System

Abstract

An object of a system according to an embodiment is to efficiently generate a text optimized for each company.SOLUTION: A system includes a prompt generation unit, a communication tool association unit, an organization structure association unit, and a human relationship association unit. The prompt generator generates an optimized prompt for each enterprise. The communication tool corresponding unit generates a sentence corresponding to a specific communication tool used by the company based on the prompt generated by the prompt generation unit. The organization structure association unit generates a sentence in consideration of the organization structure of the company. The human relationship association unit generates a sentence in consideration of a human relationship in a company.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to efficiently generate texts optimized for each company, posing challenges in improving business efficiency.

[0005] The system according to the embodiment aims to efficiently generate texts that are optimized for each company. [Means for solving the problem]

[0006] The system according to the embodiment includes a prompt generation unit, a communication tool correspondence unit, an organizational structure correspondence unit, and a human relationship correspondence unit. The prompt generation unit generates prompts optimized for each company. The communication tool correspondence unit generates sentences corresponding to specific communication tools used by the company based on the prompts generated by the prompt generation unit. The organizational structure correspondence unit generates sentences taking into account the organizational structure of the company. The human relationship correspondence unit generates sentences taking into account the human relationships within the company. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate texts that are optimized for each company. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The text generation system according to the embodiment of the present invention uses prompts optimized for each company and generates text taking into account the company's internal communication tools, organizational structure, and human relationships, thereby enabling the text generation system to efficiently create text that meets the specific needs and culture of the company.

[0029] A sentence generation system according to an embodiment includes a prompt generation unit, a communication tool support unit, an organizational structure support unit, and a human relationship support unit. The prompt generation unit generates prompts optimized for each company. For example, the prompt generation unit generates optimal prompts taking into account the company's business operations and culture, the communication tools used, organizational structure, human relationships, and the like. The prompt generation unit can also generate prompts in cases where a specific format or phrase is preferred. The communication tool support unit generates sentences compatible with the specific communication tools used by the company based on the prompts generated by the prompt generation unit. For example, the communication tool support unit generates short and concise messages for chat apps and sentences including detailed explanations for emails. The organizational structure support unit generates sentences taking into account the company's organizational structure. For example, the organizational structure support unit uses different words and tones for reports addressed to superiors and request emails addressed to colleagues. The human relationship support unit generates sentences taking into account human relationships within the company. For example, the human relationship support unit uses different levels of friendliness and politeness for messages addressed to long-time colleagues and messages addressed to new employees. As a result, the text generation system according to the embodiment can generate text using prompts optimized for each company, taking into consideration internal communication tools, organizational structure, and human relationships. For example, the system can be effectively used in a variety of tasks, such as reporting project progress, creating meeting minutes, and writing request emails.

[0030] The prompt generation unit can analyze a company's past communication history and automatically extract frequently occurring phrases and expressions to reflect them in the prompts. The prompt generation unit, for example, analyzes a company's past email and chat history to automatically extract frequently occurring phrases and expressions. For example, it extracts expressions that are often used in progress reports on a specific project and reflects them in the prompts. In this way, by analyzing a company's past communication history and automatically extracting frequently occurring phrases and expressions to reflect them in the prompts, it is possible to generate sentences that reflect expressions unique to the company.

[0031] The prompt generation unit can model a company's business processes and dynamically generate prompts that correspond to specific business scenarios. For example, the prompt generation unit models a company's business processes in detail and dynamically generates prompts that correspond to specific business scenarios. For example, it automatically generates a task list that is required at the start of a project. In this way, by modeling a company's business processes and dynamically generating prompts that correspond to specific business scenarios, it is possible to generate sentences that are suited to the business.

[0032] The prompt generation unit generates a prompt optimized for each of the different departments of a company, thereby facilitating communication between the departments. The prompt generation unit generates a prompt optimized for each of the different departments of a company, thereby facilitating communication between the departments. For example, the prompt generation unit provides a prompt that takes into account the technical terms used between the sales department and the engineering department. This generates a prompt optimized for each of the different departments of a company, thereby facilitating communication between the departments, thereby improving work efficiency.

[0033] The prompt generation unit can create a consistent message by taking into consideration the brand guidelines and marketing strategy of the company. The prompt generation unit generates a prompt that creates a consistent message by taking into consideration, for example, the brand guidelines of the company. For example, the prompt generation unit automatically generates a message that includes the company's logo and slogan. In this way, by creating a consistent message by taking into consideration the brand guidelines and marketing strategy of the company, it is possible to achieve effective communication while maintaining the company's brand image.

[0034] The communication tool support unit can analyze the usage patterns of each communication tool and automatically select the optimal message format. The communication tool support unit, for example, analyzes the usage patterns of each communication tool and automatically selects the optimal message format. For example, in a chat app, it generates short and concise messages. In this way, by analyzing the usage patterns of each communication tool and automatically selecting the optimal message format, it is possible to generate messages optimized for each tool.

[0035] The communication tool support unit can generate message templates optimized for the user interface of each tool. The communication tool support unit generates, for example, message templates optimized for the user interface of each communication tool. For example, a chat app provides templates for short sentences. In this way, by generating message templates optimized for the user interface of each tool, it is possible to provide messages appropriate for each tool.

[0036] The communication tool support unit can develop a conversion algorithm for maintaining consistency of messages between different communication tools. The communication tool support unit develops, for example, a conversion algorithm for maintaining consistency of messages between different communication tools. For example, the communication tool support unit converts chat messages into email format. In this way, by developing a conversion algorithm for maintaining consistency of messages between different communication tools, consistency of messages between tools can be maintained.

[0037] The communication tool support unit can monitor the usage status of communication tools in real time and select the optimal tool. The communication tool support unit, for example, builds a system that monitors the usage status of communication tools in real time and selects the optimal tool. For example, it selects a tool based on frequency of use and response speed. In this way, efficient communication can be achieved by monitoring the usage status of communication tools in real time and selecting the optimal tool.

[0038] The organizational structure support unit can model the communication flow within an organization and propose an optimal message route. The organizational structure support unit, for example, models the communication flow within an organization and proposes an optimal message route. For example, it sets an appropriate route so that important information is communicated quickly. In this way, by modeling the communication flow within an organization and proposing an optimal message route, the efficiency of information communication is improved.

[0039] The organizational structure support unit can develop a system that dynamically updates message generation rules in response to organizational changes. The organizational structure support unit develops a system that dynamically updates message generation rules in response to organizational changes, for example. For example, message rules are automatically updated in response to organizational restructuring or personnel transfers. In this way, by developing a system that dynamically updates message generation rules in response to organizational changes, appropriate communication that responds to organizational changes can be achieved.

[0040] The organizational structure support unit can provide message templates to facilitate communication between different departments within an organization. The organizational structure support unit provides, for example, message templates to facilitate communication between different departments within an organization. For example, it provides a common template to be used between the sales department and the engineering department. In this way, by providing message templates to facilitate communication between different departments within an organization, cooperation between departments can be strengthened.

[0041] The human relationship handling unit can build a database of human relationships within the company and generate an optimal message according to each relationship. The human relationship handling unit, for example, builds a database of human relationships within the company and generates an optimal message according to each relationship. For example, it provides a message with an appropriate tone based on the relationship between a superior and a subordinate. In this way, by building a database of human relationships within the company and generating an optimal message according to each relationship, it is possible to realize appropriate communication according to the relationship.

[0042] The human relationship management unit can analyze past communication history and automatically generate a message template based on the relationship. The human relationship management unit, for example, analyzes past communication history and automatically generates a message template based on the relationship. For example, phrases that are frequently used in interactions with superiors are reflected in the template. In this way, by analyzing past communication history and automatically generating a message template based on the relationship, it is possible to provide an appropriate message according to the relationship.

[0043] The human relations department can provide message templates for building relationships with new employees and transferred employees. The human relations department provides, for example, message templates for building relationships with new employees and transferred employees. For example, it provides templates for self-introductions and welcome messages. In this way, by providing message templates for building relationships with new employees and transferred employees, it is possible to support smooth relationship building.

[0044] The Human Relations Department can develop a system that visualizes human relationships within the company and suggests areas for improving communication. The Human Relations Department can, for example, develop a system that visualizes human relationships within the company and suggests areas for improving communication. For example, it can identify areas where cooperation between departments is lacking and propose improvement measures. In this way, by developing a system that visualizes human relationships within the company and suggests areas for improving communication, it is possible to improve communication within the organization.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The text generation system can also learn a user's individual communication style and generate prompts optimized for each individual user. For example, it can learn the words and expressions a user normally uses and customize prompts based on that. It can also analyze a user's past message history to extract phrases that are commonly used in specific situations and reflect these in the prompts. This allows for prompts optimized for each user, enabling more natural and effective communication.

[0047] Text generation systems can also generate prompts optimized for users with different cultural backgrounds. For example, they can provide prompts that use appropriate language and expressions, taking into account the culture and customs of different countries and regions. They can also generate prompts that reflect the values ​​and manners that are important in a particular culture. This can facilitate communication between users with different cultural backgrounds and reduce misunderstandings and friction.

[0048] The text generation system can also learn the user's past behavioral patterns and generate prompts based on predicted behavior. For example, if a user tends to perform a particular task at a particular time of day, it can provide prompts appropriate for that time of day. It can also generate prompts that suggest the next action the user should take based on past choices and decisions. This allows the system to provide appropriate prompts based on the user's behavioral patterns and support efficient work performance.

[0049] The sentence generation system can also monitor the user's health condition and generate prompts according to the health condition. For example, if the user is tired, it can provide a message encouraging them to rest. On the other hand, if the user is healthy and full of energy, it can generate a prompt encouraging them to take positive action. In this way, it can provide appropriate prompts according to the user's health condition and support the user in maintaining their health.

[0050] The text generation system can also analyze the user's learning style and generate optimal learning prompts. For example, it can provide prompts containing diagrams and graphs to a user who prefers visual information. It can also generate prompts that suggest audio messages or podcasts to a user who prefers auditory information. This allows the system to provide appropriate prompts according to the user's learning style and support effective learning.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The prompt generator generates prompts optimized for each company. For example, it generates optimal prompts taking into account the company's business operations, culture, communication tools used, organizational structure, interpersonal relationships, etc. It can also generate prompts in cases where a specific format or wording is preferred. Step 2: The communication tool support unit generates sentences based on the prompts generated by the prompt generation unit, which are appropriate for the specific communication tools used by the company. For example, it generates short and concise messages for chat apps, and sentences with detailed explanations for emails. Step 3: The organizational structure support section generates texts taking into account the company's organizational structure. For example, different words and tones are used for reports to superiors and request emails to colleagues. Step 4: The interpersonal relations section generates texts taking into account interpersonal relations within the company. For example, messages addressed to long-time colleagues may be expressed with different degrees of friendliness and politeness than messages addressed to new employees.

[0053] (Example 2) The text generation system according to the embodiment of the present invention uses prompts optimized for each company and generates text taking into account the company's internal communication tools, organizational structure, and human relationships, thereby enabling the text generation system to efficiently create text that meets the specific needs and culture of the company.

[0054] A sentence generation system according to an embodiment includes a prompt generation unit, a communication tool support unit, an organizational structure support unit, and a human relationship support unit. The prompt generation unit generates prompts optimized for each company. For example, the prompt generation unit generates optimal prompts taking into account the company's business operations and culture, the communication tools used, organizational structure, human relationships, and the like. The prompt generation unit can also generate prompts in cases where a specific format or phrase is preferred. The communication tool support unit generates sentences compatible with the specific communication tools used by the company based on the prompts generated by the prompt generation unit. For example, the communication tool support unit generates short and concise messages for chat apps and sentences including detailed explanations for emails. The organizational structure support unit generates sentences taking into account the company's organizational structure. For example, the organizational structure support unit uses different words and tones for reports addressed to superiors and request emails addressed to colleagues. The human relationship support unit generates sentences taking into account human relationships within the company. For example, the human relationship support unit uses different levels of friendliness and politeness for messages addressed to long-time colleagues and messages addressed to new employees. As a result, the text generation system according to the embodiment can generate text using prompts optimized for each company, taking into consideration internal communication tools, organizational structure, and human relationships. For example, the system can be effectively used in a variety of tasks, such as reporting project progress, creating meeting minutes, and writing request emails.

[0055] The prompt generation unit can analyze a company's past communication history and automatically extract frequently occurring phrases and expressions to reflect them in the prompts. The prompt generation unit, for example, analyzes a company's past email and chat history to automatically extract frequently occurring phrases and expressions. For example, it extracts expressions that are often used in progress reports on a specific project and reflects them in the prompts. In this way, by analyzing a company's past communication history and automatically extracting frequently occurring phrases and expressions to reflect them in the prompts, it is possible to generate sentences that reflect expressions unique to the company.

[0056] The prompt generation unit can model a company's business processes and dynamically generate prompts that correspond to specific business scenarios. For example, the prompt generation unit models a company's business processes in detail and dynamically generates prompts that correspond to specific business scenarios. For example, it automatically generates a task list that is required at the start of a project. In this way, by modeling a company's business processes and dynamically generating prompts that correspond to specific business scenarios, it is possible to generate sentences that are suited to the business.

[0057] The prompt generation unit can use the emotion estimation function to analyze emotional trends in past communications and generate prompts that elicit positive emotions. The prompt generation unit, for example, analyzes past communication data and analyzes emotional trends using the emotion estimation function. For example, phrases that contain a lot of positive emotions are extracted and reflected in the prompts. In this way, better communication can be achieved by using the emotion estimation function to analyze emotional trends in past communications and generate prompts that elicit positive emotions.

[0058] The prompt generation unit generates a prompt optimized for each of the different departments of a company, thereby facilitating communication between the departments. The prompt generation unit generates a prompt optimized for each of the different departments of a company, thereby facilitating communication between the departments. For example, the prompt generation unit provides a prompt that takes into account the technical terms used between the sales department and the engineering department. This generates a prompt optimized for each of the different departments of a company, thereby facilitating communication between the departments, thereby improving work efficiency.

[0059] The prompt generation unit can create a consistent message by taking into consideration the brand guidelines and marketing strategy of the company. The prompt generation unit generates a prompt that creates a consistent message by taking into consideration, for example, the brand guidelines of the company. For example, the prompt generation unit automatically generates a message that includes the company's logo and slogan. In this way, by creating a consistent message by taking into consideration the brand guidelines and marketing strategy of the company, it is possible to achieve effective communication while maintaining the company's brand image.

[0060] The prompt generation unit can use the emotion estimation function to consider the user's real-time emotional state when generating a prompt and generate a sentence in an appropriate tone. The prompt generation unit, for example, uses the emotion estimation function to consider the user's real-time emotional state when generating a prompt. For example, if the user is feeling stressed, the prompt generation unit provides a prompt including words of encouragement. In this way, by using the emotion estimation function to consider the user's real-time emotional state when generating a prompt and generate a sentence in an appropriate tone, appropriate communication according to the user's emotions can be realized.

[0061] The communication tool support unit can analyze the usage patterns of each communication tool and automatically select the optimal message format. The communication tool support unit, for example, analyzes the usage patterns of each communication tool and automatically selects the optimal message format. For example, in a chat app, it generates short and concise messages. In this way, by analyzing the usage patterns of each communication tool and automatically selecting the optimal message format, it is possible to generate messages optimized for each tool.

[0062] The communication tool support unit can generate message templates optimized for the user interface of each tool. The communication tool support unit generates, for example, message templates optimized for the user interface of each communication tool. For example, a chat app provides templates for short sentences. In this way, by generating message templates optimized for the user interface of each tool, it is possible to provide messages appropriate for each tool.

[0063] The communication tool support unit can use the emotion estimation function to analyze the user's emotional response for each tool and propose an optimal message format. The communication tool support unit, for example, uses the emotion estimation function to analyze the user's emotional response for each tool and propose an optimal message format. For example, it generates a message that elicits a positive response in a chat app. In this way, by using the emotion estimation function to analyze the user's emotional response for each tool and propose an optimal message format, it is possible to provide an appropriate message according to the user's emotions.

[0064] The communication tool support unit can develop a conversion algorithm for maintaining consistency of messages between different communication tools. The communication tool support unit develops, for example, a conversion algorithm for maintaining consistency of messages between different communication tools. For example, the communication tool support unit converts chat messages into email format. In this way, by developing a conversion algorithm for maintaining consistency of messages between different communication tools, consistency of messages between tools can be maintained.

[0065] The communication tool support unit can monitor the usage status of communication tools in real time and select the optimal tool. The communication tool support unit, for example, builds a system that monitors the usage status of communication tools in real time and selects the optimal tool. For example, it selects a tool based on frequency of use and response speed. In this way, efficient communication can be achieved by monitoring the usage status of communication tools in real time and selecting the optimal tool.

[0066] The communication tool support unit can use the emotion estimation function to grasp the emotional state of the user for each tool in real time and generate an appropriate message. The communication tool support unit, for example, uses the emotion estimation function to grasp the emotional state of the user for each tool in real time and generate an appropriate message. For example, if the user is feeling stressed, an encouraging message is provided. In this way, by using the emotion estimation function to grasp the emotional state of the user for each tool in real time and generate an appropriate message, appropriate communication according to the user's emotions can be realized.

[0067] The organizational structure support unit can model the communication flow within an organization and propose an optimal message route. The organizational structure support unit, for example, models the communication flow within an organization and proposes an optimal message route. For example, it sets an appropriate route so that important information is communicated quickly. In this way, by modeling the communication flow within an organization and proposing an optimal message route, the efficiency of information communication is improved.

[0068] The organizational structure correspondence unit can use the emotion estimation function to analyze the emotional tendency of each position and generate a message in an appropriate tone. For example, the organizational structure correspondence unit uses the emotion estimation function to analyze the emotional tendency of each position and generate a message in an appropriate tone. For example, a message in a formal and positive tone is generated for a superior. In this way, by using the emotion estimation function to analyze the emotional tendency of each position and generate a message in an appropriate tone, appropriate communication according to the position can be realized.

[0069] The organizational structure support unit can develop a system that dynamically updates message generation rules in response to organizational changes. The organizational structure support unit develops a system that dynamically updates message generation rules in response to organizational changes, for example. For example, message rules are automatically updated in response to organizational restructuring or personnel transfers. In this way, by developing a system that dynamically updates message generation rules in response to organizational changes, appropriate communication that responds to organizational changes can be achieved.

[0070] The organizational structure support unit can provide message templates to facilitate communication between different departments within an organization. The organizational structure support unit provides, for example, message templates to facilitate communication between different departments within an organization. For example, it provides a common template to be used between the sales department and the engineering department. In this way, by providing message templates to facilitate communication between different departments within an organization, cooperation between departments can be strengthened.

[0071] The organizational structure response unit can use the emotion estimation function to monitor the emotional state within the organization in real time and generate appropriate messages. The organizational structure response unit, for example, uses the emotion estimation function to monitor the emotional state within the organization in real time and generate appropriate messages. For example, an encouraging message can be provided to a department experiencing high stress. In this way, by using the emotion estimation function to monitor the emotional state within the organization in real time and generate appropriate messages, appropriate communication can be achieved in accordance with the emotional state of the entire organization.

[0072] The human relationship handling unit can build a database of human relationships within the company and generate an optimal message according to each relationship. The human relationship handling unit, for example, builds a database of human relationships within the company and generates an optimal message according to each relationship. For example, it provides a message with an appropriate tone based on the relationship between a superior and a subordinate. In this way, by building a database of human relationships within the company and generating an optimal message according to each relationship, it is possible to realize appropriate communication according to the relationship.

[0073] The human relationship management unit can analyze past communication history and automatically generate a message template based on the relationship. The human relationship management unit, for example, analyzes past communication history and automatically generates a message template based on the relationship. For example, phrases that are frequently used in interactions with superiors are reflected in the template. In this way, by analyzing past communication history and automatically generating a message template based on the relationship, it is possible to provide an appropriate message according to the relationship.

[0074] The human relationship management unit can use the emotion estimation function to analyze the emotional tendency of each relationship and generate a message in an appropriate tone. The human relationship management unit, for example, uses the emotion estimation function to analyze the emotional tendency of each relationship and generate a message in an appropriate tone. For example, a message in a formal and positive tone is generated for a superior. In this way, by using the emotion estimation function to analyze the emotional tendency of each relationship and generate a message in an appropriate tone, appropriate communication according to the relationship can be realized.

[0075] The human relations department can provide message templates for building relationships with new employees and transferred employees. The human relations department provides, for example, message templates for building relationships with new employees and transferred employees. For example, it provides templates for self-introductions and welcome messages. In this way, by providing message templates for building relationships with new employees and transferred employees, it is possible to support smooth relationship building.

[0076] The Human Relations Department can develop a system that visualizes human relationships within the company and suggests areas for improving communication. The Human Relations Department can, for example, develop a system that visualizes human relationships within the company and suggests areas for improving communication. For example, it can identify areas where cooperation between departments is lacking and propose improvement measures. In this way, by developing a system that visualizes human relationships within the company and suggests areas for improving communication, it is possible to improve communication within the organization.

[0077] The human relationship management unit can use the emotion estimation function to grasp the emotional state of each relationship in real time and generate an appropriate message. The human relationship management unit, for example, uses the emotion estimation function to grasp the emotional state of each relationship in real time and generate an appropriate message. For example, a message with a formal and positive tone is generated for a superior. In this way, by using the emotion estimation function to grasp the emotional state of each relationship in real time and generate an appropriate message, appropriate communication according to the relationship can be realized.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The text generation system can also learn a user's individual communication style and generate prompts optimized for each individual user. For example, it can learn the words and expressions a user normally uses and customize prompts based on that. It can also analyze a user's past message history to extract phrases that are commonly used in specific situations and reflect these in the prompts. This allows for prompts optimized for each user, enabling more natural and effective communication.

[0080] The text generation system can also estimate the user's emotions and adjust the prompts based on the estimated emotions. For example, if the user is feeling stressed, the system can generate prompts that include relaxing language and encouraging messages. If the user is happy, the system can provide positive messages that further enhance the user's emotions. This allows the system to provide appropriate prompts according to the user's emotions and achieve better communication.

[0081] Text generation systems can also generate prompts optimized for users with different cultural backgrounds. For example, they can provide prompts that use appropriate language and expressions, taking into account the culture and customs of different countries and regions. They can also generate prompts that reflect the values ​​and manners that are important in a particular culture. This can facilitate communication between users with different cultural backgrounds and reduce misunderstandings and friction.

[0082] The text generation system can also estimate the user's emotions and adjust the timing of communication based on the estimated emotions. For example, when the user is busy, it can postpone important messages or generate prompts that briefly convey the main points. When the user is relaxed, it can provide prompts with detailed explanations or additional information. This allows for communication to be timed appropriately according to the user's emotional state.

[0083] The text generation system can also learn the user's past behavioral patterns and generate prompts based on predicted behavior. For example, if a user tends to perform a particular task at a particular time of day, it can provide prompts appropriate for that time of day. It can also generate prompts that suggest the next action the user should take based on past choices and decisions. This allows the system to provide appropriate prompts based on the user's behavioral patterns and support efficient work performance.

[0084] The sentence generation system can also estimate the user's emotions and provide feedback based on the estimated emotions. For example, if the user is feeling dissatisfied, the system can identify the cause and generate a prompt that suggests a solution. If the user is satisfied, the system can provide positive feedback that further enhances the user's emotions. This makes it possible to provide appropriate feedback according to the user's emotions and improve user satisfaction.

[0085] The sentence generation system can also monitor the user's health condition and generate prompts according to the health condition. For example, if the user is tired, it can provide a message encouraging them to rest. On the other hand, if the user is healthy and full of energy, it can generate a prompt encouraging them to take positive action. In this way, it can provide appropriate prompts according to the user's health condition and support the user in maintaining their health.

[0086] The text generation system can also estimate the user's emotions and adjust the frequency of communication based on the estimated emotions. For example, if the user is feeling stressed, the system can generate prompts to reduce the frequency of communication and ease the burden. Also, if the user is relaxed, the system can provide prompts to encourage more proactive communication. This makes it possible to achieve communication at an appropriate frequency according to the user's emotional state.

[0087] The text generation system can also analyze the user's learning style and generate optimal learning prompts. For example, it can provide prompts containing diagrams and graphs to a user who prefers visual information. It can also generate prompts that suggest audio messages or podcasts to a user who prefers auditory information. This allows the system to provide appropriate prompts according to the user's learning style and support effective learning.

[0088] The text generation system can also estimate the user's emotions and adjust the content of the communication based on the estimated emotions. For example, if the user is sad, it can provide a message of comfort and encouragement. If the user is happy, it can also generate a message that shares the user's emotions and congratulates them. This makes it possible to communicate with appropriate content according to the user's emotions.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The prompt generator generates prompts optimized for each company. For example, it generates optimal prompts taking into account the company's business operations, culture, communication tools used, organizational structure, interpersonal relationships, etc. It can also generate prompts in cases where a specific format or wording is preferred. Step 2: The communication tool support unit generates sentences based on the prompts generated by the prompt generation unit, which are appropriate for the specific communication tools used by the company. For example, it generates short and concise messages for chat apps, and sentences with detailed explanations for emails. Step 3: The organizational structure support section generates texts taking into account the company's organizational structure. For example, different words and tones are used for reports to superiors and request emails to colleagues. Step 4: The interpersonal relations section generates texts taking into account interpersonal relations within the company. For example, messages addressed to long-time colleagues may be expressed with different degrees of friendliness and politeness than messages addressed to new employees.

[0091] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0093] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0096] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0097] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0098] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0099] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0100] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0101] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0102] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0103] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0104] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0105] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0119] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 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.

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

[0127] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

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

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

[0131] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0132] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0135] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0141] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0142] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0143] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0144] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0145] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0146] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0147] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0148] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0149] 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.

[0150] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0151] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0152] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0153] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0154] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0155] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0156] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0157] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0158] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a prompt generation unit that generates a prompt optimized for each company; a communication tool support unit that generates sentences corresponding to a specific communication tool used by the company based on the prompts generated by the prompt generation unit; an organizational structure correspondence unit that generates sentences taking into account the organizational structure of a company; a human relations handling unit that generates sentences taking into account human relations within a company; A system characterized by:

2. The prompt generation unit Analyze a company's past communication history, automatically extract frequently occurring phrases and expressions, and reflect them in the prompts.

2. The system of claim 1.

3. The communication tool support unit Analyzes the usage patterns of each communication tool and automatically selects the optimal message format 2. The system of claim 1.

4. The organizational structure corresponding unit Analyzes job titles and hierarchical structures within an organization and automatically generates the optimal message format for each level 2. The system of claim 1.

5. The human relationship handling unit Build a database of internal relationships and generate optimal messages for each relationship 2. The system of claim 1.

6. The prompt generation unit Analyzes emotional trends in past communications and generates prompts that elicit positive emotions 2. The system of claim 1.

7. The communication tool support unit Analyze users' emotional reactions to each tool and suggest the optimal message format 2. The system of claim 1.

8. The organizational structure corresponding unit Analyze the emotional trends of each role and create messages with the right tone 2. The system of claim 1.

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