system

Abstract

The system according to the embodiment aims to provide a simple consultation service for work-related worries and questions and to provide appropriate advice. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives consultation content. The analysis unit analyzes the consultation content received by the reception unit. The provision unit provides advice based on the content analyzed by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology lacks a simple way to consult about work-related worries and questions, making it difficult to obtain appropriate advice.

[0005] The system according to the embodiment aims to provide a simple consultation service for work-related worries and questions and to provide appropriate advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives consultation content. The analysis unit analyzes the consultation content received by the reception unit. The provision unit provides advice based on the content analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows for easy consultation about work-related worries and questions and provides appropriate advice. [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 simple work-related consultation system according to an embodiment of the present invention allows users to easily seek advice about work-related worries and concerns. This system allows users to input information such as distrust in conversations with their superiors, doubts about colleagues, and worries within the company. AI analyzes the input content and provides appropriate advice and solutions. For example, when a user inputs a specific problem such as, "My superior often ignores my opinions. What should I do?", the input content is sent to the AI. The AI ​​then analyzes the input consultation content. The AI ​​uses natural language processing technology to understand the user's worries and concerns and generate appropriate advice. For example, the system may provide advice such as, "To improve communication with your superior, it is effective to prepare materials in advance when making specific proposals." Furthermore, the AI ​​can learn from past consultations and solutions to provide more accurate advice. For example, it can generate more appropriate advice by referring to the consultations and solutions of other users with similar worries. This service allows users to easily consult about work-related worries and reduce stress. The AI's advice is specific and practical, helping users solve their problems. For example, it may include specific methods for improving communication with superiors or advice for smoothing relationships with colleagues. In this way, a chat service that allows simple consultation about work-related worries provides an environment where users can easily seek advice, thereby reducing work-related stress. As a result, the work-related worries simple consultation system can quickly and accurately resolve users' worries.

[0029] A simple work-related consultation system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives consultation content input by a user. The consultation content input by the user includes, but is not limited to, for example, distrust in conversations with a superior, doubts about a colleague, and worries within the company. The reception unit, for example, receives text data input by the user and transmits it to the analysis unit. The analysis unit analyzes the consultation content received by the reception unit using AI. The analysis unit, for example, uses natural language processing technology to understand the user's worries and questions and generate appropriate advice. For example, the analysis unit uses a text generation AI (e.g., LLM) to analyze the user's consultation content and generate advice. The analysis unit can also learn from past consultation content and solutions to provide more accurate advice. For example, the analysis unit generates more appropriate advice by referring to the consultation content and solutions of other users with similar worries. The provision unit provides the user with the advice generated by the analysis unit. For example, the provision unit provides the user with specific methods for improving communication with a superior or advice for smoothing relationships with colleagues. For example, the providing unit may provide the user with advice such as, "In order to improve communication with your boss, it is effective to prepare materials in advance when making a specific proposal." In this way, the work-related simple consultation system according to the embodiment can quickly and accurately solve the user's worries.

[0030] Furthermore, the work-related simple consultation system includes a learning unit that learns the content of past consultations. The learning unit uses AI to learn the content of past consultations and their solutions. For example, the learning unit stores the content of past consultations and their solutions in a database, and the analysis unit references this to provide more accurate advice. For example, the learning unit analyzes the content of past consultations and their solutions, and provides data for providing appropriate advice to other users with similar concerns. In this way, the learning unit can provide more accurate advice by learning the content of past consultations.

[0031] Furthermore, the work-related simple consultation system includes an anonymization unit that protects the user's privacy. The anonymization unit anonymizes the consultation content to protect the user's privacy. The anonymization unit protects the user's privacy, for example, by deleting the user's personal information and masking the data. For example, the anonymization unit anonymizes the consultation content by deleting personal information such as the user's name and address. The anonymization unit can also protect the user's privacy by generating pseudo data. For example, the anonymization unit anonymizes the consultation content by replacing the user's personal information with pseudo data. In this way, the anonymization unit protects the user's privacy and provides an environment where the user can receive consultation with peace of mind.

[0032] The analysis unit can analyze the consultation content using natural language processing technology. The analysis unit uses AI to analyze the consultation content using natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis, for example. For example, the analysis unit uses morphological analysis to analyze words in the consultation content and understand their meaning. The analysis unit can also use grammatical analysis to analyze the grammatical structure of the consultation content and understand their meaning. Furthermore, the analysis unit can also use semantic analysis to analyze the meaning of the consultation content and generate appropriate advice. For example, the analysis unit uses semantic analysis to understand the meaning of the consultation content and generate appropriate advice. As a result, the analysis unit's use of natural language processing technology improves the accuracy of the analysis of the consultation content.

[0033] The providing unit can generate specific advice. The providing unit generates specific advice using AI. The providing unit generates specific advice based on, for example, the content of the user's consultation. For example, the providing unit provides the user with specific methods for improving communication with their boss or advice for smoothing relationships with colleagues. For example, the providing unit provides the user with advice such as, "In order to improve communication with your boss, it is effective to prepare materials in advance when making a specific proposal." In this way, the providing unit generates specific advice to help the user solve their problem.

[0034] The learning unit can learn past consultation contents and their solutions. The learning unit uses AI to learn past consultation contents and their solutions. For example, the learning unit stores past consultation contents and their solutions in a database, and the analysis unit references this to provide more accurate advice. For example, the learning unit analyzes past consultation contents and their solutions and provides data for providing appropriate advice to other users with similar concerns. In this way, the learning unit can provide more appropriate advice by learning past consultation contents and their solutions.

[0035] Furthermore, the work-related simple consultation system includes a reception unit that analyzes the user's past consultation history and selects the optimal reception method. The reception unit uses AI to analyze the user's past consultation history and selects the optimal reception method. For example, the reception unit preferentially suggests reception methods that the user has frequently used in the past. The reception unit can also suggest the optimal reception time slot based on the user's past consultation history. Furthermore, the reception unit can also select an appropriate reception method based on the content of the user's past consultation. For example, the reception unit analyzes the user's past consultation history and selects the optimal reception method. In this way, the reception unit can select the optimal reception method by analyzing the past consultation history.

[0036] Furthermore, the work-related simple consultation system includes a reception unit that filters consultation contents based on the user's current work situation and areas of interest when receiving the consultation contents. The reception unit uses AI to filter consultation contents based on the user's current work situation and areas of interest when receiving the consultation contents. The reception unit, for example, prioritizes receiving related consultation contents based on the user's work situation. The reception unit can also filter appropriate consultation contents based on the user's areas of interest. Furthermore, the reception unit can combine the user's work situation and areas of interest to receive optimal consultation contents. For example, the reception unit prioritizes receiving related consultation contents based on the user's work situation. As a result, the reception unit can prioritize receiving highly relevant consultation contents by filtering based on the user's work situation and areas of interest.

[0037] Furthermore, the work-related simple consultation system includes a reception unit that, when receiving consultation content, prioritizes receiving consultation content that is highly relevant, taking into account the user's geographical location information. The reception unit uses AI to prioritize receiving consultation content that is highly relevant, taking into account the user's geographical location information. For example, the reception unit prioritizes receiving related consultation content based on the user's geographical location information. The reception unit can also prioritize receiving consultation content related to problems that occurred in locations close to the user's current location. Furthermore, the reception unit can analyze the relevance between the user's geographical location information and the consultation content and accept the most appropriate consultation content. For example, the reception unit prioritizes receiving related consultation content based on the user's geographical location information. As a result, the reception unit can prioritize receiving consultation content that is highly relevant by taking into account the user's geographical location information.

[0038] Furthermore, the work-related simple consultation system includes a reception unit that analyzes the user's social media activity when receiving the consultation content and receives related consultation requests. The reception unit uses AI to analyze the user's social media activity when receiving the consultation content and receives related consultation requests. The reception unit, for example, prioritizes receiving related consultation requests based on the user's social media activity. The reception unit can also analyze the user's social media comments and suggest appropriate consultation requests. The reception unit can also analyze the relevance between the user's social media activity and the consultation content and receive the most appropriate consultation request. For example, the reception unit prioritizes receiving related consultation requests based on the user's social media activity. As a result, the reception unit can prioritize receiving related consultation requests by analyzing the user's social media activity.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the consultation content during analysis. The analysis unit uses AI to adjust the level of detail of the analysis based on the importance of the consultation content during analysis. For example, the analysis unit performs a detailed analysis on consultation content with a high level of importance. The analysis unit can also perform a simple analysis on consultation content with a low level of importance. Furthermore, the analysis unit can gradually adjust the level of detail of the analysis according to the importance of the consultation content. For example, the analysis unit performs a detailed analysis on consultation content with a high level of importance. In this way, the analysis unit can perform a detailed analysis on important consultation content by adjusting the level of detail of the analysis based on the importance of the consultation content.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the consultation content during analysis. The analysis unit uses AI to apply different analysis algorithms depending on the category of the consultation content during analysis. For example, the analysis unit applies an emotion analysis algorithm to consultation content related to human relationships. The analysis unit can also apply a business process analysis algorithm to consultation content related to work. Furthermore, the analysis unit can select and apply the optimal analysis algorithm for each category. For example, the analysis unit applies an emotion analysis algorithm to consultation content related to human relationships. In this way, the analysis unit can apply the optimal analysis algorithm depending on the category of the consultation content, thereby improving the accuracy of the analysis.

[0041] The analysis unit can determine the priority of analysis based on the time when the consultation content was submitted during analysis. The analysis unit uses AI to determine the priority of analysis based on the time when the consultation content was submitted during analysis. For example, the analysis unit prioritizes the analysis of the consultation content that was most recently submitted. The analysis unit can also postpone the analysis of consultation content that was submitted earlier. Furthermore, the analysis unit can gradually adjust the priority of analysis based on the time of submission. For example, the analysis unit prioritizes the analysis of the consultation content that was most recently submitted. In this way, the analysis unit can prioritize the analysis of the most recent consultation content by determining the priority of analysis based on the time when the consultation content was submitted.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the consultation contents during analysis. The analysis unit uses AI to adjust the order of analysis based on the relevance of the consultation contents during analysis. The analysis unit, for example, prioritizes analysis of consultation contents with high relevance. The analysis unit can also postpone analysis of consultation contents with low relevance. Furthermore, the analysis unit can gradually adjust the order of analysis based on the relevance of the consultation contents. For example, the analysis unit prioritizes analysis of consultation contents with high relevance. In this way, the analysis unit can prioritize analysis of consultation contents with high relevance by adjusting the order of analysis based on the relevance of the consultation contents.

[0043] The providing unit can adjust the level of detail of the advice based on the importance of the consultation content when providing the advice. The providing unit uses AI to adjust the level of detail of the advice based on the importance of the consultation content when providing the advice. For example, the providing unit provides detailed advice for consultation content with a high level of importance. The providing unit can also provide simple advice for consultation content with a low level of importance. Furthermore, the providing unit can gradually adjust the level of detail of the advice depending on the importance of the consultation content. For example, the providing unit provides detailed advice for consultation content with a high level of importance. In this way, the providing unit can provide detailed advice for important consultation content by adjusting the level of detail of the advice based on the importance of the consultation content.

[0044] The providing unit can apply different advice algorithms depending on the category of the consultation content when providing advice. The providing unit uses AI to apply different advice algorithms depending on the category of the consultation content when providing advice. For example, the providing unit applies an emotion analysis algorithm to consultation content related to human relationships. The providing unit can also apply a business process analysis algorithm to consultation content related to work. Furthermore, the providing unit can select and apply the optimal advice algorithm for each category. For example, the providing unit applies an emotion analysis algorithm to consultation content related to human relationships. In this way, the providing unit can apply the optimal advice algorithm depending on the category of the consultation content, thereby improving the accuracy of advice.

[0045] The providing unit can determine the priority of advice based on the time when the consultation content was submitted when providing the advice. The providing unit uses AI to determine the priority of advice based on the time when the consultation content was submitted when providing the advice. For example, the providing unit provides advice preferentially to the consultation content that was submitted most recently. The providing unit can also provide advice to consultation content that was submitted older at a later date. Furthermore, the providing unit can gradually adjust the priority of advice based on the time of submission. For example, the providing unit provides advice preferentially to the consultation content that was submitted most recently. In this way, the providing unit can provide advice preferentially to the most recent consultation content by determining the priority of advice based on the time when the consultation content was submitted.

[0046] The providing unit can adjust the order of advice based on the relevance of the consultation content when providing advice. The providing unit uses AI to adjust the order of advice based on the relevance of the consultation content when providing advice. For example, the providing unit provides advice preferentially for consultation content that is highly relevant. The providing unit can also provide advice for consultation content that is less relevant at a later date. Furthermore, the providing unit can gradually adjust the order of advice based on the relevance of the consultation content. For example, the providing unit provides advice preferentially for consultation content that is highly relevant. In this way, the providing unit can provide advice preferentially for consultation content that is highly relevant by adjusting the order of advice based on the relevance of the consultation content.

[0047] The learning unit can optimize the learning algorithm by referring to past learning data during learning. The learning unit uses AI to optimize the learning algorithm by referring to past learning data during learning. The learning unit, for example, selects an optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and improve the learning algorithm. Furthermore, the learning unit can adjust the parameters of the learning algorithm by referring to past learning data. For example, the learning unit selects an optimal learning algorithm based on past learning data. In this way, the learning unit can optimize the learning algorithm by referring to past learning data.

[0048] The learning unit can weight the learning data during learning based on the time when the consultation content was submitted. The learning unit uses AI to weight the learning data during learning based on the time when the consultation content was submitted. For example, the learning unit assigns a higher weight to the consultation content that was most recently submitted. The learning unit can also assign a lower weight to the consultation content that was submitted older. Furthermore, the learning unit can gradually adjust the weighting of the learning data based on the time of submission. For example, the learning unit assigns a higher weight to the consultation content that was most recently submitted. In this way, by weighting the learning data based on the time when the consultation content was submitted, the learning unit can assign a higher weight to the most recent consultation content.

[0049] The anonymization unit can select the optimal anonymization method by referring to the user's past consultation history at the time of anonymization. The anonymization unit uses AI to select the optimal anonymization method by referring to the user's past consultation history at the time of anonymization. The anonymization unit selects the optimal anonymization method, for example, based on the user's past consultation history. The anonymization unit can also adjust the strength of anonymization according to the content of the user's past consultation. Furthermore, the anonymization unit can analyze the user's past consultation history and apply the optimal anonymization method. For example, the anonymization unit selects the optimal anonymization method based on the user's past consultation history. In this way, the anonymization unit can select the optimal anonymization method by referring to the user's past consultation history.

[0050] Furthermore, in the work-related troubles simple consultation system, the anonymization unit selects the optimal anonymization method by taking into consideration the user's geographical location information during anonymization. The anonymization unit uses AI to select the optimal anonymization method by taking into consideration the user's geographical location information during anonymization. The anonymization unit selects the optimal anonymization method, for example, based on the user's geographical location information. The anonymization unit can also adjust the strength of anonymization based on the user's current location. Furthermore, the anonymization unit can analyze the user's geographical location information and apply the optimal anonymization method. For example, the anonymization unit selects the optimal anonymization method based on the user's geographical location information. In this way, the anonymization unit can select the optimal anonymization method by taking into consideration the user's geographical location information.

[0051] The analysis unit can adjust the level of detail of the analysis based on the importance of the consultation content during natural language processing. The analysis unit uses AI to adjust the level of detail of the analysis based on the importance of the consultation content during natural language processing. For example, the analysis unit performs a detailed analysis on consultation content with a high level of importance. The analysis unit can also perform a simple analysis on consultation content with a low level of importance. Furthermore, the analysis unit can gradually adjust the level of detail of the analysis according to the importance of the consultation content. For example, the analysis unit performs a detailed analysis on consultation content with a high level of importance. In this way, the analysis unit can perform a detailed analysis on important consultation content by adjusting the level of detail of the analysis based on the importance of the consultation content.

[0052] The analysis unit can determine the priority of analysis based on the time when the consultation content was submitted during natural language processing. The analysis unit uses AI to determine the priority of analysis based on the time when the consultation content was submitted during natural language processing. For example, the analysis unit prioritizes analyzing the consultation content that was submitted most recently. The analysis unit can also postpone analyzing consultation content that was submitted earlier. Furthermore, the analysis unit can gradually adjust the priority of analysis based on the time of submission. For example, the analysis unit prioritizes analyzing the consultation content that was submitted most recently. In this way, the analysis unit can prioritize analyzing the most recent consultation content by determining the priority of analysis based on the time when the consultation content was submitted.

[0053] The providing unit can adjust the level of detail of the advice based on the importance of the consultation content when generating the advice. The providing unit uses AI to adjust the level of detail of the advice based on the importance of the consultation content when generating the advice. For example, the providing unit generates detailed advice for consultation content with a high level of importance. The providing unit can also generate simple advice for consultation content with a low level of importance. Furthermore, the providing unit can gradually adjust the level of detail of the advice according to the importance of the consultation content. For example, the providing unit generates detailed advice for consultation content with a high level of importance. In this way, the providing unit can provide detailed advice for important consultation content by adjusting the level of detail of the advice based on the importance of the consultation content.

[0054] The providing unit can determine the priority of advice based on the time of submission of the consultation content when generating the advice. The providing unit uses AI to determine the priority of advice based on the time of submission of the consultation content when generating the advice. For example, the providing unit generates advice preferentially for the consultation content that was most recently submitted. The providing unit can also generate advice later for consultation content that was submitted earlier. Furthermore, the providing unit can gradually adjust the priority of advice based on the time of submission. For example, the providing unit generates advice preferentially for the consultation content that was most recently submitted. In this way, the providing unit can provide advice preferentially for the most recent consultation content by determining the priority of advice based on the time of submission of the consultation content.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] When accepting a user's consultation content, the reception unit refers to the user's past consultation history, and can respond more quickly to users who have previously made similar consultations. The reception unit can also evaluate the effectiveness of advice the user has received in the past and provide more effective advice preferentially. Furthermore, the reception unit can transfer the consultation content to a related expert based on the user's consultation content and provide expert advice. In this way, the reception unit can provide more effective advice by utilizing the user's past consultation history.

[0057] The learning unit can take into account the user's work situation and industry-specific problems when learning past consultation content and its solutions. For example, the learning unit can store common concerns and solutions in a database in a specific industry and provide appropriate advice to users in the same industry. The learning unit can also prioritize learning related consultation content based on the user's work situation. Furthermore, the learning unit can analyze the user's work situation and industry-specific problems and predict future trends and problems. This allows the learning unit to provide more appropriate advice by taking into account the user's work situation and industry-specific problems.

[0058] When accepting a user's consultation content, the reception unit can prioritize accepting highly relevant consultation content by taking into account the user's geographical location information. For example, the reception unit can prioritize accepting related consultation content based on the user's geographical location information. The reception unit can also prioritize accepting consultation content related to problems that occurred in locations close to the user's current location. Furthermore, the reception unit can analyze the relevance between the user's geographical location information and the consultation content and accept the most appropriate consultation content. In this way, the reception unit can prioritize accepting highly relevant consultation content by taking into account the user's geographical location information.

[0059] When learning past consultation contents and their solutions, the learning unit can analyze the user's social media activity and prioritize learning related consultation contents. For example, related consultation contents can be prioritized and learned from the user's social media activity. The learning unit can also analyze the user's comments on social media and select appropriate learning data. Furthermore, the learning unit can analyze the relevance between the user's social media activity and the consultation contents and select the most appropriate learning data. This allows the learning unit to prioritize learning related consultation contents by analyzing the user's social media activity.

[0060] When analyzing the consultation content using natural language processing technology, the analysis unit can apply different analysis algorithms depending on the category of the consultation content. For example, an emotion analysis algorithm can be applied to consultation content related to human relationships. Also, a business process analysis algorithm can be applied to consultation content related to work. Furthermore, the analysis unit can select and apply the optimal analysis algorithm for each category. This allows the analysis unit to improve analysis accuracy by applying the optimal analysis algorithm depending on the category of the consultation content.

[0061] When generating specific advice, the providing unit can refer to the user's past consultation history and prioritize providing advice that has been effective in the past. For example, the providing unit can evaluate the effectiveness of advice the user has received in the past and prioritize providing advice that has been effective. The providing unit can also select relevant advice from the user's past consultation history. Furthermore, the providing unit can analyze the user's past consultation history and provide optimal advice. This allows the providing unit to provide more effective advice by utilizing the user's past consultation history.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The reception unit accepts the consultation content entered by the user. The consultation content entered by the user may include distrust in conversations with superiors, doubts about colleagues, and worries within the company. The reception unit receives the text data entered by the user and sends it to the analysis unit. Step 2: The analysis unit uses AI to analyze the consultation content received by the reception unit. The analysis unit uses natural language processing technology to understand the user's concerns and questions and generate appropriate advice. For example, it uses text generation AI (e.g., LLM) to analyze the consultation content and generate advice. It also learns from past consultation content and solutions, and generates more appropriate advice by referring to the consultation content and solutions of other users with similar concerns. Step 3: The provision unit provides the user with the advice generated by the analysis unit. For example, it provides specific methods for improving communication with a superior or advice for smoothing relationships with colleagues. Specifically, it provides advice such as, "When making a specific proposal to improve communication with your superior, it is effective to prepare materials in advance."

[0064] (Example 2) A simple work-related consultation system according to an embodiment of the present invention allows users to easily seek advice about work-related worries and concerns. This system allows users to input information such as distrust in conversations with their superiors, doubts about colleagues, and worries within the company. AI analyzes the input content and provides appropriate advice and solutions. For example, when a user inputs a specific problem such as, "My superior often ignores my opinions. What should I do?", the input content is sent to the AI. The AI ​​then analyzes the input consultation content. The AI ​​uses natural language processing technology to understand the user's worries and concerns and generate appropriate advice. For example, the system may provide advice such as, "To improve communication with your superior, it is effective to prepare materials in advance when making specific proposals." Furthermore, the AI ​​can learn from past consultations and solutions to provide more accurate advice. For example, it can generate more appropriate advice by referring to the consultations and solutions of other users with similar worries. This service allows users to easily consult about work-related worries and reduce stress. The AI's advice is specific and practical, helping users solve their problems. For example, it may include specific methods for improving communication with superiors or advice for smoothing relationships with colleagues. In this way, a chat service that allows simple consultation about work-related worries provides an environment where users can easily seek advice, thereby reducing work-related stress. As a result, the work-related worries simple consultation system can quickly and accurately resolve users' worries.

[0065] A simple work-related consultation system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives consultation content input by a user. The consultation content input by the user includes, but is not limited to, for example, distrust in conversations with a superior, doubts about a colleague, and worries within the company. The reception unit, for example, receives text data input by the user and transmits it to the analysis unit. The analysis unit analyzes the consultation content received by the reception unit using AI. The analysis unit, for example, uses natural language processing technology to understand the user's worries and questions and generate appropriate advice. For example, the analysis unit uses a text generation AI (e.g., LLM) to analyze the user's consultation content and generate advice. The analysis unit can also learn from past consultation content and solutions to provide more accurate advice. For example, the analysis unit generates more appropriate advice by referring to the consultation content and solutions of other users with similar worries. The provision unit provides the user with the advice generated by the analysis unit. For example, the provision unit provides the user with specific methods for improving communication with a superior or advice for smoothing relationships with colleagues. For example, the providing unit may provide the user with advice such as, "In order to improve communication with your boss, it is effective to prepare materials in advance when making a specific proposal." In this way, the work-related simple consultation system according to the embodiment can quickly and accurately solve the user's worries.

[0066] Furthermore, the work-related simple consultation system includes a learning unit that learns the content of past consultations. The learning unit uses AI to learn the content of past consultations and their solutions. For example, the learning unit stores the content of past consultations and their solutions in a database, and the analysis unit references this to provide more accurate advice. For example, the learning unit analyzes the content of past consultations and their solutions, and provides data for providing appropriate advice to other users with similar concerns. In this way, the learning unit can provide more accurate advice by learning the content of past consultations.

[0067] Furthermore, the work-related simple consultation system includes an anonymization unit that protects the user's privacy. The anonymization unit anonymizes the consultation content to protect the user's privacy. The anonymization unit protects the user's privacy, for example, by deleting the user's personal information and masking the data. For example, the anonymization unit anonymizes the consultation content by deleting personal information such as the user's name and address. The anonymization unit can also protect the user's privacy by generating pseudo data. For example, the anonymization unit anonymizes the consultation content by replacing the user's personal information with pseudo data. In this way, the anonymization unit protects the user's privacy and provides an environment where the user can receive consultation with peace of mind.

[0068] The analysis unit can analyze the consultation content using natural language processing technology. The analysis unit uses AI to analyze the consultation content using natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis, for example. For example, the analysis unit uses morphological analysis to analyze words in the consultation content and understand their meaning. The analysis unit can also use grammatical analysis to analyze the grammatical structure of the consultation content and understand their meaning. Furthermore, the analysis unit can also use semantic analysis to analyze the meaning of the consultation content and generate appropriate advice. For example, the analysis unit uses semantic analysis to understand the meaning of the consultation content and generate appropriate advice. As a result, the analysis unit's use of natural language processing technology improves the accuracy of the analysis of the consultation content.

[0069] The providing unit can generate specific advice. The providing unit generates specific advice using AI. The providing unit generates specific advice based on, for example, the content of the user's consultation. For example, the providing unit provides the user with specific methods for improving communication with their boss or advice for smoothing relationships with colleagues. For example, the providing unit provides the user with advice such as, "In order to improve communication with your boss, it is effective to prepare materials in advance when making a specific proposal." In this way, the providing unit generates specific advice to help the user solve their problem.

[0070] The learning unit can learn past consultation contents and their solutions. The learning unit uses AI to learn past consultation contents and their solutions. For example, the learning unit stores past consultation contents and their solutions in a database, and the analysis unit references this to provide more accurate advice. For example, the learning unit analyzes past consultation contents and their solutions and provides data for providing appropriate advice to other users with similar concerns. In this way, the learning unit can provide more appropriate advice by learning past consultation contents and their solutions.

[0071] Furthermore, the work-related simple consultation system includes a reception unit that estimates the user's emotions and adjusts the timing of accepting the consultation content based on the estimated user emotions. The reception unit uses AI to estimate the user's emotions and adjusts the timing of accepting the consultation content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit immediately accepts the consultation content. Furthermore, if the user is relaxed, the reception unit can also accept the consultation content at an appropriate time. Furthermore, if the user is in a hurry, the reception unit can quickly accept the consultation content. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. As a result, the reception unit can adjust the timing of accepting the consultation content according to the user's emotions, thereby accepting the consultation content at a more appropriate time.

[0072] Furthermore, the work-related simple consultation system includes a reception unit that analyzes the user's past consultation history and selects the optimal reception method. The reception unit uses AI to analyze the user's past consultation history and selects the optimal reception method. For example, the reception unit preferentially suggests reception methods that the user has frequently used in the past. The reception unit can also suggest the optimal reception time slot based on the user's past consultation history. Furthermore, the reception unit can also select an appropriate reception method based on the content of the user's past consultation. For example, the reception unit analyzes the user's past consultation history and selects the optimal reception method. In this way, the reception unit can select the optimal reception method by analyzing the past consultation history.

[0073] Furthermore, the work-related simple consultation system includes a reception unit that filters consultation contents based on the user's current work situation and areas of interest when receiving the consultation contents. The reception unit uses AI to filter consultation contents based on the user's current work situation and areas of interest when receiving the consultation contents. The reception unit, for example, prioritizes receiving related consultation contents based on the user's work situation. The reception unit can also filter appropriate consultation contents based on the user's areas of interest. Furthermore, the reception unit can combine the user's work situation and areas of interest to receive optimal consultation contents. For example, the reception unit prioritizes receiving related consultation contents based on the user's work situation. As a result, the reception unit can prioritize receiving highly relevant consultation contents by filtering based on the user's work situation and areas of interest.

[0074] Furthermore, the work-related simple consultation system includes a reception unit that estimates the user's emotions and determines the priority of the consultation contents to be received based on the estimated user emotions. The reception unit uses AI to estimate the user's emotions and determines the priority of the consultation contents to be received based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit prioritizes the consultation contents. Also, if the user is relaxed, the reception unit can also accept the consultation contents with normal priority. Furthermore, if the user is in a hurry, the reception unit can quickly accept the consultation contents. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. As a result, the reception unit can prioritize the consultation contents according to the user's emotions, thereby prioritizing important consultation contents.

[0075] Furthermore, the work-related simple consultation system includes a reception unit that, when receiving consultation content, prioritizes receiving consultation content that is highly relevant, taking into account the user's geographical location information. The reception unit uses AI to prioritize receiving consultation content that is highly relevant, taking into account the user's geographical location information. For example, the reception unit prioritizes receiving related consultation content based on the user's geographical location information. The reception unit can also prioritize receiving consultation content related to problems that occurred in locations close to the user's current location. Furthermore, the reception unit can analyze the relevance between the user's geographical location information and the consultation content and accept the most appropriate consultation content. For example, the reception unit prioritizes receiving related consultation content based on the user's geographical location information. As a result, the reception unit can prioritize receiving consultation content that is highly relevant by taking into account the user's geographical location information.

[0076] Furthermore, the work-related simple consultation system includes a reception unit that analyzes the user's social media activity when receiving the consultation content and receives related consultation requests. The reception unit uses AI to analyze the user's social media activity when receiving the consultation content and receives related consultation requests. The reception unit, for example, prioritizes receiving related consultation requests based on the user's social media activity. The reception unit can also analyze the user's social media comments and suggest appropriate consultation requests. The reception unit can also analyze the relevance between the user's social media activity and the consultation content and receive the most appropriate consultation request. For example, the reception unit prioritizes receiving related consultation requests based on the user's social media activity. As a result, the reception unit can prioritize receiving related consultation requests by analyzing the user's social media activity.

[0077] Furthermore, in the work-related simple consultation system, the analysis unit estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The analysis unit uses AI to estimate the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit uses a simple and easy-to-understand presentation method. The analysis unit can also provide detailed analysis results if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. For example, the analysis unit captures the user's facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. As a result, the analysis unit can adjust the way the analysis is presented based on the user's emotions and provide more appropriate analysis results.

[0078] The analysis unit can adjust the level of detail of the analysis based on the importance of the consultation content during analysis. The analysis unit uses AI to adjust the level of detail of the analysis based on the importance of the consultation content during analysis. For example, the analysis unit performs a detailed analysis on consultation content with a high level of importance. The analysis unit can also perform a simple analysis on consultation content with a low level of importance. Furthermore, the analysis unit can gradually adjust the level of detail of the analysis according to the importance of the consultation content. For example, the analysis unit performs a detailed analysis on consultation content with a high level of importance. In this way, the analysis unit can perform a detailed analysis on important consultation content by adjusting the level of detail of the analysis based on the importance of the consultation content.

[0079] The analysis unit can apply different analysis algorithms depending on the category of the consultation content during analysis. The analysis unit uses AI to apply different analysis algorithms depending on the category of the consultation content during analysis. For example, the analysis unit applies an emotion analysis algorithm to consultation content related to human relationships. The analysis unit can also apply a business process analysis algorithm to consultation content related to work. Furthermore, the analysis unit can select and apply the optimal analysis algorithm for each category. For example, the analysis unit applies an emotion analysis algorithm to consultation content related to human relationships. In this way, the analysis unit can apply the optimal analysis algorithm depending on the category of the consultation content, thereby improving the accuracy of the analysis.

[0080] Furthermore, in the work-related simple consultation system, the analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The analysis unit uses AI to estimate the user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit provides a short and to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. Furthermore, the analysis unit can provide a quick analysis result if the user is in a hurry. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. As a result, the analysis unit can adjust the length of the analysis according to the user's emotions and provide more appropriate analysis results.

[0081] The analysis unit can determine the priority of analysis based on the time when the consultation content was submitted during analysis. The analysis unit uses AI to determine the priority of analysis based on the time when the consultation content was submitted during analysis. For example, the analysis unit prioritizes the analysis of the consultation content that was most recently submitted. The analysis unit can also postpone the analysis of consultation content that was submitted earlier. Furthermore, the analysis unit can gradually adjust the priority of analysis based on the time of submission. For example, the analysis unit prioritizes the analysis of the consultation content that was most recently submitted. In this way, the analysis unit can prioritize the analysis of the most recent consultation content by determining the priority of analysis based on the time when the consultation content was submitted.

[0082] The analysis unit can adjust the order of analysis based on the relevance of the consultation contents during analysis. The analysis unit uses AI to adjust the order of analysis based on the relevance of the consultation contents during analysis. The analysis unit, for example, prioritizes analysis of consultation contents with high relevance. The analysis unit can also postpone analysis of consultation contents with low relevance. Furthermore, the analysis unit can gradually adjust the order of analysis based on the relevance of the consultation contents. For example, the analysis unit prioritizes analysis of consultation contents with high relevance. In this way, the analysis unit can prioritize analysis of consultation contents with high relevance by adjusting the order of analysis based on the relevance of the consultation contents.

[0083] Furthermore, in the work-related simple consultation system, the providing unit estimates the user's emotions and adjusts the way the advice is presented based on the estimated user emotions. The providing unit uses AI to estimate the user's emotions and adjusts the way the advice is presented based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit provides simple and easy-to-understand advice. The providing unit can also provide detailed advice if the user is relaxed. Furthermore, the providing unit can provide advice that focuses on the main points if the user is in a hurry. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. As a result, the providing unit can provide more appropriate advice by adjusting the way the advice is presented depending on the user's emotions.

[0084] The providing unit can adjust the level of detail of the advice based on the importance of the consultation content when providing the advice. The providing unit uses AI to adjust the level of detail of the advice based on the importance of the consultation content when providing the advice. For example, the providing unit provides detailed advice for consultation content with a high level of importance. The providing unit can also provide simple advice for consultation content with a low level of importance. Furthermore, the providing unit can gradually adjust the level of detail of the advice depending on the importance of the consultation content. For example, the providing unit provides detailed advice for consultation content with a high level of importance. In this way, the providing unit can provide detailed advice for important consultation content by adjusting the level of detail of the advice based on the importance of the consultation content.

[0085] The providing unit can apply different advice algorithms depending on the category of the consultation content when providing advice. The providing unit uses AI to apply different advice algorithms depending on the category of the consultation content when providing advice. For example, the providing unit applies an emotion analysis algorithm to consultation content related to human relationships. The providing unit can also apply a business process analysis algorithm to consultation content related to work. Furthermore, the providing unit can select and apply the optimal advice algorithm for each category. For example, the providing unit applies an emotion analysis algorithm to consultation content related to human relationships. In this way, the providing unit can apply the optimal advice algorithm depending on the category of the consultation content, thereby improving the accuracy of advice.

[0086] Furthermore, in the work-related simple consultation system, the providing unit estimates the user's emotions and adjusts the length of advice based on the estimated user emotions. The providing unit uses AI to estimate the user's emotions and adjusts the length of advice based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit provides short, to-the-point advice. The providing unit can also provide detailed advice if the user is relaxed. Furthermore, the providing unit can provide quick advice if the user is in a hurry. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. As a result, the providing unit can provide more appropriate advice by adjusting the length of advice according to the user's emotions.

[0087] The providing unit can determine the priority of advice based on the time when the consultation content was submitted when providing the advice. The providing unit uses AI to determine the priority of advice based on the time when the consultation content was submitted when providing the advice. For example, the providing unit provides advice preferentially to the consultation content that was submitted most recently. The providing unit can also provide advice to consultation content that was submitted older at a later date. Furthermore, the providing unit can gradually adjust the priority of advice based on the time of submission. For example, the providing unit provides advice preferentially to the consultation content that was submitted most recently. In this way, the providing unit can provide advice preferentially to the most recent consultation content by determining the priority of advice based on the time when the consultation content was submitted.

[0088] The providing unit can adjust the order of advice based on the relevance of the consultation content when providing advice. The providing unit uses AI to adjust the order of advice based on the relevance of the consultation content when providing advice. For example, the providing unit provides advice preferentially for consultation content that is highly relevant. The providing unit can also provide advice for consultation content that is less relevant at a later date. Furthermore, the providing unit can gradually adjust the order of advice based on the relevance of the consultation content. For example, the providing unit provides advice preferentially for consultation content that is highly relevant. In this way, the providing unit can provide advice preferentially for consultation content that is highly relevant by adjusting the order of advice based on the relevance of the consultation content.

[0089] Furthermore, in the work-related simple consultation system, the learning unit estimates the user's emotions and selects learning data based on the estimated user emotions. The learning unit uses AI to estimate the user's emotions and selects learning data based on the estimated user emotions. For example, if the user is feeling stressed, the learning unit preferentially selects learning data related to stress reduction. Also, if the user is relaxed, the learning unit can select learning data related to relaxation. Furthermore, if the user is in a hurry, the learning unit can quickly select learning data. For example, the learning unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. As a result, the learning unit can select more appropriate learning data by selecting learning data according to the user's emotions.

[0090] The learning unit can optimize the learning algorithm by referring to past learning data during learning. The learning unit uses AI to optimize the learning algorithm by referring to past learning data during learning. The learning unit, for example, selects an optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and improve the learning algorithm. Furthermore, the learning unit can adjust the parameters of the learning algorithm by referring to past learning data. For example, the learning unit selects an optimal learning algorithm based on past learning data. In this way, the learning unit can optimize the learning algorithm by referring to past learning data.

[0091] Furthermore, in the work-related simple consultation system, the learning unit estimates the user's emotions and adjusts the frequency of learning based on the estimated user emotions. The learning unit uses AI to estimate the user's emotions and adjusts the frequency of learning based on the estimated user emotions. For example, the learning unit increases the frequency of learning when the user is feeling stressed. The learning unit can also set the frequency of learning to the normal level when the user is relaxed. Furthermore, the learning unit can decrease the frequency of learning when the user is in a hurry. For example, the learning unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. As a result, the learning unit can set a more appropriate learning frequency by adjusting the frequency of learning according to the user's emotions.

[0092] The learning unit can weight the learning data during learning based on the time when the consultation content was submitted. The learning unit uses AI to weight the learning data during learning based on the time when the consultation content was submitted. For example, the learning unit assigns a higher weight to the consultation content that was most recently submitted. The learning unit can also assign a lower weight to the consultation content that was submitted older. Furthermore, the learning unit can gradually adjust the weighting of the learning data based on the time of submission. For example, the learning unit assigns a higher weight to the consultation content that was most recently submitted. In this way, by weighting the learning data based on the time when the consultation content was submitted, the learning unit can assign a higher weight to the most recent consultation content.

[0093] Furthermore, in the work-related simple consultation system, the anonymization unit estimates the user's emotions and adjusts the anonymization method based on the estimated user emotions. The anonymization unit uses AI to estimate the user's emotions and adjusts the anonymization method based on the estimated user emotions. For example, if the user is feeling stressed, the anonymization unit applies a strong anonymization method. Also, if the user is relaxed, the anonymization unit can apply a normal anonymization method. Furthermore, if the user is in a hurry, the anonymization unit can quickly perform anonymization. For example, the anonymization unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. As a result, the anonymization unit can adjust the anonymization method according to the user's emotions, thereby performing more appropriate anonymization.

[0094] The anonymization unit can select the optimal anonymization method by referring to the user's past consultation history at the time of anonymization. The anonymization unit uses AI to select the optimal anonymization method by referring to the user's past consultation history at the time of anonymization. The anonymization unit selects the optimal anonymization method, for example, based on the user's past consultation history. The anonymization unit can also adjust the strength of anonymization according to the content of the user's past consultation. Furthermore, the anonymization unit can analyze the user's past consultation history and apply the optimal anonymization method. For example, the anonymization unit selects the optimal anonymization method based on the user's past consultation history. In this way, the anonymization unit can select the optimal anonymization method by referring to the user's past consultation history.

[0095] Furthermore, in the work-related simple consultation system, the anonymization unit estimates the user's emotions and determines the anonymization priority based on the estimated user emotions. The anonymization unit uses AI to estimate the user's emotions and determines the anonymization priority based on the estimated user emotions. For example, if the user is feeling stressed, the anonymization unit prioritizes anonymization. Also, if the user is relaxed, the anonymization unit can perform anonymization with normal priority. Furthermore, if the user is in a hurry, the anonymization unit can quickly perform anonymization. For example, the anonymization unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. As a result, the anonymization unit determines the anonymization priority according to the user's emotions, allowing important anonymization to be prioritized.

[0096] Furthermore, in the work-related troubles simple consultation system, the anonymization unit selects the optimal anonymization method by taking into consideration the user's geographical location information during anonymization. The anonymization unit uses AI to select the optimal anonymization method by taking into consideration the user's geographical location information during anonymization. The anonymization unit selects the optimal anonymization method, for example, based on the user's geographical location information. The anonymization unit can also adjust the strength of anonymization based on the user's current location. Furthermore, the anonymization unit can analyze the user's geographical location information and apply the optimal anonymization method. For example, the anonymization unit selects the optimal anonymization method based on the user's geographical location information. In this way, the anonymization unit can select the optimal anonymization method by taking into consideration the user's geographical location information.

[0097] Furthermore, in the work-related simple consultation system, an analysis unit using natural language processing technology estimates the user's emotions and adjusts the natural language processing analysis method based on the estimated user emotions. The analysis unit uses AI to estimate the user's emotions and adjusts the natural language processing analysis method based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit uses a simple and easy-to-understand analysis method. The analysis unit can also use a detailed analysis method if the user is relaxed. Furthermore, the analysis unit can perform a quick analysis if the user is in a hurry. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. As a result, the analysis unit can adjust the natural language processing analysis method according to the user's emotions to provide more appropriate analysis results.

[0098] The analysis unit can adjust the level of detail of the analysis based on the importance of the consultation content during natural language processing. The analysis unit uses AI to adjust the level of detail of the analysis based on the importance of the consultation content during natural language processing. For example, the analysis unit performs a detailed analysis on consultation content with a high level of importance. The analysis unit can also perform a simple analysis on consultation content with a low level of importance. Furthermore, the analysis unit can gradually adjust the level of detail of the analysis according to the importance of the consultation content. For example, the analysis unit performs a detailed analysis on consultation content with a high level of importance. In this way, the analysis unit can perform a detailed analysis on important consultation content by adjusting the level of detail of the analysis based on the importance of the consultation content.

[0099] Furthermore, in the work-related simple consultation system, an analysis unit using natural language processing technology estimates the user's emotions and adjusts the display method of the natural language processing analysis results based on the estimated user emotions. The analysis unit uses AI to estimate the user's emotions and adjusts the display method of the natural language processing analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit uses a simple and easy-to-understand display method. Also, if the user is relaxed, the analysis unit can use a detailed display method. Furthermore, if the user is in a hurry, the analysis unit can quickly display the results. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. As a result, the analysis unit can adjust the display method of the natural language processing analysis results according to the user's emotions, thereby providing a more appropriate display.

[0100] The analysis unit can determine the priority of analysis based on the time when the consultation content was submitted during natural language processing. The analysis unit uses AI to determine the priority of analysis based on the time when the consultation content was submitted during natural language processing. For example, the analysis unit prioritizes analyzing the consultation content that was submitted most recently. The analysis unit can also postpone analyzing consultation content that was submitted earlier. Furthermore, the analysis unit can gradually adjust the priority of analysis based on the time of submission. For example, the analysis unit prioritizes analyzing the consultation content that was submitted most recently. In this way, the analysis unit can prioritize analyzing the most recent consultation content by determining the priority of analysis based on the time when the consultation content was submitted.

[0101] Furthermore, in the work-related simple consultation system, a unit for generating specific advice estimates the user's emotions and adjusts the method for generating the advice based on the estimated user emotions. The unit uses AI to estimate the user's emotions and adjusts the method for generating the advice based on the estimated user emotions. For example, if the user is feeling stressed, the unit generates simple and easy-to-understand advice. The unit can also generate detailed advice if the user is relaxed. Furthermore, the unit can quickly generate advice if the user is in a hurry. For example, the unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. This allows the unit to provide more appropriate advice by adjusting the method for generating advice according to the user's emotions.

[0102] The providing unit can adjust the level of detail of the advice based on the importance of the consultation content when generating the advice. The providing unit uses AI to adjust the level of detail of the advice based on the importance of the consultation content when generating the advice. For example, the providing unit generates detailed advice for consultation content with a high level of importance. The providing unit can also generate simple advice for consultation content with a low level of importance. Furthermore, the providing unit can gradually adjust the level of detail of the advice according to the importance of the consultation content. For example, the providing unit generates detailed advice for consultation content with a high level of importance. In this way, the providing unit can provide detailed advice for important consultation content by adjusting the level of detail of the advice based on the importance of the consultation content.

[0103] Furthermore, in the work-related simple consultation system, a providing unit that generates specific advice estimates the user's emotions and adjusts the display method of the advice based on the estimated user emotions. The providing unit uses AI to estimate the user's emotions and adjusts the display method of the advice based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit uses a simple and easy-to-understand display method. Also, if the user is relaxed, the providing unit can use a detailed display method. Furthermore, if the user is in a hurry, the providing unit can quickly display the advice. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. As a result, the providing unit can adjust the display method of the advice according to the user's emotions, thereby providing a more appropriate display.

[0104] The providing unit can determine the priority of advice based on the time of submission of the consultation content when generating the advice. The providing unit uses AI to determine the priority of advice based on the time of submission of the consultation content when generating the advice. For example, the providing unit generates advice preferentially for the consultation content that was most recently submitted. The providing unit can also generate advice later for consultation content that was submitted earlier. Furthermore, the providing unit can gradually adjust the priority of advice based on the time of submission. For example, the providing unit generates advice preferentially for the consultation content that was most recently submitted. In this way, the providing unit can provide advice preferentially for the most recent consultation content by determining the priority of advice based on the time of submission of the consultation content. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, learning unit, and anonymization 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 is realized by the control unit 46A of the smart device 14 and receives consultation content input by the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the consultation content using AI. The provision unit is realized by the control unit 46A of the smart device 14 and provides the analysis results to the user. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns past consultation content. The anonymization unit is realized by the control unit 46A of the smart device 14 and anonymizes the consultation content. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, learning unit, and anonymization 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 is realized by the control unit 46A of the smart glasses 214 and receives consultation details input by the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the consultation details using AI. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the analysis results to the user. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns past consultation details. The anonymization unit is realized by the control unit 46A of the smart glasses 214 and anonymizes the consultation details. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, learning unit, and anonymization 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 is realized by the control unit 46A of the headset type terminal 314 and receives consultation content input by a user. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the consultation content using AI. The provision unit is realized by the control unit 46A of the headset type terminal 314 and provides the analysis results to the user. The learning unit is realized by the identification processing unit 290 of the data processing device 12 and learns past consultation content. The anonymization unit is realized by the control unit 46A of the headset type terminal 314 and anonymizes the consultation content. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, learning unit, and anonymization unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives the consultation content input by the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the consultation content using AI. The provision unit is realized by the control unit 46A of the robot 414 and provides the analysis results to the user. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns past consultation content. The anonymization unit is realized by the control unit 46A of the robot 414 and anonymizes the consultation content.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] When accepting a user's consultation content, the reception unit refers to the user's past consultation history, and can respond more quickly to users who have previously made similar consultations. The reception unit can also evaluate the effectiveness of advice the user has received in the past and provide more effective advice preferentially. Furthermore, the reception unit can transfer the consultation content to a related expert based on the user's consultation content and provide expert advice. In this way, the reception unit can provide more effective advice by utilizing the user's past consultation history.

[0107] The learning unit can take into account the user's work situation and industry-specific problems when learning past consultation content and its solutions. For example, the learning unit can store common concerns and solutions in a database in a specific industry and provide appropriate advice to users in the same industry. The learning unit can also prioritize learning related consultation content based on the user's work situation. Furthermore, the learning unit can analyze the user's work situation and industry-specific problems and predict future trends and problems. This allows the learning unit to provide more appropriate advice by taking into account the user's work situation and industry-specific problems.

[0108] In order to protect the user's privacy, the anonymization unit can estimate the user's emotions when anonymizing the consultation content and adjust the anonymization method based on the estimated emotions. For example, if the user is feeling stressed, a strong anonymization method can be applied. Alternatively, if the user is relaxed, a normal anonymization method can be applied. Furthermore, if the user is in a hurry, quick anonymization can be performed. In this way, the anonymization unit can perform more appropriate anonymization by adjusting the anonymization method according to the user's emotions.

[0109] When analyzing the consultation content using natural language processing technology, the analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is feeling stressed, a simple and easy-to-understand presentation method can be used. Also, if the user is relaxed, detailed analysis results can be provided. Furthermore, if the user is in a hurry, analysis results that focus on the main points can be provided. In this way, the analysis unit can provide more appropriate analysis results by adjusting the way the analysis is presented according to the user's emotions.

[0110] When generating specific advice, the providing unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated emotions. For example, if the user is feeling stressed, simple and easy-to-understand advice can be provided. If the user is relaxed, detailed advice can be provided. Furthermore, if the user is in a hurry, advice that focuses on the main points can be provided. In this way, the providing unit can provide more appropriate advice by adjusting the way the advice is expressed depending on the user's emotions.

[0111] When accepting a user's consultation content, the reception unit can prioritize accepting highly relevant consultation content by taking into account the user's geographical location information. For example, the reception unit can prioritize accepting related consultation content based on the user's geographical location information. The reception unit can also prioritize accepting consultation content related to problems that occurred in locations close to the user's current location. Furthermore, the reception unit can analyze the relevance between the user's geographical location information and the consultation content and accept the most appropriate consultation content. In this way, the reception unit can prioritize accepting highly relevant consultation content by taking into account the user's geographical location information.

[0112] When learning past consultation contents and their solutions, the learning unit can analyze the user's social media activity and prioritize learning related consultation contents. For example, related consultation contents can be prioritized and learned from the user's social media activity. The learning unit can also analyze the user's comments on social media and select appropriate learning data. Furthermore, the learning unit can analyze the relevance between the user's social media activity and the consultation contents and select the most appropriate learning data. This allows the learning unit to prioritize learning related consultation contents by analyzing the user's social media activity.

[0113] When analyzing the consultation content using natural language processing technology, the analysis unit can apply different analysis algorithms depending on the category of the consultation content. For example, an emotion analysis algorithm can be applied to consultation content related to human relationships. Also, a business process analysis algorithm can be applied to consultation content related to work. Furthermore, the analysis unit can select and apply the optimal analysis algorithm for each category. This allows the analysis unit to improve analysis accuracy by applying the optimal analysis algorithm depending on the category of the consultation content.

[0114] When generating specific advice, the providing unit can refer to the user's past consultation history and prioritize providing advice that has been effective in the past. For example, the providing unit can evaluate the effectiveness of advice the user has received in the past and prioritize providing advice that has been effective. The providing unit can also select relevant advice from the user's past consultation history. Furthermore, the providing unit can analyze the user's past consultation history and provide optimal advice. This allows the providing unit to provide more effective advice by utilizing the user's past consultation history.

[0115] When learning past consultation details and their solutions, the learning unit can estimate the user's emotions and select learning data based on the estimated emotions. For example, if the user is feeling stressed, learning data related to stress reduction can be selected with priority. Also, if the user is relaxed, learning data related to relaxation can be selected. Furthermore, if the user is in a hurry, learning data can be selected quickly. In this way, the learning unit can select more appropriate learning data by selecting learning data according to the user's emotions.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The reception unit accepts the consultation content entered by the user. The consultation content entered by the user may include distrust in conversations with superiors, doubts about colleagues, and worries within the company. The reception unit receives the text data entered by the user and sends it to the analysis unit. Step 2: The analysis unit uses AI to analyze the consultation content received by the reception unit. The analysis unit uses natural language processing technology to understand the user's concerns and questions and generate appropriate advice. For example, it uses text generation AI (e.g., LLM) to analyze the consultation content and generate advice. It also learns from past consultation content and solutions, and generates more appropriate advice by referring to the consultation content and solutions of other users with similar concerns. Step 3: The provision unit provides the user with the advice generated by the analysis unit. For example, it provides specific methods for improving communication with a superior or advice for smoothing relationships with colleagues. Specifically, it provides advice such as, "When making a specific proposal to improve communication with your superior, it is effective to prepare materials in advance."

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 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 that accepts consultation requests, an analysis unit that analyzes the consultation content received by the reception unit; a providing unit that provides advice based on the content analyzed by the analyzing unit; Equipped with A system characterized by:

2. Equipping the learning department with the content of past consultations 2. The system of claim 1.

3. Equipped with an anonymization function to protect user privacy 2. The system of claim 1.

4. The analysis unit Analyzing consultation content using natural language processing technology 2. The system of claim 1.

5. The providing unit Generate specific advice 2. The system of claim 1.

6. The learning unit Learn about past consultations and their solutions 3. The system of claim 2.

7. The reception unit Estimates the user's emotions and adjusts the timing of accepting consultations based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past consultation history and select the optimal reception method 2. The system of claim 1.

9. The reception unit When receiving inquiries, filter them based on the user's current job status and areas of interest.

2. The system of claim 1.

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