system

Abstract

The system according to this embodiment aims to automatically generate optimal responses and suggestions by utilizing past communication history. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a generation unit. The collection unit collects past communication history. The analysis unit analyzes the data collected by the collection unit. The generation unit automatically generates optimal responses and suggestions based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is 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 the description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 conventional technology, it has not been fully carried out to automatically generate optimal answers or proposals by utilizing the past communication history, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically generate optimal answers or proposals by utilizing the past communication history.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects the past communication history. The analysis unit analyzes the data collected by the collection unit. The generation unit automatically generates an optimal answer or proposal based on the analysis result obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate optimal responses and suggestions by utilizing past communication history. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 communication history analysis system according to an embodiment of the present invention is a system that uses AI to automatically analyze past communication history (emails, chats, meeting notes, etc.) with the seconded company and provides optimal answers and suggestions for each seconded company. The communication history analysis system collects and analyzes past communication history with the seconded company. Next, the AI ​​learns the preferences, requests, frequently asked questions, and issues of each seconded company, and automatically generates appropriate follow-up and answers in real time based on this. This allows seconded employees and staff to respond based on the history and maintain a smooth relationship with the seconded company. For example, the communication history analysis system collects past communication history with the seconded company. In this process, it collects data such as emails, chats, and meeting notes. For example, it collects email exchanges, chat logs, and meeting notes with the seconded company. This allows the system to understand the past communication history with the seconded company. Next, the AI ​​analyzes the collected data. The AI ​​analyzes the collected data and learns the preferences, requests, frequently asked questions, and issues of each seconded company. For example, if a seconded company frequently asks questions or has specific requests, the AI ​​learns this. This allows for an understanding of the characteristics of each seconded company. Furthermore, based on what the AI ​​has learned, it automatically generates appropriate follow-ups and responses in real time. For example, it can automatically generate the optimal answer to a question from a seconded company based on past communication history. It can also automatically generate proposals tailored to the seconded company's needs. This enables seconded employees and their managers to respond quickly and appropriately. This system allows seconded employees and their managers to respond based on history and maintain smooth relationships with seconded companies. For example, the AI ​​can automatically provide appropriate follow-ups without the seconded company having to re-examine past proposals. In addition, by making proposals tailored to the seconded company's preferences and needs, it can improve the seconded company's satisfaction. In this way, the communication history analysis system can provide optimal answers and proposals based on past communication history with seconded companies.

[0029] The communication history analysis system according to this embodiment comprises a collection unit, an analysis unit, and a generation unit. The collection unit collects past communication history. The collection unit can collect data such as emails, chats, and meeting notes. For example, the collection unit can collect email exchanges with the client company. The collection unit can also collect chat logs. Furthermore, the collection unit can also collect meeting notes. For example, the collection unit can automatically collect and save email data. The collection unit can also periodically collect and save chat application logs. Furthermore, the collection unit can automatically collect and save meeting notes. The analysis unit analyzes the data collected by the collection unit. The analysis unit can learn, for example, the preferences and requests of each client company, frequently asked questions, and issues. For example, the analysis unit can learn the topics that clients frequently ask. The analysis unit can also learn the specific requests of clients. Furthermore, the analysis unit can learn the issues of clients. For example, the analysis unit analyzes the content of past emails to extract the preferences and requests of the client company. The analysis unit can also analyze chat logs to identify frequently asked questions. Furthermore, the analysis unit can analyze meeting notes to understand the client company's challenges. The generation unit automatically generates optimal answers and suggestions based on the analysis results obtained by the analysis unit. For example, the generation unit can automatically generate optimal answers to questions from the client company based on past communication history. For example, the generation unit generates answers to questions from the client company based on the content of past emails. The generation unit can also generate answers to questions from the client company based on chat logs. Furthermore, the generation unit can generate answers to questions from the client company based on meeting notes. For example, the generation unit analyzes the content of past emails to generate optimal answers. The generation unit can also analyze chat logs to generate optimal answers. Furthermore, the generation unit can analyze meeting notes to generate optimal answers. As a result, the communication history analysis system according to this embodiment can provide optimal answers and suggestions based on past communication history with the client company.

[0030] The data collection unit collects past communication history. For example, it can collect data such as emails, chats, and meeting notes. Specifically, it automatically collects and stores email data from the company's email server and cloud storage. This includes detailed information such as sender, recipient, date and time sent, subject, and body. The data collection unit can also periodically collect and store chat logs using chat application APIs. This includes chat participants, message sending date and time, and message content. Furthermore, the data collection unit can automatically collect and store meeting notes. For example, it can use online meeting tool APIs to retrieve and store meeting minutes and notes. This includes meeting participants, date and time, agenda, and minutes content. The data collection unit centrally manages and securely stores this data. The frequency and scope of data collection can be flexibly adjusted according to system settings. For example, it is possible to set the system to collect only data within a specific period or only data containing specific keywords. This allows the data collection unit to efficiently collect the necessary data and improve the overall system performance.

[0031] The analysis department analyzes the data collected by the data collection department. For example, the analysis department can learn about the preferences, requests, frequently asked questions, and challenges of each seconded company. Specifically, the analysis department uses natural language processing technology to analyze the content of emails and extract the preferences and requests of seconded companies. For example, it uses text mining technology to identify frequently occurring keywords and phrases from the email body to understand the interests and requests of seconded companies. The analysis department can also analyze chat logs to identify frequently asked questions. For example, it can cluster the content of chat logs and group similar questions and requests to identify topics that seconded companies are frequently interested in. Furthermore, the analysis department can analyze meeting notes to understand the challenges of seconded companies. For example, it can analyze the content of meeting minutes to extract the issues discussed and the solutions proposed. The analysis department integrates this data to create a profile for each seconded company. The profile includes the preferences, requests, frequently asked questions, and challenges of seconded companies, and based on this, more accurate analysis becomes possible. Furthermore, the analytics department can use machine learning algorithms to learn patterns from past data and predict future trends and risks. This allows the analytics department to quickly and accurately analyze the collected data and understand the needs and challenges of the client company.

[0032] The generation unit automatically generates optimal answers and suggestions based on the analysis results obtained by the analysis unit. For example, the generation unit can automatically generate optimal answers to questions from the client company based on past communication history. Specifically, the generation unit uses natural language generation technology to analyze the content of past emails and generate answers to questions from the client company. For example, it extracts answers to similar questions from past email exchanges and generates new answers based on them. The generation unit can also generate answers to questions from the client company based on chat logs. For example, it analyzes the content of chat logs and generates optimal answers to frequently asked questions. Furthermore, the generation unit can also generate answers to questions from the client company based on meeting notes. For example, it analyzes the content of meeting notes and generates optimal answers based on the issues discussed and solutions proposed. The generation unit automatically generates these answers and provides them to the client company. The generation unit can also evaluate the quality of the generated answers and make corrections as needed. For example, if the generated answer is not appropriate, it re-analyzes past data and generates a new answer. Furthermore, the generation unit can collect user feedback and continuously improve the accuracy of its generation algorithm. This allows the generation unit to provide optimal answers and suggestions based on past communication history with the client company, enabling it to respond quickly and accurately to the client company's needs.

[0033] The collection unit can collect data such as emails, chats, and meeting notes. For example, the collection unit can automatically collect and store email data. The collection unit can also periodically collect and store logs from chat applications. The collection unit can also automatically collect and store meeting notes. This allows for more comprehensive analysis by collecting diverse communication histories. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or not. For example, the collection unit can input email data into a generating AI, which can then automatically collect and store the data.

[0034] The analysis department can learn the preferences, requests, frequently asked questions, and challenges of each seconded client. For example, the analysis department can learn the questions that seconded clients frequently ask. The analysis department can also learn the specific requests of seconded clients. The analysis department can also learn the challenges of seconded clients. For example, the analysis department can analyze the content of past emails to extract the preferences and requests of seconded clients. The analysis department can also analyze chat logs to identify frequently asked questions. Furthermore, the analysis department can analyze meeting notes to understand the challenges of seconded clients. This allows for more appropriate responses by understanding the characteristics of each seconded client. Some or all of the above processing in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input the content of past emails into a generating AI, which can automatically analyze the data and extract the preferences and requests of seconded clients.

[0035] The generation unit can automatically generate appropriate follow-ups and responses in real time based on what it has learned. For example, the generation unit can automatically generate the optimal response to a question from a client company based on past communication history. For example, the generation unit can generate a response to a question from a client company based on the content of past emails. The generation unit can also generate a response to a question from a client company based on chat logs. For example, the generation unit can generate a response to a question from a client company based on meeting notes. This enables a rapid response by providing appropriate follow-ups and responses in real time. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the content of past emails into a generation AI, and the generation AI can automatically generate the optimal response.

[0036] The generation unit can automatically generate proposals that meet the requirements of the client company. For example, the generation unit can automatically generate the optimal proposal based on the client company's requirements. For example, the generation unit can generate proposals that meet the client company's requirements based on the content of past emails. For example, the generation unit can also generate proposals that meet the client company's requirements based on chat logs. For example, the generation unit can also generate proposals that meet the client company's requirements based on meeting notes. By making proposals that meet the client company's requirements, it is possible to improve the client company's satisfaction. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the content of past emails into a generation AI, and the generation AI can automatically generate the optimal proposal.

[0037] The generation unit can automatically generate the optimal response based on past communication history. For example, the generation unit can generate the optimal response to a question from the client company based on the content of past emails. The generation unit can also generate the optimal response to a question from the client company based on chat logs. The generation unit can also generate the optimal response to a question from the client company based on meeting notes. This allows for the maintenance of a smooth relationship with the client company by providing the optimal response based on past communication history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the content of past emails into a generation AI, and the generation AI can automatically generate the optimal response.

[0038] The data collection unit can analyze past communication history and select the optimal collection method. For example, the data collection unit can analyze past email exchanges and prioritize the collection of important emails. For example, the data collection unit can analyze past chat logs and collect data based on frequently used keywords. For example, the data collection unit can analyze past meeting minutes and prioritize the collection of notes related to important agenda items. This allows for the selection of an efficient collection method by analyzing past history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the content of past emails into a generating AI, which can then automatically select the optimal collection method.

[0039] The collection unit can filter communication history based on the current projects and areas of interest of the seconded employee. For example, the collection unit can prioritize collecting emails related to the current projects of the seconded employee. The collection unit can also prioritize collecting chat logs related to the areas of interest of the seconded employee. The collection unit can also prioritize collecting meeting notes related to the current projects of the seconded employee. This allows for the collection of highly relevant history by filtering based on the current projects and areas of interest of the seconded employee. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input project information from the seconded employee into a generating AI, which can then automatically filter the relevant history.

[0040] The collection unit can prioritize the collection of highly relevant history based on the geographical location information of the dispatch destination when collecting communication history. For example, the collection unit can prioritize the collection of history related to nearby projects based on the geographical location information of the dispatch destination. The collection unit can also prioritize the collection of history related to region-specific issues based on the geographical location information of the dispatch destination. The collection unit can also prioritize the collection of history related to local events based on the geographical location information of the dispatch destination. This allows for addressing region-specific issues by prioritizing the collection of highly relevant history based on geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the geographical location information of the dispatch destination into a generating AI, and the generating AI can automatically prioritize the collection of relevant history.

[0041] The collection unit can analyze the social media activity of the seconded company when collecting communication history and collect relevant history. For example, the collection unit can analyze the social media activity of the seconded company and prioritize the collection of relevant emails. The collection unit can also analyze the social media activity of the seconded company and prioritize the collection of relevant chat logs. The collection unit can also analyze the social media activity of the seconded company and prioritize the collection of relevant meeting notes. This allows for the collection of highly relevant history by analyzing social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input the social media activity data of the seconded company into a generating AI, and the generating AI can automatically collect relevant history.

[0042] The analysis unit can adjust the level of detail of its analysis based on the importance of the seconded company. For example, the analysis unit will perform a detailed analysis for important seconded companies. For example, the analysis unit can perform a simplified analysis for seconded companies of lower importance. The analysis unit can also adjust the level of detail of its analysis based on the importance of the seconded company's project. This allows for efficient analysis by adjusting the level of detail based on the importance of the seconded company. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of the seconded companies into a generating AI, and the generating AI can automatically adjust the level of detail of the analysis.

[0043] The analysis department can apply different analysis algorithms depending on the category of the seconded company during analysis. For example, if the seconded company is in the technology sector, the analysis department will apply a technology-specific analysis algorithm. If the seconded company is in the marketing sector, the analysis department can also apply a marketing-specific analysis algorithm. If the seconded company is in the financial sector, the analysis department can also apply an analysis algorithm specialized in financial data. By applying an analysis algorithm appropriate to the category of the seconded company, more appropriate analysis results can be provided. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input the category data of the seconded company into a generating AI, and the generating AI can automatically apply an appropriate analysis algorithm.

[0044] The analysis department can prioritize its analysis based on the submission timing of communication histories at the client company. For example, the analysis department might prioritize analyzing recent communication histories. It might also postpone analyzing older histories. Furthermore, it might prioritize analyzing histories related to important projects. This allows for more efficient analysis by prioritizing analysis based on submission timing. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department could input submission timing data into a generating AI, which could then automatically determine the analysis priorities.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the seconded company during the analysis process. For example, the analysis unit may prioritize analyzing history with a high relevance to the seconded company. For example, the analysis unit may postpone analyzing history with a low relevance to the seconded company. The analysis unit may also adjust the order of analysis based on the relevance of the seconded company's projects. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the seconded company. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance data of the seconded company into a generating AI, and the generating AI can automatically adjust the order of analysis.

[0046] The generation unit can adjust the level of detail of the generated content based on the importance of the seconded client during the generation process. For example, the generation unit generates detailed responses for important seconded clients. For example, the generation unit can also generate concise responses for less important seconded clients. The generation unit can also adjust the level of detail of the generated content based on the importance of the seconded client's project. This allows for efficient responses by adjusting the level of detail of the generated content based on the importance of the seconded client. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance data of the seconded client into a generation AI, which can then automatically adjust the level of detail of the content.

[0047] The generation unit can apply different generation algorithms depending on the category of the seconded company during generation. For example, if the seconded company is in the technical field, the generation unit can apply an algorithm that generates technical answers. If the seconded company is in the marketing field, the generation unit can also apply an algorithm that generates marketing-specific answers. If the seconded company is in the financial field, the generation unit can also apply an algorithm that generates answers specialized in financial data. By applying a generation algorithm appropriate to the category of the seconded company, more appropriate answers and suggestions can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category data of the seconded company into a generation AI, and the generation AI can automatically apply an appropriate generation algorithm.

[0048] The generation unit can determine the priority of the content to be generated based on the submission timing of the client's communication history during generation. For example, the generation unit can generate responses based on recent communication history. For example, the generation unit can postpone older submission histories. For example, the generation unit can generate responses based on histories related to important projects. This enables efficient responses by determining the priority of the content to be generated based on the submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input submission timing data into a generation AI, which can automatically determine the priority of the content.

[0049] The generation unit can adjust the order of the generated content based on the relevance of the seconded company during generation. For example, the generation unit generates responses based on history with high relevance to the seconded company. The generation unit can also, for example, postpone history with low relevance to the seconded company. The generation unit can also adjust the order of the generated content based on the relevance of the seconded company's projects. This allows for efficient responses by adjusting the order of the generated content based on the relevance of the seconded company. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance data of the seconded company into a generation AI, and the generation AI can automatically adjust the order of the content.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The communication history analysis system can also include a feedback collection unit. The feedback collection unit collects feedback from the client company and provides it to the analysis unit. For example, the feedback collection unit can automatically collect and store feedback provided by the client company. Furthermore, the feedback collection unit can analyze the feedback provided by the client company and extract information on the client company's satisfaction level and areas for improvement. In addition, the feedback collection unit can provide data based on the client company's feedback to improve the quality of responses and suggestions provided by the generation unit. This enables more appropriate responses by utilizing feedback from the client company.

[0052] The communication history analysis system can also be equipped with a predictive unit. This unit predicts future requests and questions from the client company based on past communication history. For example, it can analyze the content of past emails to predict what the client company is most likely to ask next. It can also analyze chat logs to predict the information the client company will need next. Furthermore, it can analyze meeting notes to predict the challenges the client company will face next. This allows the predictive unit to anticipate the client company's needs in advance, enabling a quick and appropriate response.

[0053] The communication history analysis system can also be equipped with a notification unit. This notification unit will notify the responsible person in real time of important communications from the client company. For example, the notification unit can detect urgent emails from the client company and immediately notify the responsible person. It can also detect important chat messages from the client company and notify the responsible person. Furthermore, it can detect important meeting notes from the client company and notify the responsible person. This ensures that the notification unit does not miss important communications and enables a quick response.

[0054] The communication history analysis system can also include a learning component. This component provides learning content to improve the communication skills of the assigned personnel, based on their communication history at the client company. For example, the learning component can analyze past email exchanges and identify areas for improvement. It can also analyze chat logs and provide advice for more effective communication. Furthermore, it can analyze meeting notes and provide hints for making better proposals. In this way, the learning component supports the skill development of the assigned personnel and facilitates smooth communication with the client company.

[0055] The communication history analysis system can also include a trend analysis unit. This unit analyzes trends based on the communication history of the client company and provides the results to the responsible personnel. For example, the trend analysis unit can analyze the content of past emails to identify the client company's interests and trends. It can also analyze chat logs to identify topics frequently mentioned by the client company. Furthermore, it can analyze meeting minutes to predict future trends at the client company. This allows the trend analysis unit to understand the client company's trends and respond more appropriately.

[0056] The communication history analysis system can also include a performance evaluation unit. This unit evaluates performance and provides feedback based on the employee's interaction history. For example, it can analyze past email exchanges to evaluate the quality of the employee's responses. It can also analyze chat logs to evaluate the speed and accuracy of the employee's responses. Furthermore, it can analyze meeting minutes to evaluate the effectiveness of the employee's proposals. This allows the performance evaluation unit to support the improvement of the employee's skills and facilitate smooth communication with client companies.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The collection unit collects past communication history. For example, the collection unit can collect data such as emails, chats, and meeting notes. Specifically, it automatically collects and saves email exchanges with the client company, chat logs, and meeting notes. Step 2: The analysis department analyzes the data collected by the data collection department. For example, the analysis department learns the preferences, requests, frequently asked questions, and challenges of each seconded company. Specifically, they analyze past email content, chat logs, and meeting notes to understand the preferences, requests, frequently asked questions, and challenges of each seconded company. Step 3: The generation unit automatically generates optimal answers and suggestions based on the analysis results obtained by the analysis unit. For example, the generation unit generates the best answers to questions from the client company based on past email content, chat logs, and meeting notes.

[0059] (Example of form 2) The communication history analysis system according to an embodiment of the present invention is a system that uses AI to automatically analyze past communication history (emails, chats, meeting notes, etc.) with the seconded company and provides optimal answers and suggestions for each seconded company. The communication history analysis system collects and analyzes past communication history with the seconded company. Next, the AI ​​learns the preferences, requests, frequently asked questions, and issues of each seconded company, and automatically generates appropriate follow-up and answers in real time based on this. This allows seconded employees and staff to respond based on the history and maintain a smooth relationship with the seconded company. For example, the communication history analysis system collects past communication history with the seconded company. In this process, it collects data such as emails, chats, and meeting notes. For example, it collects email exchanges, chat logs, and meeting notes with the seconded company. This allows the system to understand the past communication history with the seconded company. Next, the AI ​​analyzes the collected data. The AI ​​analyzes the collected data and learns the preferences, requests, frequently asked questions, and issues of each seconded company. For example, if a seconded company frequently asks questions or has specific requests, the AI ​​learns this. This allows for an understanding of the characteristics of each seconded company. Furthermore, based on what the AI ​​has learned, it automatically generates appropriate follow-ups and responses in real time. For example, it can automatically generate the optimal answer to a question from a seconded company based on past communication history. It can also automatically generate proposals tailored to the seconded company's needs. This enables seconded employees and their managers to respond quickly and appropriately. This system allows seconded employees and their managers to respond based on history and maintain smooth relationships with seconded companies. For example, the AI ​​can automatically provide appropriate follow-ups without the seconded company having to re-examine past proposals. In addition, by making proposals tailored to the seconded company's preferences and needs, it can improve the seconded company's satisfaction. In this way, the communication history analysis system can provide optimal answers and proposals based on past communication history with seconded companies.

[0060] The communication history analysis system according to this embodiment comprises a collection unit, an analysis unit, and a generation unit. The collection unit collects past communication history. The collection unit can collect data such as emails, chats, and meeting notes. For example, the collection unit can collect email exchanges with the client company. The collection unit can also collect chat logs. Furthermore, the collection unit can also collect meeting notes. For example, the collection unit can automatically collect and save email data. The collection unit can also periodically collect and save chat application logs. Furthermore, the collection unit can automatically collect and save meeting notes. The analysis unit analyzes the data collected by the collection unit. The analysis unit can learn, for example, the preferences and requests of each client company, frequently asked questions, and issues. For example, the analysis unit can learn the topics that clients frequently ask. The analysis unit can also learn the specific requests of clients. Furthermore, the analysis unit can learn the issues of clients. For example, the analysis unit analyzes the content of past emails to extract the preferences and requests of the client company. The analysis unit can also analyze chat logs to identify frequently asked questions. Furthermore, the analysis unit can analyze meeting notes to understand the client company's challenges. The generation unit automatically generates optimal answers and suggestions based on the analysis results obtained by the analysis unit. For example, the generation unit can automatically generate optimal answers to questions from the client company based on past communication history. For example, the generation unit generates answers to questions from the client company based on the content of past emails. The generation unit can also generate answers to questions from the client company based on chat logs. Furthermore, the generation unit can generate answers to questions from the client company based on meeting notes. For example, the generation unit analyzes the content of past emails to generate optimal answers. The generation unit can also analyze chat logs to generate optimal answers. Furthermore, the generation unit can analyze meeting notes to generate optimal answers. As a result, the communication history analysis system according to this embodiment can provide optimal answers and suggestions based on past communication history with the client company.

[0061] The data collection unit collects past communication history. For example, it can collect data such as emails, chats, and meeting notes. Specifically, it automatically collects and stores email data from the company's email server and cloud storage. This includes detailed information such as sender, recipient, date and time sent, subject, and body. The data collection unit can also periodically collect and store chat logs using chat application APIs. This includes chat participants, message sending date and time, and message content. Furthermore, the data collection unit can automatically collect and store meeting notes. For example, it can use online meeting tool APIs to retrieve and store meeting minutes and notes. This includes meeting participants, date and time, agenda, and minutes content. The data collection unit centrally manages and securely stores this data. The frequency and scope of data collection can be flexibly adjusted according to system settings. For example, it is possible to set the system to collect only data within a specific period or only data containing specific keywords. This allows the data collection unit to efficiently collect the necessary data and improve the overall system performance.

[0062] The analysis department analyzes the data collected by the data collection department. For example, the analysis department can learn about the preferences, requests, frequently asked questions, and challenges of each seconded company. Specifically, the analysis department uses natural language processing technology to analyze the content of emails and extract the preferences and requests of seconded companies. For example, it uses text mining technology to identify frequently occurring keywords and phrases from the email body to understand the interests and requests of seconded companies. The analysis department can also analyze chat logs to identify frequently asked questions. For example, it can cluster the content of chat logs and group similar questions and requests to identify topics that seconded companies are frequently interested in. Furthermore, the analysis department can analyze meeting notes to understand the challenges of seconded companies. For example, it can analyze the content of meeting minutes to extract the issues discussed and the solutions proposed. The analysis department integrates this data to create a profile for each seconded company. The profile includes the preferences, requests, frequently asked questions, and challenges of seconded companies, and based on this, more accurate analysis becomes possible. Furthermore, the analytics department can use machine learning algorithms to learn patterns from past data and predict future trends and risks. This allows the analytics department to quickly and accurately analyze the collected data and understand the needs and challenges of the client company.

[0063] The generation unit automatically generates optimal answers and suggestions based on the analysis results obtained by the analysis unit. For example, the generation unit can automatically generate optimal answers to questions from the client company based on past communication history. Specifically, the generation unit uses natural language generation technology to analyze the content of past emails and generate answers to questions from the client company. For example, it extracts answers to similar questions from past email exchanges and generates new answers based on them. The generation unit can also generate answers to questions from the client company based on chat logs. For example, it analyzes the content of chat logs and generates optimal answers to frequently asked questions. Furthermore, the generation unit can also generate answers to questions from the client company based on meeting notes. For example, it analyzes the content of meeting notes and generates optimal answers based on the issues discussed and solutions proposed. The generation unit automatically generates these answers and provides them to the client company. The generation unit can also evaluate the quality of the generated answers and make corrections as needed. For example, if the generated answer is not appropriate, it re-analyzes past data and generates a new answer. Furthermore, the generation unit can collect user feedback and continuously improve the accuracy of its generation algorithm. This allows the generation unit to provide optimal answers and suggestions based on past communication history with the client company, enabling it to respond quickly and accurately to the client company's needs.

[0064] The collection unit can collect data such as emails, chats, and meeting notes. For example, the collection unit can automatically collect and store email data. The collection unit can also periodically collect and store logs from chat applications. The collection unit can also automatically collect and store meeting notes. This allows for more comprehensive analysis by collecting diverse communication histories. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or not. For example, the collection unit can input email data into a generating AI, which can then automatically collect and store the data.

[0065] The analysis department can learn the preferences, requests, frequently asked questions, and challenges of each seconded client. For example, the analysis department can learn the questions that seconded clients frequently ask. The analysis department can also learn the specific requests of seconded clients. The analysis department can also learn the challenges of seconded clients. For example, the analysis department can analyze the content of past emails to extract the preferences and requests of seconded clients. The analysis department can also analyze chat logs to identify frequently asked questions. Furthermore, the analysis department can analyze meeting notes to understand the challenges of seconded clients. This allows for more appropriate responses by understanding the characteristics of each seconded client. Some or all of the above processing in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input the content of past emails into a generating AI, which can automatically analyze the data and extract the preferences and requests of seconded clients.

[0066] The generation unit can automatically generate appropriate follow-ups and responses in real time based on what it has learned. For example, the generation unit can automatically generate the optimal response to a question from a client company based on past communication history. For example, the generation unit can generate a response to a question from a client company based on the content of past emails. The generation unit can also generate a response to a question from a client company based on chat logs. For example, the generation unit can generate a response to a question from a client company based on meeting notes. This enables a rapid response by providing appropriate follow-ups and responses in real time. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the content of past emails into a generation AI, and the generation AI can automatically generate the optimal response.

[0067] The generation unit can automatically generate proposals that meet the requirements of the client company. For example, the generation unit can automatically generate the optimal proposal based on the client company's requirements. For example, the generation unit can generate proposals that meet the client company's requirements based on the content of past emails. For example, the generation unit can also generate proposals that meet the client company's requirements based on chat logs. For example, the generation unit can also generate proposals that meet the client company's requirements based on meeting notes. By making proposals that meet the client company's requirements, it is possible to improve the client company's satisfaction. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the content of past emails into a generation AI, and the generation AI can automatically generate the optimal proposal.

[0068] The generation unit can automatically generate the optimal response based on past communication history. For example, the generation unit can generate the optimal response to a question from the client company based on the content of past emails. The generation unit can also generate the optimal response to a question from the client company based on chat logs. The generation unit can also generate the optimal response to a question from the client company based on meeting notes. This allows for the maintenance of a smooth relationship with the client company by providing the optimal response based on past communication history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the content of past emails into a generation AI, and the generation AI can automatically generate the optimal response.

[0069] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay collection and collect data when the user is relaxed. For example, if the user is busy, the data collection unit can adjust the timing to match the user's schedule. For example, if the user is concentrating, the data collection unit can adjust the timing to avoid interrupting the user's concentration. By adjusting the data collection timing according to the user's emotions, the user's burden can be reduced. 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 data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into the generative AI, which can then automatically adjust the data collection timing.

[0070] The data collection unit can analyze past communication history and select the optimal collection method. For example, the data collection unit can analyze past email exchanges and prioritize the collection of important emails. For example, the data collection unit can analyze past chat logs and collect data based on frequently used keywords. For example, the data collection unit can analyze past meeting minutes and prioritize the collection of notes related to important agenda items. This allows for the selection of an efficient collection method by analyzing past history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the content of past emails into a generating AI, which can then automatically select the optimal collection method.

[0071] The collection unit can filter communication history based on the current projects and areas of interest of the seconded employee. For example, the collection unit can prioritize collecting emails related to the current projects of the seconded employee. The collection unit can also prioritize collecting chat logs related to the areas of interest of the seconded employee. The collection unit can also prioritize collecting meeting notes related to the current projects of the seconded employee. This allows for the collection of highly relevant history by filtering based on the current projects and areas of interest of the seconded employee. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input project information from the seconded employee into a generating AI, which can then automatically filter the relevant history.

[0072] The data collection unit can estimate the user's emotions and determine the priority of communication history to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may postpone collecting less important history. For example, if the user is relaxed, the data collection unit may prioritize collecting more important history. For example, if the user is in a hurry, the data collection unit may prioritize collecting history that requires immediate attention. This enables efficient data collection by determining the priority of history to collect according to the user's 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 processing described above in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI, and the generative AI can automatically determine the priority of history to collect.

[0073] The collection unit can prioritize the collection of highly relevant history based on the geographical location information of the dispatch destination when collecting communication history. For example, the collection unit can prioritize the collection of history related to nearby projects based on the geographical location information of the dispatch destination. The collection unit can also prioritize the collection of history related to region-specific issues based on the geographical location information of the dispatch destination. The collection unit can also prioritize the collection of history related to local events based on the geographical location information of the dispatch destination. This allows for addressing region-specific issues by prioritizing the collection of highly relevant history based on geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the geographical location information of the dispatch destination into a generating AI, and the generating AI can automatically prioritize the collection of relevant history.

[0074] The collection unit can analyze the social media activity of the seconded company when collecting communication history and collect relevant history. For example, the collection unit can analyze the social media activity of the seconded company and prioritize the collection of relevant emails. The collection unit can also analyze the social media activity of the seconded company and prioritize the collection of relevant chat logs. The collection unit can also analyze the social media activity of the seconded company and prioritize the collection of relevant meeting notes. This allows for the collection of highly relevant history by analyzing social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input the social media activity data of the seconded company into a generating AI, and the generating AI can automatically collect relevant history.

[0075] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can also provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. In this way, by adjusting the presentation of the analysis according to the user's emotions, the analysis results can be provided that are easy for the user to understand. 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 AI, for example, or not using AI. For example, the analysis unit can input the user's emotion data into the generative AI, and the generative AI can automatically adjust the presentation of the analysis.

[0076] The analysis unit can adjust the level of detail of its analysis based on the importance of the seconded company. For example, the analysis unit will perform a detailed analysis for important seconded companies. For example, the analysis unit can perform a simplified analysis for seconded companies of lower importance. The analysis unit can also adjust the level of detail of its analysis based on the importance of the seconded company's project. This allows for efficient analysis by adjusting the level of detail based on the importance of the seconded company. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of the seconded companies into a generating AI, and the generating AI can automatically adjust the level of detail of the analysis.

[0077] The analysis department can apply different analysis algorithms depending on the category of the seconded company during analysis. For example, if the seconded company is in the technology sector, the analysis department will apply a technology-specific analysis algorithm. If the seconded company is in the marketing sector, the analysis department can also apply a marketing-specific analysis algorithm. If the seconded company is in the financial sector, the analysis department can also apply an analysis algorithm specialized in financial data. By applying an analysis algorithm appropriate to the category of the seconded company, more appropriate analysis results can be provided. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input the category data of the seconded company into a generating AI, and the generating AI can automatically apply an appropriate analysis algorithm.

[0078] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. For example, if the user is relaxed, the analysis unit can provide a longer analysis with detailed explanations. For example, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis results. 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the generative AI, which can then automatically adjust the length of the analysis.

[0079] The analysis department can prioritize its analysis based on the submission timing of communication histories at the client company. For example, the analysis department might prioritize analyzing recent communication histories. It might also postpone analyzing older histories. Furthermore, it might prioritize analyzing histories related to important projects. This allows for more efficient analysis by prioritizing analysis based on submission timing. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department could input submission timing data into a generating AI, which could then automatically determine the analysis priorities.

[0080] The analysis unit can adjust the order of analysis based on the relevance of the seconded company during the analysis process. For example, the analysis unit may prioritize analyzing history with a high relevance to the seconded company. For example, the analysis unit may postpone analyzing history with a low relevance to the seconded company. The analysis unit may also adjust the order of analysis based on the relevance of the seconded company's projects. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the seconded company. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance data of the seconded company into a generating AI, and the generating AI can automatically adjust the order of analysis.

[0081] The generation unit can estimate the user's emotions and adjust the expression of the responses and suggestions it generates based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate responses that proceed at a leisurely pace. If the user is in a hurry, the generation unit can also generate responses that emphasize the shortest route. If the user is excited, the generation unit can also generate responses with visually stimulating effects. This allows the system to provide the user with the most suitable responses and suggestions by adjusting the expression according to their 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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI, which can then automatically adjust the expression of the responses and suggestions.

[0082] The generation unit can adjust the level of detail of the generated content based on the importance of the seconded client during the generation process. For example, the generation unit generates detailed responses for important seconded clients. For example, the generation unit can also generate concise responses for less important seconded clients. The generation unit can also adjust the level of detail of the generated content based on the importance of the seconded client's project. This allows for efficient responses by adjusting the level of detail of the generated content based on the importance of the seconded client. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance data of the seconded client into a generation AI, which can then automatically adjust the level of detail of the content.

[0083] The generation unit can apply different generation algorithms depending on the category of the seconded company during generation. For example, if the seconded company is in the technical field, the generation unit can apply an algorithm that generates technical answers. If the seconded company is in the marketing field, the generation unit can also apply an algorithm that generates marketing-specific answers. If the seconded company is in the financial field, the generation unit can also apply an algorithm that generates answers specialized in financial data. By applying a generation algorithm appropriate to the category of the seconded company, more appropriate answers and suggestions can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category data of the seconded company into a generation AI, and the generation AI can automatically apply an appropriate generation algorithm.

[0084] The generation unit can estimate the user's emotions and adjust the length of the responses and suggestions it generates based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise response. If the user is relaxed, the generation unit can also generate a longer response that includes detailed explanations. If the user is excited, the generation unit can also generate a response with visually stimulating effects. This allows the system to provide the user with the most relevant information by adjusting the length of responses and suggestions according to their 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-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI, which can then automatically adjust the length of responses and suggestions.

[0085] The generation unit can determine the priority of the content to be generated based on the submission timing of the client's communication history during generation. For example, the generation unit can generate responses based on recent communication history. For example, the generation unit can postpone older submission histories. For example, the generation unit can generate responses based on histories related to important projects. This enables efficient responses by determining the priority of the content to be generated based on the submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input submission timing data into a generation AI, which can automatically determine the priority of the content.

[0086] The generation unit can adjust the order of the generated content based on the relevance of the seconded company during generation. For example, the generation unit generates responses based on history with high relevance to the seconded company. The generation unit can also, for example, postpone history with low relevance to the seconded company. The generation unit can also adjust the order of the generated content based on the relevance of the seconded company's projects. This allows for efficient responses by adjusting the order of the generated content based on the relevance of the seconded company. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance data of the seconded company into a generation AI, and the generation AI can automatically adjust the order of the content.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The communication history analysis system can also include a feedback collection unit. The feedback collection unit collects feedback from the client company and provides it to the analysis unit. For example, the feedback collection unit can automatically collect and store feedback provided by the client company. Furthermore, the feedback collection unit can analyze the feedback provided by the client company and extract information on the client company's satisfaction level and areas for improvement. In addition, the feedback collection unit can provide data based on the client company's feedback to improve the quality of responses and suggestions provided by the generation unit. This enables more appropriate responses by utilizing feedback from the client company.

[0089] The communication history analysis system can also be equipped with a predictive unit. This unit predicts future requests and questions from the client company based on past communication history. For example, it can analyze the content of past emails to predict what the client company is most likely to ask next. It can also analyze chat logs to predict the information the client company will need next. Furthermore, it can analyze meeting notes to predict the challenges the client company will face next. This allows the predictive unit to anticipate the client company's needs in advance, enabling a quick and appropriate response.

[0090] The communication history analysis system can also include an emotion estimation unit. This unit estimates emotions from the communication history of the seconded employee and provides this information to the analysis unit. For example, the emotion estimation unit can estimate the emotions of the seconded employee from the content and style of emails. It can also estimate the emotions of the seconded employee from the wording and expressions in chat logs. Furthermore, it can estimate the emotions of the seconded employee from the content of meeting notes. This allows the emotion estimation unit to respond more appropriately based on the emotions of the seconded employee.

[0091] The communication history analysis system can also be equipped with a notification unit. This notification unit will notify the responsible person in real time of important communications from the client company. For example, the notification unit can detect urgent emails from the client company and immediately notify the responsible person. It can also detect important chat messages from the client company and notify the responsible person. Furthermore, it can detect important meeting notes from the client company and notify the responsible person. This ensures that the notification unit does not miss important communications and enables a quick response.

[0092] The communication history analysis system can also be equipped with an emotional feedback unit. This unit estimates the emotions of the recipient and provides feedback based on those emotions. For example, if the recipient is feeling stressed, the emotional feedback unit can offer suggestions to help them relax. It can also offer further suggestions if the recipient is satisfied. Furthermore, if the recipient is dissatisfied, the emotional feedback unit can suggest areas for improvement. In this way, the emotional feedback unit can provide feedback tailored to the recipient's emotions, thereby improving their satisfaction.

[0093] The communication history analysis system can also include a learning component. This component provides learning content to improve the communication skills of the assigned personnel, based on their communication history at the client company. For example, the learning component can analyze past email exchanges and identify areas for improvement. It can also analyze chat logs and provide advice for more effective communication. Furthermore, it can analyze meeting notes and provide hints for making better proposals. In this way, the learning component supports the skill development of the assigned personnel and facilitates smooth communication with the client company.

[0094] The communication history analysis system can also be equipped with an emotion monitoring unit. The emotion monitoring unit monitors the emotions of the seconded employee in real time and notifies the person in charge. For example, if the seconded employee is feeling stressed, the emotion monitoring unit can notify the person in charge of that information. It can also notify the person in charge of that if the seconded employee is relaxed. Furthermore, it can notify the person in charge of that if the seconded employee is feeling dissatisfied. In this way, the emotion monitoring unit can support responses that are appropriate to the emotions of the seconded employee and improve the satisfaction level of the seconded employee.

[0095] The communication history analysis system can also include a trend analysis unit. This unit analyzes trends based on the communication history of the client company and provides the results to the responsible personnel. For example, the trend analysis unit can analyze the content of past emails to identify the client company's interests and trends. It can also analyze chat logs to identify topics frequently mentioned by the client company. Furthermore, it can analyze meeting minutes to predict future trends at the client company. This allows the trend analysis unit to understand the client company's trends and respond more appropriately.

[0096] The communication history analysis system can also be equipped with an emotion adjustment unit. This unit estimates the emotions of the recipient and adjusts the tone and content of communication based on those emotions. For example, if the recipient is stressed, the emotion adjustment unit can suggest a calm tone of communication. If the recipient is relaxed, it can suggest a friendly tone of communication. Furthermore, if the recipient is dissatisfied, it can suggest a prompt and courteous response. In this way, the emotion adjustment unit can support communication tailored to the recipient's emotions and improve their satisfaction.

[0097] The communication history analysis system can also include a performance evaluation unit. This unit evaluates performance and provides feedback based on the employee's interaction history. For example, it can analyze past email exchanges to evaluate the quality of the employee's responses. It can also analyze chat logs to evaluate the speed and accuracy of the employee's responses. Furthermore, it can analyze meeting minutes to evaluate the effectiveness of the employee's proposals. This allows the performance evaluation unit to support the improvement of the employee's skills and facilitate smooth communication with client companies.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The collection unit collects past communication history. For example, the collection unit can collect data such as emails, chats, and meeting notes. Specifically, it automatically collects and saves email exchanges with the client company, chat logs, and meeting notes. Step 2: The analysis department analyzes the data collected by the data collection department. For example, the analysis department learns the preferences, requests, frequently asked questions, and challenges of each seconded company. Specifically, they analyze past email content, chat logs, and meeting notes to understand the preferences, requests, frequently asked questions, and challenges of each seconded company. Step 3: The generation unit automatically generates optimal answers and suggestions based on the analysis results obtained by the analysis unit. For example, the generation unit generates the best answers to questions from the client company based on past email content, chat logs, and meeting notes.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] Each of the multiple elements described above, including the collection unit, analysis unit, and generation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects data such as emails, chats, and meeting notes. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to learn the preferences and requests, frequently asked questions, and issues of each dispatch destination. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically generates optimal answers and suggestions based on the analysis results. Some or all of the collection unit, analysis unit, and generation unit may be implemented by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the collection unit, analysis unit, and generation unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects data such as emails, chats, and meeting notes. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to learn the preferences and requests of each assignment, frequently asked questions, and issues. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically generates optimal answers and suggestions based on the analysis results. Some or all of the collection unit, analysis unit, and generation unit may be implemented by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the collection unit, analysis unit, and generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects data such as emails, chats, and meeting notes. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to learn the preferences and requests of each dispatch destination, frequently asked questions, and issues. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates optimal answers and suggestions based on the analysis results. Some or all of the collection unit, analysis unit, and generation unit may be implemented by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the collection unit, analysis unit, and generation unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects data such as emails, chats, and meeting notes. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to learn the preferences and requests of each dispatch destination, frequently asked questions, and issues. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates optimal answers and suggestions based on the analysis results. Some or all of the collection unit, analysis unit, and generation unit may be implemented by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] (Note 1) A collection department that collects past communication history, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a generation unit that automatically generates optimal answers and suggestions based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data such as emails, chats, and meeting notes. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Learn about the preferences, requests, frequently asked questions, and challenges of each assigned company. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Based on what has been learned, it automatically generates appropriate follow-ups and responses in real time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Automatically generates proposals tailored to the requirements of the client company. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is The system automatically generates the optimal response based on past communication history. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of communication history collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past communication history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting communication history, filtering is performed based on the current projects and areas of interest at the seconded company. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of communication history to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting communication history, the system prioritizes collecting highly relevant history based on the geographical location information of the employee's assignment location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting communication history, analyze the social media activity of the seconded company and collect relevant history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of the assigned company. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of the seconded company. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, the priority of the analysis will be determined based on when the communication history of the seconded company was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During the analysis, adjust the order of analysis based on the relevance of the seconded company. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the way responses and suggestions are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the level of detail in the generated content is adjusted based on the importance of the assigned destination. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, different generation algorithms are applied depending on the category of the destination organization. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the length of the responses and suggestions generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the priority of the content to be generated is determined based on the timing of the submission of the communication history from the seconded company. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the order of generated content is adjusted based on the relevance of the seconded organization. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0172] 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. A collection department that collects past communication history, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a generation unit that automatically generates optimal answers and suggestions based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data such as emails, chats, and meeting notes. The system according to feature 1.

3. The aforementioned analysis unit is Learn about the preferences, requests, frequently asked questions, and challenges of each assigned company. The system according to feature 1.

4. The generating unit is Based on what has been learned, it automatically generates appropriate follow-ups and responses in real time. The system according to feature 1.

5. The generating unit is Automatically generates proposals tailored to the requirements of the client company. The system according to feature 1.

6. The generating unit is The system automatically generates the optimal response based on past communication history. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of communication history collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze past communication history and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting communication history, filtering is performed based on the current projects and areas of interest at the seconded company. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and determines the priority of communication history to collect based on the estimated user emotions. The system according to feature 1.

Images