal unit. The reception unit inputs company data and information about services. The company data includes, but is not limited to, sales data, customer data, and product data. For example, a user inputs information about the company database and services into the reception unit. The collection unit collects data based on the information input by the reception unit. The collection unit collects data such as product information, customer information, and past inquiry history. The collection unit can also automatically collect data such as product usage and customer feedback. For example, the collection unit monitors product usage and collects data. The collection unit can also collect customer feedback and store it in a database. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit identifies an optimal response method for an inquiry based on the collected data. For example, the analysis unit uses data mining technology to extract patterns from the collected data and identify the optimal response method. The analysis unit can also analyze the collected data and identify the optimal response method using a machine learning algorithm. The suggestion unit proposes an optimal response method based on the analysis results obtained by the analysis unit. The suggestion unit proposes, for example, a product troubleshooting method or a customer follow-up method. For example, the suggestion unit proposes a product troubleshooting method and provides specific response procedures to the user. The suggestion unit can also propose a customer follow-up method and provide specific follow-up procedures to the user. This allows the data collection and analysis system according to the embodiment to enable users to respond to inquiries quickly and efficiently. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may propose a response method using an AI model that inputs the analysis results obtained by the analysis unit and outputs an optimal response method.

[0030] The collection unit may collect data on product information, customer information, and past inquiry history. Product information includes, but is not limited to, product specifications, product usage methods, and product lifecycles. For example, the collection unit may collect product specification information and store it in a database. The collection unit may also collect information on product usage methods and store it in a database. The collection unit may also collect information on product lifecycles and store it in a database. Customer information includes, but is not limited to, customer attribute information, purchase history, and customer feedback. For example, the collection unit may collect customer attribute information and store it in a database. The collection unit may also collect customer purchase history and store it in a database. The collection unit may also collect customer feedback and store it in a database. Past inquiry history includes, but is not limited to, inquiry content, response history, and resolution status. For example, the collection unit may collect past inquiry content and store it in a database. The collection unit may also collect past response history and store it in a database. The collection unit may also collect past resolution statuses and store it in a database. This enables more accurate analysis by collecting a variety of data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input product information, customer information, and past inquiry history data into the generation AI and have the generation AI collect the data.

[0031] The analysis unit can identify a response method for an inquiry based on the collected data. The analysis unit, for example, identifies an optimal response method for an inquiry based on the collected data. For example, the analysis unit can use data mining technology to extract patterns from the collected data and identify an optimal response method. The analysis unit can also analyze the collected data using a machine learning algorithm to identify an optimal response method. For example, the analysis unit can analyze past inquiry history and identify a response method for similar inquiries. The analysis unit can also analyze customer feedback and identify a response method that meets the customer's needs. Furthermore, the analysis unit can analyze product usage and identify a troubleshooting method for the product. This allows the analysis unit to identify an optimal response method, enabling a prompt response. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI identify an optimal response method.

[0032] The suggestion unit can suggest a troubleshooting method for the product and a follow-up method for the customer based on the analysis results. The suggestion unit, for example, suggests a troubleshooting method for the product based on the analysis results. For example, the suggestion unit suggests a troubleshooting method for the product and indicates specific response procedures to the user. The suggestion unit can also suggest a follow-up method for the customer based on the analysis results. For example, the suggestion unit suggests a follow-up method for the customer and indicates specific follow-up procedures to the user. The suggestion unit can also make a product improvement proposal based on the analysis results. For example, the suggestion unit makes a product improvement proposal and indicates specific improvement procedures to the user. In this way, the suggestion unit suggests a specific response method, allowing the user to respond quickly. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the analysis results to a generation AI and cause the generation AI to propose an optimal response method.

[0033] The reception unit can analyze the user's past input history and suggest an input method. For example, the reception unit can automatically display information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest information that will be used in a specific time period based on the user's past input history. This improves input efficiency by suggesting the optimal input method based on the past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI suggest the optimal input method.

[0034] The reception unit can customize input items based on the user's work situation and areas of interest when inputting information. For example, when the user is performing a specific task, the reception unit prioritizes displaying input items related to that task. The reception unit can also automatically suggest related input items based on the user's areas of interest. Furthermore, the reception unit can dynamically adjust the required input items according to the user's work situation. This improves input efficiency by providing input items according to the user's work situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's work situation and areas of interest into a generation AI and have the generation AI customize the input items.

[0035] When inputting information, the reception unit can select the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs information using voice recognition technology. Furthermore, if the user selects text input, the reception unit can prioritize keyboard input. Furthermore, if the user selects image input, the reception unit can input information using image recognition technology. This improves input efficiency by providing the optimal means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data to a generation AI and have the generation AI select the optimal input means.

[0036] When inputting information, the reception unit can prioritize inputting highly relevant information taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes inputting information related to that area. The reception unit can also automatically suggest relevant information based on the user's current location. Furthermore, the reception unit can select optimal input items based on the user's geographical location information. This improves input efficiency by prioritizing input of information based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to a generation AI and cause the generation AI to select highly relevant information.

[0037] The reception unit can analyze the user's social media activity and input relevant information when inputting information. The reception unit can suggest relevant input items based on, for example, information shared by the user on social media. The reception unit can also analyze the user's social media activity and automatically input relevant information. Furthermore, the reception unit can also input relevant information by referring to the activity of the user's friends on social media. This improves input efficiency by inputting information based on the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media activity data to a generation AI and cause the generation AI to input relevant information.

[0038] The reception unit can customize the input method by reflecting the user's past feedback when inputting information. For example, the reception unit can suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also adjust the input interface by reflecting the user's past feedback. Furthermore, the reception unit can analyze the user's feedback history and select the optimal input means. This improves input efficiency by providing an input method based on the user's past feedback. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI customize the input method.

[0039] The collection unit can analyze the user's past data collection history and select the optimal collection method. The collection unit can, for example, suggest the optimal collection method based on data collected by the user in the past. The collection unit can also analyze the user's past data collection history and select an efficient collection method. Furthermore, the collection unit can also suggest the optimal collection timing based on the user's data collection history. This improves collection efficiency by providing the optimal collection method based on the user's past data collection history. Some or all of the above-described processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the user's past data collection history data into the generation AI and cause the generation AI to select the optimal collection method.

[0040] The collection unit can perform filtering based on the user's current work status and areas of interest when collecting data. For example, if the user is performing a specific task, the collection unit prioritizes collecting data related to that task. The collection unit can also filter related data based on the user's areas of interest. Furthermore, the collection unit can dynamically adjust the required data according to the user's work status. This improves collection efficiency by collecting data based on the user's work status and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's work status and areas of interest to the generation AI and have the generation AI perform filtering.

[0041] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user selects voice input, the collection unit can collect data using voice recognition technology. Furthermore, if the user selects text input, the collection unit can also collect data using text analysis technology. Furthermore, if the user selects image input, the collection unit can also collect data using image recognition technology. This improves collection efficiency by providing the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input user input method data into a generation AI and have the generation AI select the optimal collection means.

[0042] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting data related to that area. The collection unit can also automatically suggest relevant data based on the user's current location. Furthermore, the collection unit can select optimal data based on the user's geographical location information. This improves collection efficiency by prioritizing the collection of data based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data into the generation AI and cause the generation AI to collect highly relevant data.

[0043] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit collects related data based on information shared by the user on social media. The collection unit can also analyze the user's social media activities and automatically collect related data. Furthermore, the collection unit can also collect related data by referring to the activities of the user's friends on social media. This improves collection efficiency by collecting data based on the user's social media activities. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect related data.

[0044] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit can suggest an optimal collection method based on feedback provided by the user in the past. The collection unit can also adjust the collection interface by reflecting the user's past feedback. Furthermore, the collection unit can analyze the user's feedback history and select an optimal collection means. This improves collection efficiency by providing a collection method based on the user's past feedback. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and have the generation AI customize the collection method.

[0045] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on general data. Furthermore, the analysis unit can dynamically adjust the depth of the analysis according to the importance of the data. This improves the efficiency of the analysis by providing an analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0046] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a specific analysis algorithm to product information. The analysis unit can also apply a different analysis algorithm to customer information. Furthermore, the analysis unit can apply a dedicated analysis algorithm to past inquiry history. This improves the accuracy of the analysis by providing an optimal analysis according to the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the data category into the generation AI and have the generation AI apply different analysis algorithms.

[0047] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. Furthermore, the analysis unit can perform a more accurate analysis by utilizing the user's past analysis results. This improves the accuracy of the analysis by providing an optimal analysis based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0048] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit can prioritize analysis of urgent data. The analysis unit can also analyze periodic data with normal priority. Furthermore, the analysis unit can dynamically adjust the priority of analysis according to the time of data submission. This improves the efficiency of analysis by providing the priority of analysis according to the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of data submission to the generation AI and have the generation AI determine the priority of analysis.

[0049] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This improves the efficiency of analysis by providing an analysis order based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0050] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit uses a lot of technical terminology for users with high levels of expertise. The analysis unit can also provide explanations in simpler terms for users with low levels of expertise. Furthermore, the analysis unit can dynamically adjust the use of technical terminology according to the user's level of expertise. This improves understanding of the analysis by providing analysis results according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0051] The suggestion unit can adjust the level of detail of the proposal based on the importance of the response method when making a proposal. For example, the suggestion unit makes a detailed proposal for an important response method. The suggestion unit can also make a simplified proposal for a general response method. Furthermore, the suggestion unit can dynamically adjust the level of detail of the proposal according to the importance of the response method. This improves the efficiency of the proposal by providing a proposal according to the importance of the response method. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input importance data of the response method to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0052] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the response method. For example, the proposal unit applies a specific proposal algorithm to product problems. The proposal unit can also apply a different proposal algorithm to customer follow-up. Furthermore, the proposal unit can select the optimal proposal algorithm depending on the content of the inquiry. This improves the accuracy of the proposal by providing the optimal proposal depending on the category of the response method. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input category data of the response method into the generation AI and cause the generation AI to apply different proposal algorithms.

[0053] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, optimizes the proposal algorithm based on the user's past proposal results. The suggestion unit can also improve the accuracy of the proposal by referring to the user's past proposal results. Furthermore, the suggestion unit can make more accurate suggestions by utilizing the user's past proposal results. This improves the accuracy of the proposal by providing optimal suggestions based on the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0054] The suggestion unit can determine the priority of the proposals based on the submission time of the response methods when making the proposals. For example, the suggestion unit can prioritize proposals for emergency response methods. The suggestion unit can also propose regular response methods with normal priority. Furthermore, the suggestion unit can dynamically adjust the priority of the proposals according to the submission time of the response methods. This improves the efficiency of the proposals by providing the priority of the proposals according to the submission time of the response methods. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input data on the submission time of the response methods into the generation AI and cause the generation AI to determine the priority of the proposals.

[0055] The suggestion unit can adjust the order of suggestions based on the relevance of the response methods when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant response methods. The suggestion unit can also postpone suggesting less relevant response methods. Furthermore, the suggestion unit can dynamically adjust the order of suggestions based on the relevance of the response methods. This improves the efficiency of suggestions by providing an order of suggestions based on the relevance of the response methods. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input relevance data of the response methods to a generation AI and cause the generation AI to adjust the order of suggestions.

[0056] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, the suggestion unit uses a lot of technical terminology for a user with high level of expertise. The suggestion unit can also provide explanations in simpler terms for a user with low level of expertise. Furthermore, the suggestion unit can dynamically adjust the use of technical terminology according to the user's level of expertise. This improves understanding of the proposal by providing a proposal according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The reception unit can monitor the user's input speed in real time and dynamically adjust the interface according to the input speed. For example, if the user is inputting quickly, the reception unit can automatically enlarge the input field to smooth the input flow. Alternatively, if the user is inputting slowly, the reception unit can shrink the input field and display detailed guides or tips. Furthermore, if the user pauses input, the reception unit can display a pop-up message to encourage the user to resume input. This improves input efficiency by providing an interface that adapts to the user's input speed.

[0059] The collection unit can customize the data collection method based on the type of device the user is using. For example, if the user is using a smartphone, the collection unit can provide a mobile-friendly data collection method. Alternatively, if the user is using a desktop, the collection unit can provide detailed data entry options. Furthermore, if the user is using a tablet, the collection unit can provide a data collection method optimized for touch operation. This improves collection efficiency by providing the optimal data collection method according to the user's device.

[0060] The suggestion unit can analyze the user's past proposal history and select the optimal proposal method. For example, it can analyze the patterns of proposals that the user has accepted in the past and present similar proposals with priority. It can also analyze the reasons for proposals that the user has rejected in the past and avoid similar proposals. Furthermore, it can predict and propose the optimal proposal method for a specific time period from the user's past proposal history. This improves the proposal acceptance rate by providing the optimal proposal method based on the user's past proposal history.

[0061] The analysis unit can build a feedback loop to improve the accuracy of the analysis based on the user's past analysis results. For example, it can analyze feedback provided by the user in the past and optimize the analysis algorithm. It can also improve the accuracy of the analysis by referring to the user's past analysis results. Furthermore, it can collect user feedback in real time and reflect it in the analysis results. This improves the accuracy of the analysis by providing an optimal analysis based on the user's past analysis results.

[0062] The reception unit can analyze the user's input content in real time and dynamically generate input fields based on the input content. For example, if the user inputs a specific keyword, related input fields can be automatically added. Also, if the user changes the input content, the input fields can be dynamically adjusted. Furthermore, if the user completes input, the next input field can be automatically displayed. This improves input efficiency by providing dynamic input fields according to the user's input content.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The reception unit inputs company data and information about services. Company data includes sales data, customer data, product data, etc. The user inputs information about their company database and services. Step 2: The collection unit collects data based on the information entered by the reception unit. The collection unit collects data such as product information, customer information, past inquiry history, product usage status, and customer feedback. The collection unit can also automatically collect this data and store it in a database. Step 3: The analysis unit analyzes the data collected by the collection unit. Using data mining techniques and machine learning algorithms, the analysis unit extracts patterns from the collected data and identifies the optimal response method. Step 4: The proposal unit proposes the optimal response method based on the analysis results obtained by the analysis unit. The proposal unit proposes methods for troubleshooting the product and following up with the customer, and provides the user with specific response and follow-up procedures. The processing by the proposal unit can also be performed using an AI model.

[0065] (Example 2) A data collection and analysis system according to an embodiment of the present invention automatically collects and analyzes information about a company's data and services, enabling rapid responses to inquiries. In the data collection and analysis system, a user inputs information about the company's data and services, and the system automatically collects and analyzes the data and proposes optimal responses. For example, in a data collection and analysis system, a user inputs information about the company's database and services, including product information, customer information, and past inquiry history. This information is entered into the system. The data collection and analysis system then automatically collects related data based on the input information, such as product usage status and customer feedback. The collected data is analyzed by the system to identify the optimal response to the inquiry. Based on the analysis results, the data collection and analysis system then proposes specific response methods to the user, such as product troubleshooting methods and customer follow-up methods. This allows the user to respond to inquiries quickly and efficiently. The data collection and analysis system allows users to utilize their company's data and services to respond to inquiries quickly and efficiently. For example, appropriate responses to various inquiries, such as product troubleshooting and customer follow-up, are possible. In addition, because the system automatically collects and analyzes data, users can respond quickly and effortlessly, which is expected to improve customer satisfaction and operational efficiency.

[0066] A data collection and analysis system according to an embodiment includes a reception unit, a collection unit, an analysis unit, and a proposal unit. The reception unit inputs company data and information about services. The company data includes, but is not limited to, sales data, customer data, and product data. For example, a user inputs information about the company database and services into the reception unit. The collection unit collects data based on the information input by the reception unit. The collection unit collects data such as product information, customer information, and past inquiry history. The collection unit can also automatically collect data such as product usage and customer feedback. For example, the collection unit monitors product usage and collects data. The collection unit can also collect customer feedback and store it in a database. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit identifies an optimal response method for an inquiry based on the collected data. For example, the analysis unit uses data mining technology to extract patterns from the collected data and identify the optimal response method. The analysis unit can also analyze the collected data and identify the optimal response method using a machine learning algorithm. The suggestion unit proposes an optimal response method based on the analysis results obtained by the analysis unit. The suggestion unit proposes, for example, a product troubleshooting method or a customer follow-up method. For example, the suggestion unit proposes a product troubleshooting method and provides specific response procedures to the user. The suggestion unit can also propose a customer follow-up method and provide specific follow-up procedures to the user. This allows the data collection and analysis system according to the embodiment to enable users to respond to inquiries quickly and efficiently. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may propose a response method using an AI model that inputs the analysis results obtained by the analysis unit and outputs an optimal response method.

[0067] The collection unit may collect data on product information, customer information, and past inquiry history. Product information includes, but is not limited to, product specifications, product usage methods, and product lifecycles. For example, the collection unit may collect product specification information and store it in a database. The collection unit may also collect information on product usage methods and store it in a database. The collection unit may also collect information on product lifecycles and store it in a database. Customer information includes, but is not limited to, customer attribute information, purchase history, and customer feedback. For example, the collection unit may collect customer attribute information and store it in a database. The collection unit may also collect customer purchase history and store it in a database. The collection unit may also collect customer feedback and store it in a database. Past inquiry history includes, but is not limited to, inquiry content, response history, and resolution status. For example, the collection unit may collect past inquiry content and store it in a database. The collection unit may also collect past response history and store it in a database. The collection unit may also collect past resolution statuses and store it in a database. This enables more accurate analysis by collecting a variety of data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input product information, customer information, and past inquiry history data into the generation AI and have the generation AI collect the data.

[0068] The analysis unit can identify a response method for an inquiry based on the collected data. The analysis unit, for example, identifies an optimal response method for an inquiry based on the collected data. For example, the analysis unit can use data mining technology to extract patterns from the collected data and identify an optimal response method. The analysis unit can also analyze the collected data using a machine learning algorithm to identify an optimal response method. For example, the analysis unit can analyze past inquiry history and identify a response method for similar inquiries. The analysis unit can also analyze customer feedback and identify a response method that meets the customer's needs. Furthermore, the analysis unit can analyze product usage and identify a troubleshooting method for the product. This allows the analysis unit to identify an optimal response method, enabling a prompt response. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI identify an optimal response method.

[0069] The suggestion unit can suggest a troubleshooting method for the product and a follow-up method for the customer based on the analysis results. The suggestion unit, for example, suggests a troubleshooting method for the product based on the analysis results. For example, the suggestion unit suggests a troubleshooting method for the product and indicates specific response procedures to the user. The suggestion unit can also suggest a follow-up method for the customer based on the analysis results. For example, the suggestion unit suggests a follow-up method for the customer and indicates specific follow-up procedures to the user. The suggestion unit can also make a product improvement proposal based on the analysis results. For example, the suggestion unit makes a product improvement proposal and indicates specific improvement procedures to the user. In this way, the suggestion unit suggests a specific response method, allowing the user to respond quickly. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the analysis results to a generation AI and cause the generation AI to propose an optimal response method.

[0070] The reception unit can estimate the user's emotions and adjust the information input interface based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick information input. This improves input efficiency by providing an interface tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0071] The reception unit can analyze the user's past input history and suggest an input method. For example, the reception unit can automatically display information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest information that will be used in a specific time period based on the user's past input history. This improves input efficiency by suggesting the optimal input method based on the past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI suggest the optimal input method.

[0072] The reception unit can customize input items based on the user's work situation and areas of interest when inputting information. For example, when the user is performing a specific task, the reception unit prioritizes displaying input items related to that task. The reception unit can also automatically suggest related input items based on the user's areas of interest. Furthermore, the reception unit can dynamically adjust the required input items according to the user's work situation. This improves input efficiency by providing input items according to the user's work situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's work situation and areas of interest into a generation AI and have the generation AI customize the input items.

[0073] When inputting information, the reception unit can select the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs information using voice recognition technology. Furthermore, if the user selects text input, the reception unit can prioritize keyboard input. Furthermore, if the user selects image input, the reception unit can input information using image recognition technology. This improves input efficiency by providing the optimal means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data to a generation AI and have the generation AI select the optimal input means.

[0074] The reception unit can estimate the user's emotions and determine the priority of information to be input based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit prioritizes input of important information. Furthermore, when the user is relaxed, the reception unit can also input detailed information. Furthermore, when the user is in a hurry, the reception unit can quickly input the most important information. This improves input efficiency by determining the priority of information according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0075] When inputting information, the reception unit can prioritize inputting highly relevant information taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes inputting information related to that area. The reception unit can also automatically suggest relevant information based on the user's current location. Furthermore, the reception unit can select optimal input items based on the user's geographical location information. This improves input efficiency by prioritizing input of information based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to a generation AI and cause the generation AI to select highly relevant information.

[0076] The reception unit can analyze the user's social media activity and input relevant information when inputting information. The reception unit can suggest relevant input items based on, for example, information shared by the user on social media. The reception unit can also analyze the user's social media activity and automatically input relevant information. Furthermore, the reception unit can also input relevant information by referring to the activity of the user's friends on social media. This improves input efficiency by inputting information based on the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media activity data to a generation AI and cause the generation AI to input relevant information.

[0077] The reception unit can customize the input method by reflecting the user's past feedback when inputting information. For example, the reception unit can suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also adjust the input interface by reflecting the user's past feedback. Furthermore, the reception unit can analyze the user's feedback history and select the optimal input means. This improves input efficiency by providing an input method based on the user's past feedback. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI customize the input method.

[0078] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit delays data collection when the user is stressed. The collection unit can also quickly collect data when the user is relaxed. Furthermore, the collection unit can optimally collect data when the user is in a hurry. This improves collection efficiency by providing the timing of data collection according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0079] The collection unit can analyze the user's past data collection history and select the optimal collection method. The collection unit can, for example, suggest the optimal collection method based on data collected by the user in the past. The collection unit can also analyze the user's past data collection history and select an efficient collection method. Furthermore, the collection unit can also suggest the optimal collection timing based on the user's data collection history. This improves collection efficiency by providing the optimal collection method based on the user's past data collection history. Some or all of the above-described processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the user's past data collection history data into the generation AI and cause the generation AI to select the optimal collection method.

[0080] The collection unit can perform filtering based on the user's current work status and areas of interest when collecting data. For example, if the user is performing a specific task, the collection unit prioritizes collecting data related to that task. The collection unit can also filter related data based on the user's areas of interest. Furthermore, the collection unit can dynamically adjust the required data according to the user's work status. This improves collection efficiency by collecting data based on the user's work status and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's work status and areas of interest to the generation AI and have the generation AI perform filtering.

[0081] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user selects voice input, the collection unit can collect data using voice recognition technology. Furthermore, if the user selects text input, the collection unit can also collect data using text analysis technology. Furthermore, if the user selects image input, the collection unit can also collect data using image recognition technology. This improves collection efficiency by providing the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input user input method data into a generation AI and have the generation AI select the optimal collection means.

[0082] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit prioritizes collecting important data. The collection unit can also collect detailed data when the user is relaxed. Furthermore, when the user is in a hurry, the collection unit can quickly collect the most important data. This improves collection efficiency by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0083] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting data related to that area. The collection unit can also automatically suggest relevant data based on the user's current location. Furthermore, the collection unit can select optimal data based on the user's geographical location information. This improves collection efficiency by prioritizing the collection of data based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data into the generation AI and cause the generation AI to collect highly relevant data.

[0084] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit collects related data based on information shared by the user on social media. The collection unit can also analyze the user's social media activities and automatically collect related data. Furthermore, the collection unit can also collect related data by referring to the activities of the user's friends on social media. This improves collection efficiency by collecting data based on the user's social media activities. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect related data.

[0085] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit can suggest an optimal collection method based on feedback provided by the user in the past. The collection unit can also adjust the collection interface by reflecting the user's past feedback. Furthermore, the collection unit can analyze the user's feedback history and select an optimal collection means. This improves collection efficiency by providing a collection method based on the user's past feedback. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and have the generation AI customize the collection method.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. This improves understanding of the analysis by providing analysis results that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0087] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on general data. Furthermore, the analysis unit can dynamically adjust the depth of the analysis according to the importance of the data. This improves the efficiency of the analysis by providing an analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0088] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a specific analysis algorithm to product information. The analysis unit can also apply a different analysis algorithm to customer information. Furthermore, the analysis unit can apply a dedicated analysis algorithm to past inquiry history. This improves the accuracy of the analysis by providing an optimal analysis according to the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the data category into the generation AI and have the generation AI apply different analysis algorithms.

[0089] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. Furthermore, the analysis unit can perform a more accurate analysis by utilizing the user's past analysis results. This improves the accuracy of the analysis by providing an optimal analysis based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0090] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a longer analysis result with detailed explanations. If the user is excited, the analysis unit can also provide an analysis result with visually stimulating effects. This improves comprehension of the analysis by providing the length of the analysis result according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0091] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit can prioritize analysis of urgent data. The analysis unit can also analyze periodic data with normal priority. Furthermore, the analysis unit can dynamically adjust the priority of analysis according to the time of data submission. This improves the efficiency of analysis by providing the priority of analysis according to the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of data submission to the generation AI and have the generation AI determine the priority of analysis.

[0092] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This improves the efficiency of analysis by providing an analysis order based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0093] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit uses a lot of technical terminology for users with high levels of expertise. The analysis unit can also provide explanations in simpler terms for users with low levels of expertise. Furthermore, the analysis unit can dynamically adjust the use of technical terminology according to the user's level of expertise. This improves understanding of the analysis by providing analysis results according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0094] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions that focus on the main points. This improves understanding of suggestions by providing suggestions that correspond to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way suggestions are expressed.

[0095] The suggestion unit can adjust the level of detail of the proposal based on the importance of the response method when making a proposal. For example, the suggestion unit makes a detailed proposal for an important response method. The suggestion unit can also make a simplified proposal for a general response method. Furthermore, the suggestion unit can dynamically adjust the level of detail of the proposal according to the importance of the response method. This improves the efficiency of the proposal by providing a proposal according to the importance of the response method. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input importance data of the response method to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0096] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the response method. For example, the proposal unit applies a specific proposal algorithm to product problems. The proposal unit can also apply a different proposal algorithm to customer follow-up. Furthermore, the proposal unit can select the optimal proposal algorithm depending on the content of the inquiry. This improves the accuracy of the proposal by providing the optimal proposal depending on the category of the response method. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input category data of the response method into the generation AI and cause the generation AI to apply different proposal algorithms.

[0097] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, optimizes the proposal algorithm based on the user's past proposal results. The suggestion unit can also improve the accuracy of the proposal by referring to the user's past proposal results. Furthermore, the suggestion unit can make more accurate suggestions by utilizing the user's past proposal results. This improves the accuracy of the proposal by providing optimal suggestions based on the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0098] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short, to-the-point suggestions. If the user is relaxed, the suggestion unit can also provide longer suggestions with detailed explanations. If the user is excited, the suggestion unit can also provide suggestions with visually stimulating effects. This improves the understanding of the suggestions by providing suggestions with lengths that correspond to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestions.

[0099] The suggestion unit can determine the priority of the proposals based on the submission time of the response methods when making the proposals. For example, the suggestion unit can prioritize proposals for emergency response methods. The suggestion unit can also propose regular response methods with normal priority. Furthermore, the suggestion unit can dynamically adjust the priority of the proposals according to the submission time of the response methods. This improves the efficiency of the proposals by providing the priority of the proposals according to the submission time of the response methods. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input data on the submission time of the response methods into the generation AI and cause the generation AI to determine the priority of the proposals.

[0100] The suggestion unit can adjust the order of suggestions based on the relevance of the response methods when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant response methods. The suggestion unit can also postpone suggesting less relevant response methods. Furthermore, the suggestion unit can dynamically adjust the order of suggestions based on the relevance of the response methods. This improves the efficiency of suggestions by providing an order of suggestions based on the relevance of the response methods. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input relevance data of the response methods to a generation AI and cause the generation AI to adjust the order of suggestions.

[0101] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, the suggestion unit uses a lot of technical terminology for a user with high level of expertise. The suggestion unit can also provide explanations in simpler terms for a user with low level of expertise. Furthermore, the suggestion unit can dynamically adjust the use of technical terminology according to the user's level of expertise. This improves understanding of the proposal by providing a proposal according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, collection unit, analysis unit, and suggestion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit allows a user to input information about the company's data and services using the reception device 38 of the smart device 14. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects data based on the input information. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests an optimal response method based on the analysis results. The suggestion unit is also realized by the control unit 46A of the smart device 14 and can show the user specific response procedures. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, collection unit, analysis unit, and suggestion unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit allows a user to input information about their company's data and services using the microphone 238 of the smart glasses 214. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects data based on the input information. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests an optimal response method based on the analysis results. The suggestion unit is also realized by the control unit 46A of the smart glasses 214 and can provide the user with specific response procedures. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, collection unit, analysis unit, and proposal unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit allows the user to input information about the company's data and services using the microphone 238 of the headset type terminal 314. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects data based on the input information. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal response method based on the analysis results. The proposal unit is also realized by the control unit 46A of the headset type terminal 314 and can present specific response procedures to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, collection unit, analysis unit, and proposal unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit allows the user to input information about the company's data and services using the microphone 238 of the robot 414. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects data based on the input information. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal response method based on the analysis results. The proposal unit is also realized by the control unit 46A of the robot 414 and can present specific response procedures to the user.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The reception unit can monitor the user's input speed in real time and dynamically adjust the interface according to the input speed. For example, if the user is inputting quickly, the reception unit can automatically enlarge the input field to smooth the input flow. Alternatively, if the user is inputting slowly, the reception unit can shrink the input field and display detailed guides or tips. Furthermore, if the user pauses input, the reception unit can display a pop-up message to encourage the user to resume input. This improves input efficiency by providing an interface that adapts to the user's input speed.

[0104] The collection unit can customize the data collection method based on the type of device the user is using. For example, if the user is using a smartphone, the collection unit can provide a mobile-friendly data collection method. Alternatively, if the user is using a desktop, the collection unit can provide detailed data entry options. Furthermore, if the user is using a tablet, the collection unit can provide a data collection method optimized for touch operation. This improves collection efficiency by providing the optimal data collection method according to the user's device.

[0105] The analysis unit can estimate the user's emotions and adjust the presentation order of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the most important analysis results can be presented first. If the user is relaxed, detailed analysis results can be presented sequentially. Furthermore, if the user is in a hurry, concise analysis results that focus on the main points can be presented first. This improves the user's understanding of the analysis by providing an order of presentation of analysis results according to the user's emotions.

[0106] The suggestion unit can analyze the user's past proposal history and select the optimal proposal method. For example, it can analyze the patterns of proposals that the user has accepted in the past and present similar proposals with priority. It can also analyze the reasons for proposals that the user has rejected in the past and avoid similar proposals. Furthermore, it can predict and propose the optimal proposal method for a specific time period from the user's past proposal history. This improves the proposal acceptance rate by providing the optimal proposal method based on the user's past proposal history.

[0107] The reception unit can estimate the user's emotions and provide input feedback based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide positive feedback to encourage the user. If the user is relaxed, the reception unit can provide detailed feedback to improve input accuracy. Furthermore, if the user is in a hurry, the reception unit can provide quick feedback to support input speed. Thus, by providing feedback according to the user's emotions, input efficiency is improved.

[0108] The collection unit can estimate the user's emotions and adjust the data collection method based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can provide a simple data collection method to reduce the user's burden. Alternatively, if the user is relaxed, the collection unit can provide a detailed data collection method to collect more information. Furthermore, if the user is in a hurry, the collection unit can provide a quick data collection method to improve the speed of collection. Thus, by providing a data collection method according to the user's emotions, the efficiency of collection is improved.

[0109] The analysis unit can build a feedback loop to improve the accuracy of the analysis based on the user's past analysis results. For example, it can analyze feedback provided by the user in the past and optimize the analysis algorithm. It can also improve the accuracy of the analysis by referring to the user's past analysis results. Furthermore, it can collect user feedback in real time and reflect it in the analysis results. This improves the accuracy of the analysis by providing an optimal analysis based on the user's past analysis results.

[0110] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion timing can be delayed. Also, if the user is relaxed, the suggestion timing can be advanced. Furthermore, if the user is in a hurry, the suggestion can be made at the optimal timing. In this way, by providing the timing of suggestions according to the user's emotions, the acceptance rate of suggestions can be improved.

[0111] The reception unit can analyze the user's input content in real time and dynamically generate input fields based on the input content. For example, if the user inputs a specific keyword, related input fields can be automatically added. Also, if the user changes the input content, the input fields can be dynamically adjusted. Furthermore, if the user completes input, the next input field can be automatically displayed. This improves input efficiency by providing dynamic input fields according to the user's input content.

[0112] The suggestion unit can estimate the user's emotions and customize the content of the suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can provide simple and easy-to-implement suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions to deepen understanding of the user. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions that can be implemented quickly. In this way, the suggestion unit can provide the content of suggestions according to the user's emotions, thereby improving the acceptance rate of suggestions.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The reception unit inputs company data and information about services. Company data includes sales data, customer data, product data, etc. The user inputs information about their company database and services. Step 2: The collection unit collects data based on the information entered by the reception unit. The collection unit collects data such as product information, customer information, past inquiry history, product usage status, and customer feedback. The collection unit can also automatically collect this data and store it in a database. Step 3: The analysis unit analyzes the data collected by the collection unit. Using data mining techniques and machine learning algorithms, the analysis unit extracts patterns from the collected data and identifies the optimal response method. Step 4: The proposal unit proposes the optimal response method based on the analysis results obtained by the analysis unit. The proposal unit proposes methods for troubleshooting the product and following up with the customer, and provides the user with specific response and follow-up procedures. The processing by the proposal unit can also be performed using an AI model.

[0115] 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.

[0116] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] [Explanation of symbols]

[0187] 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 reception desk where you can input your company data and information about your services; a collection unit that collects data based on the information input by the reception unit; an analysis unit that analyzes the data collected by the collection unit; a proposal unit that proposes a response method based on the analysis result obtained by the analysis unit. A system characterized by:

2. The collecting unit Collect product information, customer information, and past inquiry history data 2. The system of claim 1.

3. The analysis unit Use the data collected to identify how to respond to inquiries 2. The system of claim 1.

4. The proposal unit Based on the analysis, recommend ways to troubleshoot the product and follow up with customers 2. The system of claim 1.

5. The reception unit Estimates a user's emotions and adjusts the information input interface based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit Analyzes the user's past input history and suggests input methods 2. The system of claim 1.

7. The reception unit Customize input fields based on the user's business situation and interests as they enter information 2. The system of claim 1.

8. The reception unit When entering information, select the most appropriate input method depending on the user's input method.

2. The system of claim 1.

Images