system

Abstract

The system according to this embodiment aims to enable less experienced managers to provide accurate advice to their subordinates when they seek their assistance. [Solution] The system according to the embodiment comprises an input unit, a reception unit, an analysis unit, a generation unit, and a provision unit. The input unit inputs learning data. The reception unit inputs the consultation content. The analysis unit analyzes the consultation content input by the reception unit. The generation unit generates advice based on the content analyzed by the analysis unit. The provision unit provides the advice generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for a management position with little experience to provide accurate advice on the consultations of subordinates.

[0005] The system according to the embodiment aims to enable a management position with little experience to provide accurate advice on the consultations of subordinates.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an input unit, a reception unit, an analysis unit, a generation unit, and a provision unit. The input unit inputs learning data. The reception unit inputs the consultation content. The analysis unit analyzes the consultation content input by the reception unit. The generation unit generates advice based on the content analyzed by the analysis unit. The provision unit provides the advice generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows less experienced managers to provide accurate advice to their subordinates when they seek it. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The advice provision system according to an embodiment of the present invention is a system that uses a generating AI to provide accurate advice to inexperienced managers, such as newly appointed managers, in response to consultations from their subordinates. This advice provision system inputs learning data into the generating AI, which is a record of how excellent managers respond to consultations from their subordinates. This learning data includes specific consultation content and corresponding responses. Next, when a newly appointed manager seeks advice, they input the consultation content into the generating AI. The generating AI analyzes the input consultation content and generates optimal advice based on the learning data. The generated advice is provided to the newly appointed manager. The newly appointed manager uses the advice provided by the generating AI as a reference when responding to their subordinates. As a result, even inexperienced managers can provide accurate advice and resolve their subordinates' problems. This service enables newly appointed managers to respond to consultations from their subordinates quickly and accurately, improving the efficiency of the entire organization. In addition, the sharing of know-how from excellent managers improves the overall management capabilities of the organization. Thus, the advice provision system enables newly appointed managers to provide accurate advice to their subordinates in response to consultations from their subordinates.

[0029] The advice provision system according to this embodiment comprises an input unit, a reception unit, an analysis unit, a generation unit, and a provision unit. The input unit inputs training data. The training data includes, for example, data recording how excellent managers respond to consultations from their subordinates. The input unit can input training data in the form of, for example, text data, numerical data, or image data. The reception unit inputs the content of the consultation. The content of the consultation includes, for example, consultations regarding work or consultations regarding interpersonal relationships. The reception unit can accept the content of the consultation in the form of, for example, text input, voice input, or image input. The analysis unit analyzes the content of the consultation input by the reception unit. The analysis unit can analyze the content of the consultation using, for example, natural language processing technology. The analysis unit can analyze the content of the consultation using, for example, text analysis, statistical analysis, or machine learning. The generation unit generates advice based on the content analyzed by the analysis unit. The generation unit can generate advice using, for example, a generation AI. The generation unit can generate advice in the form of, for example, a document or a checklist. The provision unit provides the advice generated by the generation unit. The provision unit can provide advice in the form of, for example, email notifications, push notifications, or screen displays. This allows the advice provision system according to the embodiment to provide accurate advice to newly appointed managers in response to inquiries from their subordinates. Some or all of the above-described processes in the input unit, reception unit, analysis unit, generation unit, and provision unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the input unit can input learning data into the generation AI and cause the generation AI to perform analysis of the learning data. The reception unit can input the consultation content into the generation AI and cause the generation AI to perform analysis of the consultation content. The analysis unit can input the consultation content into the generation AI and cause the generation AI to output the analysis results. The generation unit can input the analysis results into the generation AI and cause the generation AI to generate advice. The provision unit can input the generated advice into the generation AI and cause the generation AI to provide the advice.

[0030] The input unit inputs training data. This training data includes, for example, records of how excellent managers handle consultations with their subordinates. The input unit can input training data in various formats, such as text data, numerical data, and image data. Specifically, text data includes documents and email exchanges recording past consultations and how they were handled. Numerical data includes statistical information such as the frequency of consultations, response time, and satisfaction ratings. Image data includes photographs and videos recording facial expressions and gestures during consultations. This data is used to train the generative AI and forms the basis for generating more accurate advice. The input unit centrally manages this data and preprocesses it as needed. For example, it may clean and normalize text data, scale numerical data, and resize and filter image data. This allows the input unit to prepare the data so that the generative AI can learn efficiently and improve the overall system performance. Furthermore, the input unit is designed to easily update and add data, allowing for quick responses when new training data becomes available. This enables the input unit to always support the provision of advice based on the latest information.

[0031] The reception desk receives the consultation details. These consultation details include, for example, business-related consultations and interpersonal relationship consultations. The reception desk can accept consultation details in various formats, such as text input, voice input, and image input. Specifically, for text input, the user enters the consultation details using a keyboard. For voice input, the consultation details are entered by voice via a microphone and converted into text using speech recognition technology. For image input, the consultation details are written by hand on paper, scanned, and entered as image data. These input formats can be selected with user convenience in mind, and can accommodate various situations. The reception desk temporarily stores the entered consultation details and performs necessary pre-processing before sending them to the analysis department. For example, for voice input, noise reduction and speech normalization are performed, and for text input, spell checking and grammar checking are performed. This allows the reception desk to prepare the data so that the analysis department can perform analysis efficiently, improving the overall accuracy of the system. Furthermore, to protect user privacy, the reception desk encrypts and stores the entered consultation details and performs anonymization processing as needed. This allows the reception desk to provide an environment where users can enter their consultation details with peace of mind.

[0032] The analysis unit analyzes the consultation content entered by the reception unit. For example, the analysis unit can analyze the consultation content using natural language processing technology. Specifically, text analysis involves morphological and grammatical analysis to extract the main topic and emotions of the consultation. Statistical analysis compares the current consultation content with past consultation data to identify its characteristics. Machine learning classifies the consultation content and predicts appropriate responses. By combining these analysis methods, the analysis unit can analyze the consultation content from multiple angles and obtain more accurate results. Furthermore, the analysis unit can also analyze the consultation content using generative AI. Generative AI deeply understands the meaning of the consultation content based on a large amount of training data and proposes appropriate responses. For example, generative AI understands the context of the consultation content, refers to similar past consultation cases, and generates optimal advice. This allows the analysis unit to perform more advanced analysis and provide accurate advice to users. Moreover, the analysis unit can update the analysis results in real time and provide advice based on the latest information. This allows the analysis unit to always respond to the latest situation and meet user needs.

[0033] The generation unit generates advice based on the analysis performed by the analysis unit. The generation unit can, for example, use a generation AI to generate advice. Specifically, the generation AI generates appropriate advice based on the analysis results provided by the analysis unit. The generation AI utilizes past learning data to generate the most suitable advice for the consultation. For example, if the consultation concerns work, the generation AI will propose specific work procedures and improvement measures; if it concerns interpersonal relationships, it will propose communication methods and measures to improve interpersonal skills. The generation unit provides the generated advice in a user-friendly format, such as text or a checklist. This allows the generation unit to provide users with specific and practical advice. Furthermore, the generation unit evaluates the quality of the generated advice and makes corrections as needed. For example, if the advice generated by the generation AI is inappropriate, the generation unit reviews the advice and regenerates it. This ensures that the generation unit consistently provides high-quality advice and improves user satisfaction.

[0034] The delivery unit provides advice generated by the generation unit. The delivery unit can provide advice in various forms, such as email notifications, push notifications, and screen displays. Specifically, email notifications send advice to the user's email address, push notifications send real-time notifications to smartphones and tablets, and screen displays show advice on the web pages and applications the user accesses. This allows the delivery unit to ensure that users receive advice in the most convenient way. Furthermore, the delivery unit monitors the delivery status of advice and collects user feedback. For example, it checks whether the user has received the advice and whether they are satisfied with its content, and makes improvements as needed. This enables the delivery unit to respond flexibly to user needs, improving the overall reliability of the system and user satisfaction. In addition, the delivery unit can save the history of advice provided and use it as reference material for the future. This allows the delivery unit to make continuous improvements based on past advice and always provide the latest and most optimal advice.

[0035] The generation unit includes a search unit that searches for similar consultation content from past training data. The generation unit can use the search unit to generate optimal advice based on past training data, for example. The search unit can search for similar consultation content using methods such as keyword matching and semantic similarity. The search unit can calculate the similarity between past and current consultation content and search for the most similar consultation content, for example. The search unit can analyze the semantic similarity between past and current consultation content using natural language processing techniques, for example. This allows the generation unit to generate more accurate advice by utilizing past training data. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input past training data into a generation AI and have the generation AI perform a search for similar consultation content.

[0036] The provisioning unit includes a notification unit that notifies newly appointed managers of the generated advice. The provisioning unit can use the notification unit, for example, to quickly notify newly appointed managers of the generated advice. The notification unit can notify the advice by methods such as email notifications, push notifications, or screen displays. The notification unit can select the notification method according to the importance of the advice. For example, the notification unit can provide high-priority advice by push notification and low-priority advice by email notification. This allows the provisioning unit to quickly notify newly appointed managers of the generated advice. Some or all of the above processing in the provisioning unit may be performed using a generation AI, for example, or without a generation AI. For example, the provisioning unit can input the generated advice into a generation AI and have the generation AI select the notification method.

[0037] The input unit estimates the emotions of high-performing managers and selects training data based on the estimated emotions. For example, if a high-performing manager is stressed, the input unit prioritizes inputting training data to help them cope with that stress. For example, if a high-performing manager is relaxed, the input unit can input training data that includes how to respond in that relaxed state. For example, if a high-performing manager is tense, the input unit can input training data that includes how to alleviate that tension. This allows for the selection of appropriate training data based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using a generative AI, or not using a generative AI. For example, the input unit can input the emotional data of high-performing managers into a generative AI and have the generative AI perform emotion estimation.

[0038] The input unit applies different data input methods depending on the category of the consultation content when inputting training data. For example, if the consultation content is related to interpersonal relationships, the input unit will prioritize inputting training data specific to that category. For example, if the consultation content is related to improving work efficiency, the input unit can prioritize inputting training data specific to that category. For example, if the consultation content is related to career paths, the input unit can prioritize inputting training data specific to that category. This allows for the input of optimal training data for each category. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the input unit can input the category of the consultation content into the generative AI and have the generative AI select the data input method for each category.

[0039] The input unit evaluates the reliability of the training data when inputting it and prioritizes inputting highly reliable data. For example, the input unit can verify the source of the training data and prioritize inputting highly reliable data. For example, the input unit can verify the content of the training data and prioritize inputting data with high accuracy. For example, the input unit can check the update frequency of the training data and prioritize inputting the latest data. By prioritizing the input of highly reliable data, the accuracy of the system is improved. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the input unit can input the reliability of the training data into the generative AI and have the generative AI perform the reliability evaluation.

[0040] The reception department determines the priority of inquiries based on their urgency upon receipt. For example, the reception department may prioritize and respond quickly to inquiries with high urgency. For example, the reception department may accept inquiries with low urgency in a way that does not cause problems if postponed. For example, the reception department may accept inquiries with moderate urgency at an appropriate time. This allows for the acceptance of inquiries with priority according to their urgency. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or without a generative AI. For example, the reception department may input the urgency of the inquiry into a generative AI and have the generative AI perform the priority determination based on urgency.

[0041] The reception department applies different reception algorithms depending on the category of the consultation content when it is received. For example, if the consultation content is related to interpersonal relationships, the reception department will apply a reception algorithm specialized for that category. For example, if the consultation content is related to improving work efficiency, the reception department may apply a reception algorithm specialized for that category. For example, if the consultation content is related to career paths, the reception department may apply a reception algorithm specialized for that category. This allows the most suitable reception algorithm to be applied for each category. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception department can input the category of the consultation content into a generative AI and have the generative AI select a reception algorithm for each category.

[0042] The analysis unit optimizes the analysis algorithm by referring to past consultation data when analyzing the content of a consultation. For example, the analysis unit selects the optimal analysis algorithm based on past consultation data. For example, the analysis unit can apply an analysis algorithm specialized for a specific category from past consultation data. For example, the analysis unit can analyze past consultation data and select the most efficient analysis algorithm. This allows the analysis algorithm to be optimized by utilizing past consultation data. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input past consultation data into a generative AI and have the generative AI perform the optimization of the analysis algorithm.

[0043] The analysis unit applies different analysis methods to each category of consultation content when analyzing the content. For example, in the case of a consultation about interpersonal relationships, the analysis unit applies an analysis method specific to that category. For example, in the case of a consultation about improving work efficiency, the analysis unit can apply an analysis method specific to that category. For example, in the case of a consultation about career paths, the analysis unit can apply an analysis method specific to that category. This allows the most suitable analysis method to be applied to each category. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the categories of the consultation content into the generative AI and have the generative AI select an analysis method for each category.

[0044] The generation unit optimizes the generation algorithm by referring to past advice data when generating advice. For example, the generation unit selects the optimal generation algorithm based on past advice data. For example, the generation unit can apply a generation algorithm specialized for a specific category from past advice data. For example, the generation unit can analyze past advice data and select the most efficient generation algorithm. This allows the generation algorithm to be optimized by utilizing past advice data. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input past advice data into a generation AI and have the generation AI perform the optimization of the generation algorithm.

[0045] The generation unit applies different generation methods to each category of consultation content when generating advice. For example, if the consultation content concerns interpersonal relationships, the generation unit applies a generation method specialized for that category. For example, if the consultation content concerns improving work efficiency, the generation unit can apply a generation method specialized for that category. For example, if the consultation content concerns career paths, the generation unit can apply a generation method specialized for that category. This allows the optimal generation method to be applied to each category. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the categories of the consultation content into the generation AI and have the generation AI select a generation method for each category.

[0046] The service provider selects the optimal service delivery method by referring to the manager's past advice history when providing advice. For example, the service provider selects the optimal service delivery method based on the manager's past advice history. For example, the service provider can select a service delivery method specialized for a specific category from the manager's past advice history. For example, the service provider can analyze the manager's past advice history and select the most efficient service delivery method. This allows the service provider to select the optimal service delivery method by utilizing past advice history. Some or all of the above processes in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input past advice history into a generative AI and have the generative AI perform the selection of a service delivery method.

[0047] The advice delivery unit adjusts the timing of its delivery based on the manager's current situation. For example, if the manager is in a meeting, the advice delivery unit will provide it after the meeting ends. For example, if the manager is on a business trip, the advice delivery unit can provide it according to the situation at the destination. For example, if the manager is on vacation, the advice delivery unit can provide it after the vacation ends. This ensures that advice is provided at an appropriate time according to the manager's situation. Some or all of the above processing in the advice delivery unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice delivery unit can input the manager's current situation into the generative AI and have the generative AI adjust the timing of the delivery.

[0048] The search unit optimizes the search algorithm by referring to past search data during a search. For example, the search unit selects the optimal search algorithm based on past search data. For example, the search unit can apply a search algorithm specialized for a specific category based on past search data. For example, the search unit can analyze past search data and select the most efficient search algorithm. This allows the search algorithm to be optimized by utilizing past search data. Some or all of the above processes in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input past search data into a generative AI and have the generative AI perform the optimization of the search algorithm.

[0049] The search unit applies different search methods depending on the category of the consultation content during the search. For example, if the consultation content is related to interpersonal relationships, the search unit applies a search method specific to that category. For example, if the consultation content is related to improving work efficiency, the search unit can apply a search method specific to that category. For example, if the consultation content is related to career paths, the search unit can apply a search method specific to that category. This allows the most suitable search method to be applied for each category. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input the category of the consultation content into the generative AI and have the generative AI select a search method for each category.

[0050] The search unit improves search accuracy by referring to the manager's past search history during a search. For example, the search unit selects the optimal search algorithm based on the manager's past search history. For example, the search unit can apply a search algorithm specialized for a specific category based on the manager's past search history. For example, the search unit can analyze the manager's past search history and select the most efficient search algorithm. This allows for improved search accuracy by utilizing past search history. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input past search history into a generative AI and have the generative AI perform the optimization of the search algorithm.

[0051] The search unit improves search accuracy by referencing relevant external data during a search. For example, the search unit can improve search accuracy by referencing relevant industry data. For example, the search unit can improve search accuracy by referencing relevant market data. For example, the search unit can improve search accuracy by referencing relevant academic data. In this way, search accuracy can be improved by referencing relevant external data. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input relevant external data into a generative AI and have the generative AI perform optimization of the search algorithm.

[0052] The notification unit, when issuing a notification, selects the optimal notification method by referring to the manager's past notification history. For example, the notification unit can select the optimal notification method based on the manager's past notification history. For example, the notification unit can select a notification method specialized for a specific category from the manager's past notification history. For example, the notification unit can analyze the manager's past notification history and select the most efficient notification method. This allows the optimal notification method to be selected by utilizing past notification history. Some or all of the above processing in the notification unit may be performed using, for example, a generation AI, or without a generation AI. For example, the notification unit can input past notification history into a generation AI and have the generation AI perform the selection of a notification method.

[0053] The notification unit selects the optimal notification method when sending a notification, taking into account the manager's device information. For example, if the manager is using a smartphone, the notification unit can provide a notification method that matches the screen size. For example, if the manager is using a tablet, the notification unit can provide a notification method optimized for a larger screen. For example, if the manager is using a desktop computer, the notification unit can provide a more detailed notification method. This allows the notification unit to provide the optimal notification method according to the device information. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can input the manager's device information into a generative AI and have the generative AI select the notification method.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The advice provision system may further include a feedback collection unit. The feedback collection unit collects feedback from newly appointed managers after they have actually used the advice provided. For example, the feedback collection unit can collect opinions on the effectiveness of the advice and areas for improvement. The feedback collection unit can collect feedback using methods such as questionnaires, free-response formats, or voice input formats. This allows the advice provision system to improve the quality of its advice based on the collected feedback. Some or all of the above-described processes in the feedback collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback collection unit can input the collected feedback into a generative AI and have the generative AI perform an analysis of the feedback.

[0056] The advice provision system may further include a schedule management unit. The schedule management unit manages the schedules of newly appointed managers and optimizes the timing of advice provision. For example, the schedule management unit can provide advice at an appropriate time, taking into account the manager's meeting and business trip schedules. For example, the schedule management unit can provide advice while avoiding busy times for managers. This allows the advice provision system to provide advice at the optimal time according to the manager's schedule. Some or all of the above processing in the schedule management unit may be performed using, for example, a generative AI, or without a generative AI. For example, the schedule management unit can input schedule data into a generative AI and have the generative AI perform the optimization of the provision timing.

[0057] The advice-providing system may further include a personalization unit. The personalization unit customizes advice based on the individual needs and preferences of newly appointed managers. For example, the personalization unit can provide individually optimized advice based on the manager's past consultations and feedback. The personalization unit can adjust the content and format of the advice according to the manager's personality and communication style, for example. This allows the advice-providing system to provide more effective advice tailored to individual needs. Some or all of the above-described processes in the personalization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the personalization unit can input individual data into a generative AI and have the generative AI perform the customization of the advice.

[0058] The advice provision system may further include a real-time monitoring unit. The real-time monitoring unit provides advice in real time while a newly appointed manager is consulting with a subordinate. For example, the real-time monitoring unit can provide advice at the appropriate time while the manager is conversing with a subordinate. For example, the real-time monitoring unit can instantly generate and provide advice while the manager is listening to the subordinate's consultation. This allows the advice provision system to support real-time responses and enable managers to provide appropriate advice immediately. Some or all of the above-described processes in the real-time monitoring unit may be performed using, for example, a generation AI, or without a generation AI. For example, the real-time monitoring unit can input the consultation content into a generation AI and have the generation AI perform real-time advice generation.

[0059] The advice provision system may further include a context analysis unit. The context analysis unit analyzes background information of the consultation content to provide more accurate advice. For example, the context analysis unit can analyze the business situation and interpersonal relationships that underlie the consultation content. If the consultation content is related to a specific project, the context analysis unit can analyze the progress of that project and information about the stakeholders. This allows the advice provision system to provide more accurate advice that takes background information into account. Some or all of the above-described processing in the context analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the context analysis unit can input background information into a generative AI and have the generative AI perform the context analysis.

[0060] The advice provision system may also include a multilingual support unit. The multilingual support unit receives, analyzes, and provides advice based on consultations in different languages. For example, the multilingual support unit can receive consultations in multiple languages, such as English, Spanish, and Chinese. The multilingual support unit can, for example, automatically translate consultations in different languages ​​and input them into the analysis unit. This allows the advice provision system to support managers who speak different languages. Some or all of the above-described processes in the multilingual support unit may be performed using, for example, a generative AI, or not. For example, the multilingual support unit can input translation data into a generative AI to improve the accuracy of the translation.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The input unit inputs the training data. The training data may include, for example, data recording how excellent managers handle consultations with their subordinates. The input unit can input the training data in various formats, such as text data, numerical data, or image data. Step 2: The reception desk inputs the details of the consultation. These may include, for example, consultations regarding work or interpersonal relationships. The reception desk can accept consultation details in various formats, such as text input, voice input, or image input. Step 3: The analysis unit analyzes the consultation content entered by the reception unit. The analysis unit can analyze the consultation content using, for example, natural language processing technology. The analysis unit can also analyze the consultation content using methods such as text analysis, statistical analysis, and machine learning. Step 4: The generation unit generates advice based on the content analyzed by the analysis unit. The generation unit can generate advice using, for example, a generation AI. The generation unit can generate advice in, for example, text format, checklist format, etc. Step 5: The providing unit provides the advice generated by the generating unit. The providing unit can provide the advice in the form of, for example, email notifications, push notifications, or screen displays.

[0063] (Example of form 2) The advice provision system according to an embodiment of the present invention is a system that uses a generating AI to provide accurate advice to inexperienced managers, such as newly appointed managers, in response to consultations from their subordinates. This advice provision system inputs learning data into the generating AI, which is a record of how excellent managers respond to consultations from their subordinates. This learning data includes specific consultation content and corresponding responses. Next, when a newly appointed manager seeks advice, they input the consultation content into the generating AI. The generating AI analyzes the input consultation content and generates optimal advice based on the learning data. The generated advice is provided to the newly appointed manager. The newly appointed manager uses the advice provided by the generating AI as a reference when responding to their subordinates. As a result, even inexperienced managers can provide accurate advice and resolve their subordinates' problems. This service enables newly appointed managers to respond to consultations from their subordinates quickly and accurately, improving the efficiency of the entire organization. In addition, the sharing of know-how from excellent managers improves the overall management capabilities of the organization. Thus, the advice provision system enables newly appointed managers to provide accurate advice to their subordinates in response to consultations from their subordinates.

[0064] The advice provision system according to this embodiment comprises an input unit, a reception unit, an analysis unit, a generation unit, and a provision unit. The input unit inputs training data. The training data includes, for example, data recording how excellent managers respond to consultations from their subordinates. The input unit can input training data in the form of, for example, text data, numerical data, or image data. The reception unit inputs the content of the consultation. The content of the consultation includes, for example, consultations regarding work or consultations regarding interpersonal relationships. The reception unit can accept the content of the consultation in the form of, for example, text input, voice input, or image input. The analysis unit analyzes the content of the consultation input by the reception unit. The analysis unit can analyze the content of the consultation using, for example, natural language processing technology. The analysis unit can analyze the content of the consultation using, for example, text analysis, statistical analysis, or machine learning. The generation unit generates advice based on the content analyzed by the analysis unit. The generation unit can generate advice using, for example, a generation AI. The generation unit can generate advice in the form of, for example, a document or a checklist. The provision unit provides the advice generated by the generation unit. The provision unit can provide advice in the form of, for example, email notifications, push notifications, or screen displays. This allows the advice provision system according to the embodiment to provide accurate advice to newly appointed managers in response to inquiries from their subordinates. Some or all of the above-described processes in the input unit, reception unit, analysis unit, generation unit, and provision unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the input unit can input learning data into the generation AI and cause the generation AI to perform analysis of the learning data. The reception unit can input the consultation content into the generation AI and cause the generation AI to perform analysis of the consultation content. The analysis unit can input the consultation content into the generation AI and cause the generation AI to output the analysis results. The generation unit can input the analysis results into the generation AI and cause the generation AI to generate advice. The provision unit can input the generated advice into the generation AI and cause the generation AI to provide the advice.

[0065] The input unit inputs training data. This training data includes, for example, records of how excellent managers handle consultations with their subordinates. The input unit can input training data in various formats, such as text data, numerical data, and image data. Specifically, text data includes documents and email exchanges recording past consultations and how they were handled. Numerical data includes statistical information such as the frequency of consultations, response time, and satisfaction ratings. Image data includes photographs and videos recording facial expressions and gestures during consultations. This data is used to train the generative AI and forms the basis for generating more accurate advice. The input unit centrally manages this data and preprocesses it as needed. For example, it may clean and normalize text data, scale numerical data, and resize and filter image data. This allows the input unit to prepare the data so that the generative AI can learn efficiently and improve the overall system performance. Furthermore, the input unit is designed to easily update and add data, allowing for quick responses when new training data becomes available. This enables the input unit to always support the provision of advice based on the latest information.

[0066] The reception desk receives the consultation details. These consultation details include, for example, business-related consultations and interpersonal relationship consultations. The reception desk can accept consultation details in various formats, such as text input, voice input, and image input. Specifically, for text input, the user enters the consultation details using a keyboard. For voice input, the consultation details are entered by voice via a microphone and converted into text using speech recognition technology. For image input, the consultation details are written by hand on paper, scanned, and entered as image data. These input formats can be selected with user convenience in mind, and can accommodate various situations. The reception desk temporarily stores the entered consultation details and performs necessary pre-processing before sending them to the analysis department. For example, for voice input, noise reduction and speech normalization are performed, and for text input, spell checking and grammar checking are performed. This allows the reception desk to prepare the data so that the analysis department can perform analysis efficiently, improving the overall accuracy of the system. Furthermore, to protect user privacy, the reception desk encrypts and stores the entered consultation details and performs anonymization processing as needed. This allows the reception desk to provide an environment where users can enter their consultation details with peace of mind.

[0067] The analysis unit analyzes the consultation content entered by the reception unit. For example, the analysis unit can analyze the consultation content using natural language processing technology. Specifically, text analysis involves morphological and grammatical analysis to extract the main topic and emotions of the consultation. Statistical analysis compares the current consultation content with past consultation data to identify its characteristics. Machine learning classifies the consultation content and predicts appropriate responses. By combining these analysis methods, the analysis unit can analyze the consultation content from multiple angles and obtain more accurate results. Furthermore, the analysis unit can also analyze the consultation content using generative AI. Generative AI deeply understands the meaning of the consultation content based on a large amount of training data and proposes appropriate responses. For example, generative AI understands the context of the consultation content, refers to similar past consultation cases, and generates optimal advice. This allows the analysis unit to perform more advanced analysis and provide accurate advice to users. Moreover, the analysis unit can update the analysis results in real time and provide advice based on the latest information. This allows the analysis unit to always respond to the latest situation and meet user needs.

[0068] The generation unit generates advice based on the analysis performed by the analysis unit. The generation unit can, for example, use a generation AI to generate advice. Specifically, the generation AI generates appropriate advice based on the analysis results provided by the analysis unit. The generation AI utilizes past learning data to generate the most suitable advice for the consultation. For example, if the consultation concerns work, the generation AI will propose specific work procedures and improvement measures; if it concerns interpersonal relationships, it will propose communication methods and measures to improve interpersonal skills. The generation unit provides the generated advice in a user-friendly format, such as text or a checklist. This allows the generation unit to provide users with specific and practical advice. Furthermore, the generation unit evaluates the quality of the generated advice and makes corrections as needed. For example, if the advice generated by the generation AI is inappropriate, the generation unit reviews the advice and regenerates it. This ensures that the generation unit consistently provides high-quality advice and improves user satisfaction.

[0069] The delivery unit provides advice generated by the generation unit. The delivery unit can provide advice in various forms, such as email notifications, push notifications, and screen displays. Specifically, email notifications send advice to the user's email address, push notifications send real-time notifications to smartphones and tablets, and screen displays show advice on the web pages and applications the user accesses. This allows the delivery unit to ensure that users receive advice in the most convenient way. Furthermore, the delivery unit monitors the delivery status of advice and collects user feedback. For example, it checks whether the user has received the advice and whether they are satisfied with its content, and makes improvements as needed. This enables the delivery unit to respond flexibly to user needs, improving the overall reliability of the system and user satisfaction. In addition, the delivery unit can save the history of advice provided and use it as reference material for the future. This allows the delivery unit to make continuous improvements based on past advice and always provide the latest and most optimal advice.

[0070] The generation unit includes a search unit that searches for similar consultation content from past training data. The generation unit can use the search unit to generate optimal advice based on past training data, for example. The search unit can search for similar consultation content using methods such as keyword matching and semantic similarity. The search unit can calculate the similarity between past and current consultation content and search for the most similar consultation content, for example. The search unit can analyze the semantic similarity between past and current consultation content using natural language processing techniques, for example. This allows the generation unit to generate more accurate advice by utilizing past training data. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input past training data into a generation AI and have the generation AI perform a search for similar consultation content.

[0071] The provisioning unit includes a notification unit that notifies newly appointed managers of the generated advice. The provisioning unit can use the notification unit, for example, to quickly notify newly appointed managers of the generated advice. The notification unit can notify the advice by methods such as email notifications, push notifications, or screen displays. The notification unit can select the notification method according to the importance of the advice. For example, the notification unit can provide high-priority advice by push notification and low-priority advice by email notification. This allows the provisioning unit to quickly notify newly appointed managers of the generated advice. Some or all of the above processing in the provisioning unit may be performed using a generation AI, for example, or without a generation AI. For example, the provisioning unit can input the generated advice into a generation AI and have the generation AI select the notification method.

[0072] The input unit estimates the emotions of high-performing managers and selects training data based on the estimated emotions. For example, if a high-performing manager is stressed, the input unit prioritizes inputting training data to help them cope with that stress. For example, if a high-performing manager is relaxed, the input unit can input training data that includes how to respond in that relaxed state. For example, if a high-performing manager is tense, the input unit can input training data that includes how to alleviate that tension. This allows for the selection of appropriate training data based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using a generative AI, or not using a generative AI. For example, the input unit can input the emotional data of high-performing managers into a generative AI and have the generative AI perform emotion estimation.

[0073] The input unit applies different data input methods depending on the category of the consultation content when inputting training data. For example, if the consultation content is related to interpersonal relationships, the input unit will prioritize inputting training data specific to that category. For example, if the consultation content is related to improving work efficiency, the input unit can prioritize inputting training data specific to that category. For example, if the consultation content is related to career paths, the input unit can prioritize inputting training data specific to that category. This allows for the input of optimal training data for each category. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the input unit can input the category of the consultation content into the generative AI and have the generative AI select the data input method for each category.

[0074] The input unit evaluates the reliability of the training data when inputting it and prioritizes inputting highly reliable data. For example, the input unit can verify the source of the training data and prioritize inputting highly reliable data. For example, the input unit can verify the content of the training data and prioritize inputting data with high accuracy. For example, the input unit can check the update frequency of the training data and prioritize inputting the latest data. By prioritizing the input of highly reliable data, the accuracy of the system is improved. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the input unit can input the reliability of the training data into the generative AI and have the generative AI perform the reliability evaluation.

[0075] The reception desk estimates the emotions of new managers and adjusts the method of receiving inquiries based on the estimated emotions. For example, if a new manager is nervous, the reception desk can provide a simple and intuitive method of receiving inquiries. If a new manager is relaxed, the reception desk can provide detailed input options. If a new manager is stressed, the reception desk can prioritize voice input to allow for quick input of inquiries. This allows for the provision of an appropriate method of receiving inquiries according to emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input the new manager's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The reception department determines the priority of inquiries based on their urgency upon receipt. For example, the reception department may prioritize and respond quickly to inquiries with high urgency. For example, the reception department may accept inquiries with low urgency in a way that does not cause problems if postponed. For example, the reception department may accept inquiries with moderate urgency at an appropriate time. This allows for the acceptance of inquiries with priority according to their urgency. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or without a generative AI. For example, the reception department may input the urgency of the inquiry into a generative AI and have the generative AI perform the priority determination based on urgency.

[0077] The reception department applies different reception algorithms depending on the category of the consultation content when it is received. For example, if the consultation content is related to interpersonal relationships, the reception department will apply a reception algorithm specialized for that category. For example, if the consultation content is related to improving work efficiency, the reception department may apply a reception algorithm specialized for that category. For example, if the consultation content is related to career paths, the reception department may apply a reception algorithm specialized for that category. This allows the most suitable reception algorithm to be applied for each category. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception department can input the category of the consultation content into a generative AI and have the generative AI select a reception algorithm for each category.

[0078] The analysis unit estimates the emotions of newly appointed managers and adjusts the analysis method based on the estimated emotions. For example, if the newly appointed manager is nervous, the analysis unit can provide a simple and intuitive analysis method. For example, if the newly appointed manager is relaxed, the analysis unit can provide detailed analysis options. For example, if the newly appointed manager is stressed, the analysis unit can perform a rapid analysis and provide results. This allows for the provision of an appropriate analysis method according to the emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input the emotional data of the newly appointed manager into a generative AI and have the generative AI perform emotion estimation.

[0079] The analysis unit optimizes the analysis algorithm by referring to past consultation data when analyzing the content of a consultation. For example, the analysis unit selects the optimal analysis algorithm based on past consultation data. For example, the analysis unit can apply an analysis algorithm specialized for a specific category from past consultation data. For example, the analysis unit can analyze past consultation data and select the most efficient analysis algorithm. This allows the analysis algorithm to be optimized by utilizing past consultation data. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input past consultation data into a generative AI and have the generative AI perform the optimization of the analysis algorithm.

[0080] The analysis unit applies different analysis methods to each category of consultation content when analyzing the content. For example, in the case of a consultation about interpersonal relationships, the analysis unit applies an analysis method specific to that category. For example, in the case of a consultation about improving work efficiency, the analysis unit can apply an analysis method specific to that category. For example, in the case of a consultation about career paths, the analysis unit can apply an analysis method specific to that category. This allows the most suitable analysis method to be applied to each category. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the categories of the consultation content into the generative AI and have the generative AI select an analysis method for each category.

[0081] The generation unit estimates the emotions of the newly appointed manager and adjusts the method of generating advice based on the estimated emotions. For example, if the newly appointed manager is nervous, the generation unit can provide simple and intuitive advice. For example, if the newly appointed manager is relaxed, the generation unit can provide detailed advice. For example, if the newly appointed manager is stressed, the generation unit can provide advice quickly. This allows for the generation of appropriate advice according to emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not using a generation AI. For example, the generation unit can input the emotional data of the newly appointed manager into a generation AI and have the generation AI perform emotion estimation.

[0082] The generation unit optimizes the generation algorithm by referring to past advice data when generating advice. For example, the generation unit selects the optimal generation algorithm based on past advice data. For example, the generation unit can apply a generation algorithm specialized for a specific category from past advice data. For example, the generation unit can analyze past advice data and select the most efficient generation algorithm. This allows the generation algorithm to be optimized by utilizing past advice data. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input past advice data into a generation AI and have the generation AI perform the optimization of the generation algorithm.

[0083] The generation unit applies different generation methods to each category of consultation content when generating advice. For example, if the consultation content concerns interpersonal relationships, the generation unit applies a generation method specialized for that category. For example, if the consultation content concerns improving work efficiency, the generation unit can apply a generation method specialized for that category. For example, if the consultation content concerns career paths, the generation unit can apply a generation method specialized for that category. This allows the optimal generation method to be applied to each category. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the categories of the consultation content into the generation AI and have the generation AI select a generation method for each category.

[0084] The service provider estimates the emotions of newly appointed managers and adjusts the method of providing advice based on the estimated emotions. For example, if a newly appointed manager is nervous, the service provider can provide a simple and intuitive method of advice. For example, if a newly appointed manager is relaxed, the service provider can provide a detailed method of advice. For example, if a newly appointed manager is stressed, the service provider can provide advice quickly. This allows for the provision of appropriate advice tailored to the emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input the emotions of newly appointed managers into a generative AI and have the generative AI perform emotion estimation.

[0085] The service provider selects the optimal service delivery method by referring to the manager's past advice history when providing advice. For example, the service provider selects the optimal service delivery method based on the manager's past advice history. For example, the service provider can select a service delivery method specialized for a specific category from the manager's past advice history. For example, the service provider can analyze the manager's past advice history and select the most efficient service delivery method. This allows the service provider to select the optimal service delivery method by utilizing past advice history. Some or all of the above processes in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input past advice history into a generative AI and have the generative AI perform the selection of a service delivery method.

[0086] The advice delivery unit adjusts the timing of its delivery based on the manager's current situation. For example, if the manager is in a meeting, the advice delivery unit will provide it after the meeting ends. For example, if the manager is on a business trip, the advice delivery unit can provide it according to the situation at the destination. For example, if the manager is on vacation, the advice delivery unit can provide it after the vacation ends. This ensures that advice is provided at an appropriate time according to the manager's situation. Some or all of the above processing in the advice delivery unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice delivery unit can input the manager's current situation into the generative AI and have the generative AI adjust the timing of the delivery.

[0087] The search unit estimates the emotions of new managers and adjusts the display method of search results based on the estimated emotions. For example, if a new manager is nervous, the search unit can provide a simple and highly visible display method. For example, if a new manager is relaxed, the search unit can provide a display method that includes detailed information. For example, if a new manager is in a hurry, the search unit can provide a display method that gets straight to the point. This allows for the provision of appropriate search result display methods according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using a generative AI, or not using a generative AI. For example, the search unit can input the emotions of new managers into a generative AI and have the generative AI perform emotion estimation.

[0088] The search unit optimizes the search algorithm by referring to past search data during a search. For example, the search unit selects the optimal search algorithm based on past search data. For example, the search unit can apply a search algorithm specialized for a specific category based on past search data. For example, the search unit can analyze past search data and select the most efficient search algorithm. This allows the search algorithm to be optimized by utilizing past search data. Some or all of the above processes in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input past search data into a generative AI and have the generative AI perform the optimization of the search algorithm.

[0089] The search unit applies different search methods depending on the category of the consultation content during the search. For example, if the consultation content is related to interpersonal relationships, the search unit applies a search method specific to that category. For example, if the consultation content is related to improving work efficiency, the search unit can apply a search method specific to that category. For example, if the consultation content is related to career paths, the search unit can apply a search method specific to that category. This allows the most suitable search method to be applied for each category. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input the category of the consultation content into the generative AI and have the generative AI select a search method for each category.

[0090] The search unit estimates the emotions of a new manager and prioritizes search results based on the estimated emotions. For example, if the new manager is nervous, the search unit may prioritize displaying important search results. For example, if the new manager is relaxed, the search unit may display detailed search results. For example, if the new manager is in a hurry, the search unit may display search results quickly. This provides appropriate priority of search results according to emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using a generative AI, or not using a generative AI. For example, the search unit may input the new manager's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The search unit improves search accuracy by referring to the manager's past search history during a search. For example, the search unit selects the optimal search algorithm based on the manager's past search history. For example, the search unit can apply a search algorithm specialized for a specific category based on the manager's past search history. For example, the search unit can analyze the manager's past search history and select the most efficient search algorithm. This allows for improved search accuracy by utilizing past search history. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input past search history into a generative AI and have the generative AI perform the optimization of the search algorithm.

[0092] The search unit improves search accuracy by referencing relevant external data during a search. For example, the search unit can improve search accuracy by referencing relevant industry data. For example, the search unit can improve search accuracy by referencing relevant market data. For example, the search unit can improve search accuracy by referencing relevant academic data. In this way, search accuracy can be improved by referencing relevant external data. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input relevant external data into a generative AI and have the generative AI perform optimization of the search algorithm.

[0093] The notification unit estimates the emotions of newly appointed managers and adjusts the notification method based on the estimated emotions. For example, if a newly appointed manager is nervous, the notification unit can provide a simple and intuitive notification method. For example, if a newly appointed manager is relaxed, the notification unit can provide a detailed notification method. For example, if a newly appointed manager is stressed, the notification unit can provide a rapid notification method. This allows for the provision of appropriate notification methods according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using a generative AI, or not using a generative AI. For example, the notification unit can input the emotions of newly appointed managers into a generative AI and have the generative AI perform emotion estimation.

[0094] The notification unit, when issuing a notification, selects the optimal notification method by referring to the manager's past notification history. For example, the notification unit can select the optimal notification method based on the manager's past notification history. For example, the notification unit can select a notification method specialized for a specific category from the manager's past notification history. For example, the notification unit can analyze the manager's past notification history and select the most efficient notification method. This allows the optimal notification method to be selected by utilizing past notification history. Some or all of the above processing in the notification unit may be performed using, for example, a generation AI, or without a generation AI. For example, the notification unit can input past notification history into a generation AI and have the generation AI perform the selection of a notification method.

[0095] The notification unit estimates the emotions of new managers and determines the priority of notifications based on the estimated emotions. For example, if a new manager is nervous, the notification unit may prioritize important notifications. For example, if a new manager is relaxed, the notification unit may provide detailed notifications. For example, if a new manager is stressed, the notification unit may provide notifications quickly. This allows for the provision of appropriate notification priorities according to emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using or without a generative AI. For example, the notification unit may input the new manager's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0096] The notification unit selects the optimal notification method when sending a notification, taking into account the manager's device information. For example, if the manager is using a smartphone, the notification unit can provide a notification method that matches the screen size. For example, if the manager is using a tablet, the notification unit can provide a notification method optimized for a larger screen. For example, if the manager is using a desktop computer, the notification unit can provide a more detailed notification method. This allows the notification unit to provide the optimal notification method according to the device information. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can input the manager's device information into a generative AI and have the generative AI select the notification method.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The advice provision system may further include a feedback collection unit. The feedback collection unit collects feedback from newly appointed managers after they have actually used the advice provided. For example, the feedback collection unit can collect opinions on the effectiveness of the advice and areas for improvement. The feedback collection unit can collect feedback using methods such as questionnaires, free-response formats, or voice input formats. This allows the advice provision system to improve the quality of its advice based on the collected feedback. Some or all of the above-described processes in the feedback collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback collection unit can input the collected feedback into a generative AI and have the generative AI perform an analysis of the feedback.

[0099] The advice-providing system may further include an emotion analysis unit. The emotion analysis unit analyzes the emotions of a newly appointed manager upon receiving the advice provided. For example, the emotion analysis unit can estimate emotions using facial recognition technology or voice analysis technology. For example, the emotion analysis unit can analyze the sense of security or anxiety felt by the newly appointed manager upon receiving the advice. This allows the advice-providing system to adjust its advice delivery method based on emotions. Some or all of the above processing in the emotion analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion analysis unit can input emotion data into a generative AI and have the generative AI perform the emotion analysis.

[0100] The advice provision system may further include a schedule management unit. The schedule management unit manages the schedules of newly appointed managers and optimizes the timing of advice provision. For example, the schedule management unit can provide advice at an appropriate time, taking into account the manager's meeting and business trip schedules. For example, the schedule management unit can provide advice while avoiding busy times for managers. This allows the advice provision system to provide advice at the optimal time according to the manager's schedule. Some or all of the above processing in the schedule management unit may be performed using, for example, a generative AI, or without a generative AI. For example, the schedule management unit can input schedule data into a generative AI and have the generative AI perform the optimization of the provision timing.

[0101] The advice-providing system may further include a personalization unit. The personalization unit customizes advice based on the individual needs and preferences of newly appointed managers. For example, the personalization unit can provide individually optimized advice based on the manager's past consultations and feedback. The personalization unit can adjust the content and format of the advice according to the manager's personality and communication style, for example. This allows the advice-providing system to provide more effective advice tailored to individual needs. Some or all of the above-described processes in the personalization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the personalization unit can input individual data into a generative AI and have the generative AI perform the customization of the advice.

[0102] The advice-providing system may further include an emotional feedback unit. The emotional feedback unit provides feedback on how newly appointed managers felt about the advice they received. For example, the emotional feedback unit can record the manager's feelings after receiving the advice and reflect them in future advice provision. For example, if the manager had positive feelings about the advice, the emotional feedback unit can maintain the format and content of that advice. For example, if the manager had negative feelings about the advice, the emotional feedback unit can improve the format and content of that advice. This allows the advice-providing system to improve the quality of advice based on emotions. Some or all of the above processing in the emotional feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotional feedback unit can input emotional data into a generative AI and have the generative AI perform the analysis of the feedback.

[0103] The advice provision system may further include a real-time monitoring unit. The real-time monitoring unit provides advice in real time while a newly appointed manager is consulting with a subordinate. For example, the real-time monitoring unit can provide advice at the appropriate time while the manager is conversing with a subordinate. For example, the real-time monitoring unit can instantly generate and provide advice while the manager is listening to the subordinate's consultation. This allows the advice provision system to support real-time responses and enable managers to provide appropriate advice immediately. Some or all of the above-described processes in the real-time monitoring unit may be performed using, for example, a generation AI, or without a generation AI. For example, the real-time monitoring unit can input the consultation content into a generation AI and have the generation AI perform real-time advice generation.

[0104] The advice-providing system may further include an emotional history unit. The emotional history unit records the past emotional data of newly appointed managers and utilizes it for providing advice. For example, the emotional history unit can record what emotions managers have felt in the past and use this as a reference when providing advice in the future. The emotional history unit can also record situations in which managers have felt stressed in the past and use this for providing advice in similar situations. This allows the advice-providing system to provide more appropriate advice based on past emotional data. Some or all of the above processing in the emotional history unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the emotional history unit can input emotional data into a generative AI and have the generative AI perform an analysis of the emotional history.

[0105] The advice provision system may further include a context analysis unit. The context analysis unit analyzes background information of the consultation content to provide more accurate advice. For example, the context analysis unit can analyze the business situation and interpersonal relationships that underlie the consultation content. If the consultation content is related to a specific project, the context analysis unit can analyze the progress of that project and information about the stakeholders. This allows the advice provision system to provide more accurate advice that takes background information into account. Some or all of the above-described processing in the context analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the context analysis unit can input background information into a generative AI and have the generative AI perform the context analysis.

[0106] The advice-providing system may further include an emotion prediction unit. The emotion prediction unit predicts what emotions a newly appointed manager will feel when receiving advice. For example, the emotion prediction unit can predict the emotions a manager will feel when receiving advice based on past emotion data and the current situation. For example, the emotion prediction unit can predict the sense of security or anxiety that a manager may feel when receiving advice. This allows the advice-providing system to adjust the content and method of advice based on the predicted emotions. Some or all of the above processing in the emotion prediction unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the emotion prediction unit can input emotion data into a generative AI and have the generative AI perform emotion prediction.

[0107] The advice provision system may also include a multilingual support unit. The multilingual support unit receives, analyzes, and provides advice based on consultations in different languages. For example, the multilingual support unit can receive consultations in multiple languages, such as English, Spanish, and Chinese. The multilingual support unit can, for example, automatically translate consultations in different languages ​​and input them into the analysis unit. This allows the advice provision system to support managers who speak different languages. Some or all of the above-described processes in the multilingual support unit may be performed using, for example, a generative AI, or not. For example, the multilingual support unit can input translation data into a generative AI to improve the accuracy of the translation.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The input unit inputs the training data. The training data may include, for example, data recording how excellent managers handle consultations with their subordinates. The input unit can input the training data in various formats, such as text data, numerical data, or image data. Step 2: The reception desk inputs the details of the consultation. These may include, for example, consultations regarding work or interpersonal relationships. The reception desk can accept consultation details in various formats, such as text input, voice input, or image input. Step 3: The analysis unit analyzes the consultation content entered by the reception unit. The analysis unit can analyze the consultation content using, for example, natural language processing technology. The analysis unit can also analyze the consultation content using methods such as text analysis, statistical analysis, and machine learning. Step 4: The generation unit generates advice based on the content analyzed by the analysis unit. The generation unit can generate advice using, for example, a generation AI. The generation unit can generate advice in, for example, text format, checklist format, etc. Step 5: The providing unit provides the advice generated by the generating unit. The providing unit can provide the advice in the form of, for example, email notifications, push notifications, or screen displays.

[0110] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0113] Each of the multiple elements described above, including the input unit, reception unit, analysis unit, generation unit, and provision unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the input unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The reception unit is implemented by the reception device 38 of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12. The provision unit is implemented by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the input unit, reception unit, analysis unit, generation unit, and provision unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the input unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The reception unit is implemented by the microphone 238 of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12. The provision unit is implemented by the speaker 240 of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the input unit, reception unit, analysis unit, generation unit, and provision unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the input unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The reception unit is implemented by the microphone 238 of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12. The provision unit is implemented by the speaker 240 of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the 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.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the input unit, reception unit, analysis unit, generation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the input unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The reception unit is implemented by the microphone 238 of the robot 414 or the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12. The provision unit is implemented by the speaker 240 of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0163] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) The input unit for inputting training data, The reception area where you enter the details of your inquiry, An analysis unit analyzes the consultation content entered by the reception unit, A generation unit that generates advice based on the content analyzed by the analysis unit, The system includes a providing unit that provides advice generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is It includes a search function that searches for similar consultation topics from past learning data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, It includes a notification unit that informs newly appointed managers of the generated advice. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned input section is The system estimates the emotions of high-performing managers and selects training data based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned input section is When inputting training data, different data input methods are applied for each category of consultation content. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned input section is When inputting training data, the reliability of the data is evaluated, and reliable data is prioritized for input. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We will estimate the emotions of newly appointed managers and adjust the method of receiving consultations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving a consultation request, we determine the priority of acceptance based on the urgency of the request. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving a consultation request, a different reception algorithm is applied depending on the category of the consultation request. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, We estimate the emotions of newly appointed managers and adjust the analysis method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing the content of a consultation, the analysis algorithm is optimized by referring to past consultation data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When analyzing the content of the consultation, different analysis methods are applied to each category of consultation content. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is Estimate the emotions of newly appointed managers and adjust the advice generation method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating advice, the generation algorithm is optimized by referring to past advice data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating advice, different generation methods are applied depending on the category of the consultation topic. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, Estimate the emotions of newly appointed managers and adjust the way advice is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing advice, refer to the manager's past advice history to select the most appropriate method of delivery. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing advice, adjust the timing based on the manager's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned search unit, The system estimates the emotions of newly appointed managers and adjusts how search results are displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 20) The aforementioned search unit, During a search, the search algorithm is optimized by referring to past search data. The system described in Appendix 2, characterized by the features described herein. (Note 21) The aforementioned search unit, When searching, different search methods are applied depending on the category of the inquiry. The system described in Appendix 2, characterized by the features described herein. (Note 22) The aforementioned search unit, The system estimates the emotions of newly appointed managers and prioritizes search results based on these estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 23) The aforementioned search unit, When performing a search, the system improves search accuracy by referencing the past search history of managers. The system described in Appendix 2, characterized by the features described herein. (Note 24) The aforementioned search unit, When searching, we refer to relevant external data to improve search accuracy. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned notification unit, Estimate the emotions of newly appointed managers and adjust the notification method based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 26) The aforementioned notification unit, When sending a notification, the system will refer to the manager's past notification history to select the most suitable notification method. The system described in Appendix 3, characterized by the features described herein. (Note 27) The aforementioned notification unit, Estimate the emotions of newly appointed managers and determine the priority of notifications based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 28) The aforementioned notification unit, When sending notifications, the system will select the most suitable notification method, taking into account the device information of the manager. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The input unit for inputting training data, The reception area where you enter the details of your inquiry, An analysis unit analyzes the consultation content entered by the reception unit, A generation unit that generates advice based on the content analyzed by the analysis unit, The system includes a providing unit that provides advice generated by the generation unit. A system characterized by the following features.

2. The generating unit is It includes a search function that searches for similar consultation topics from past learning data. The system according to feature 1.

3. The aforementioned supply unit is, It includes a notification unit that informs newly appointed managers of the generated advice. The system according to feature 1.

4. The aforementioned input section is The system estimates the emotions of high-performing managers and selects training data based on these estimated emotions. The system according to feature 1.

5. The aforementioned input section is When inputting training data, different data input methods are applied for each category of consultation content. The system according to feature 1.

6. The aforementioned input section is When inputting training data, the reliability of the data is evaluated, and reliable data is prioritized for input. The system according to feature 1.

7. The aforementioned reception unit is We will estimate the emotions of newly appointed managers and adjust the method of receiving consultations based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is When receiving a consultation request, we determine the priority of acceptance based on the urgency of the request. The system according to feature 1.

9. The aforementioned reception unit is When receiving a consultation request, a different reception algorithm is applied depending on the category of the consultation request. The system according to feature 1.

10. The aforementioned analysis unit, We estimate the emotions of newly appointed managers and adjust the analysis method based on those estimated emotions. The system according to feature 1.

Images