system

Abstract

The system according to the embodiment aims to supplement and correct sentences by taking into account the sender's background. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects background information about the sender. The analysis unit analyzes the text the sender is about to send based on the information collected by the collection unit. The suggestion unit suggests appropriate additions or corrections based on the results of the analysis by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not supplement or modify text to take into account the sender's background, which can lead to misunderstandings in communication.

[0005] The system according to the embodiment aims to supplement and correct sentences by taking into account the sender's background. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects background information about the sender. The analysis unit analyzes the text that the sender is about to send based on the information collected by the collection unit. The suggestion unit suggests appropriate additions or corrections based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can supplement and correct sentences by taking into account the sender's background. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) An application according to an embodiment of the present invention is a system that supplements internal email and chat messages by taking into account the sender's background. This system collects background information about the sender, analyzes the message the sender is about to send, and suggests appropriate supplements and corrections. For example, the system collects information such as the sender's age, work history, and hometown, and predicts how the message the sender is about to send will be received by the recipient. Next, based on the analysis results, the system suggests appropriate supplements and corrections to the sender. This facilitates internal communication and prevents unfortunate outcomes such as mental health problems and employee resignations. This application facilitates internal communication and prevents unfortunate outcomes such as mental health problems and employee resignations. For example, it can prevent a junior employee from receiving a message from their superior from being perceived as rude. It can also prevent a superior from receiving a message from their superior from being perceived as accusatory.

[0029] A communication supplementation system according to an embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects background information about the sender. The background information about the sender includes, but is not limited to, information about the sender's age, work history, and hometown. The collection unit may collect information about the sender's age, work history, and hometown from, for example, an internal personnel database. The collection unit may also analyze the sender's past communication history and select an optimal collection method. For example, the collection unit may prioritize collection of communication methods (email, chat, etc.) frequently used by the sender in the past. The analysis unit uses natural language processing technology to analyze a sentence the sender is about to send based on the information collected by the collection unit. Examples of natural language processing technology include, but are not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit analyzes a message the sender is about to send and predicts how the message will be received by the recipient. The suggestion unit suggests appropriate supplements or corrections to the sender based on the results of the analysis by the analysis unit. For example, if a message that a junior employee is about to send to a superior is perceived as rude, the suggestion unit notifies the sender of this and suggests appropriate supplements or corrections. Furthermore, if a message that a superior is about to send to a junior employee is perceived as accusatory, the suggestion unit notifies the sender of this and suggests appropriate supplements or corrections. Thus, the communication supplement system according to the embodiment can facilitate internal communication by considering the sender's background information and suggesting appropriate supplements or corrections.

[0030] The collection unit can collect information on the sender's age, work history, and hometown from an internal personnel database. The collection unit, for example, collects information on the sender's age, work history, and hometown from an internal personnel database. The information on age, work history, and hometown is collected, for example, using a database acquisition method or privacy protection means. In this way, by collecting information from the internal personnel database, it is possible to accurately understand the sender's background information. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit may input information acquired from the internal personnel database into a generation AI and have the generation AI analyze the information.

[0031] The analysis unit can analyze the text that the sender is about to send using natural language processing technology. The analysis unit analyzes the text that the sender is about to send using, for example, natural language processing technology. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit analyzes the message that the sender is about to send and predicts how the message will be received by the recipient. The analysis unit can also improve the accuracy of the analysis by referring to the sender's past message analysis results. For example, the analysis unit optimizes the analysis algorithm based on the sender's past message analysis results. In this way, the use of natural language processing technology improves the accuracy of text analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the text that the sender is about to send into a generation AI and have the generation AI analyze the text.

[0032] The suggestion unit can suggest appropriate supplements or corrections to the sender based on the analysis results. The suggestion unit, for example, suggests appropriate supplements or corrections to the sender based on the analysis results. Appropriate supplements or corrections include, for example, grammatical corrections and content corrections. For example, if a message a junior employee is about to send to their boss is perceived as rude, the suggestion unit notifies the sender and suggests appropriate supplements or corrections. Also, if a message a boss is about to send to a junior employee is perceived as accusatory, the suggestion unit notifies the sender and suggests appropriate supplements or corrections. In this way, misunderstandings can be prevented by suggesting appropriate supplements or corrections based on the analysis results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit inputs the analysis results into a generation AI and causes the generation AI to suggest appropriate supplements or corrections.

[0033] If a message that a junior employee is about to send to his / her boss is perceived as rude, the suggestion unit can notify the sender of this and suggest appropriate supplements or corrections. For example, if a message that a junior employee is about to send to his / her boss is perceived as rude, the suggestion unit can notify the sender of this and suggest appropriate supplements or corrections. Criteria for being perceived as rude include, for example, the use of honorific language and the tone of expression. For example, if the message that a junior employee is about to send to his / her boss does not use honorific language, the suggestion unit can notify the sender of this and suggest that the junior employee use honorific language. Furthermore, if the message that a junior employee is about to send to his / her boss contains too strong language, the suggestion unit can notify the sender of this and suggest that the junior employee tone down the language. This can prevent junior employees from sending rude messages to their boss. Some or all of the above-described processing by the suggestion unit can be performed, for example, using AI or without AI. For example, the proposal department can input a message that a young employee is about to send to their boss into the generation AI, and have the AI ​​determine whether the message would be perceived as rude and suggest appropriate additions or corrections.

[0034] If a message that a supervisor is about to send to a junior employee is perceived as blaming, the suggestion unit can notify the sender of this and suggest appropriate clarification or correction. For example, if a message that a supervisor is about to send to a junior employee is perceived as blaming, the suggestion unit can notify the sender of this and suggest appropriate clarification or correction. Criteria for being perceived as blaming include, for example, strong language and negative expressions. For example, if a message that a supervisor is about to send to a junior employee contains strong language, the suggestion unit can notify the sender of this and suggest that the language be softened. Furthermore, if a message that a supervisor is about to send to a junior employee contains negative language, the suggestion unit can notify the sender of this and suggest that the language be revised to be more positive. This can prevent a supervisor from sending a message that is perceived as blaming to a junior employee. Some or all of the above-described processing by the suggestion unit may be performed, for example, using AI or without using AI. For example, the proposal department can input a message that a supervisor is about to send to a junior employee into the generation AI, and have the AI ​​determine whether the message is perceived as blaming and suggest appropriate additions or corrections.

[0035] The collection unit can analyze the sender's past communication history and select the optimal collection method. The collection unit, for example, analyzes the sender's past communication history and selects the optimal collection method. The past communication history includes, for example, email history and chat logs. For example, the collection unit prioritizes collection of communication methods (email, chat, etc.) that the sender frequently used in the past. The collection unit can also analyze the content of the sender's past messages and collect information based on a specific tone or style. Furthermore, the collection unit can select the optimal collection method by referring to the language and expressions used by the sender in the past. In this way, the optimal collection method can be selected by analyzing the past communication history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the sender's past communication history into a generation AI and cause the generation AI to select the optimal collection method.

[0036] The collection unit may filter the background information based on the sender's current project and job title when collecting the background information. For example, the collection unit may filter the background information based on the sender's current project and job title. Filtering criteria may include, for example, the type of project and the job title. For example, the collection unit may prioritize collecting information related to the project the sender is currently in charge of. The collection unit may also filter necessary information based on the sender's job title and collect only appropriate information. Furthermore, the collection unit may collect highly relevant information according to the sender's current job content. Thus, highly relevant information can be collected by filtering information based on the sender's current project and job title. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input information about the sender's current project and job title into the generation AI and have the generation AI perform the filtering.

[0037] The collection unit can select an appropriate collection means depending on the input method of the sender when collecting background information. For example, when collecting background information, the collection unit selects an appropriate collection means depending on the input method of the sender (voice, text, image, etc.). Input methods include, for example, voice input, text input, and image input. For example, if the sender uses voice input, the collection unit can collect information by analyzing voice data. Also, if the sender uses text input, the collection unit can collect information by analyzing text data. Furthermore, if the sender uses images, the collection unit can collect information using image analysis technology. This allows information to be collected efficiently by selecting the optimal collection means depending on the sender's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the sender's input data to a generation AI and cause the generation AI to select the optimal collection means.

[0038] When collecting background information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the sender. For example, when collecting background information, the collection unit prioritizes collecting highly relevant information by taking into account the geographical location information of the sender. Geographical location information includes, for example, GPS data, IP address, etc. For example, if the sender is in a specific area, the collection unit prioritizes collecting information related to that area. The collection unit can also filter and collect highly relevant information based on the sender's current location. Furthermore, the collection unit can also collect optimal information by taking into account the geographical location information of the sender. In this way, highly relevant information can be prioritized by taking into account the geographical location information of the sender. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the sender to the generation AI and cause the generation AI to collect highly relevant information.

[0039] The collection unit can analyze the sender's social media activity and collect relevant information when collecting background information. For example, the collection unit can analyze the sender's social media activity and collect relevant information when collecting background information. Social media activity includes, for example, post content and follower information. For example, the collection unit can collect relevant background information based on information shared by the sender on social media. The collection unit can also analyze the sender's social media activity history and collect highly relevant information. Furthermore, the collection unit can also collect relevant information by referring to the sender's friendships on social media. In this way, highly relevant information can be collected by analyzing the sender's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the sender's social media activity data into a generation AI and cause the generation AI to collect related information.

[0040] The collection unit can customize the collection method by reflecting the sender's past feedback when collecting background information. For example, the collection unit customizes the collection method by reflecting the sender's past feedback when collecting background information. Past feedback includes, for example, survey results, user reviews, etc. For example, the collection unit optimizes the collection method based on feedback provided by the sender in the past. The collection unit can also adjust the type and amount of information to be collected by referring to the sender's past feedback. Furthermore, the collection unit can customize the collection method by reflecting the sender's feedback and collect more appropriate information. In this way, the collection method can be optimized by reflecting the sender's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the sender's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the message when analyzing the text. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the message when analyzing the text. Criteria for determining the importance of a message include, for example, the sender's job title and the content of the message. For example, the analysis unit performs a detailed analysis of a message with high importance and provides comprehensive information. The analysis unit can also perform a concise analysis of a message with low importance and provide the minimum necessary information. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis based on the importance of the message. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the message. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input message importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the message category when analyzing text. For example, the analysis unit applies different analysis algorithms depending on the message category when analyzing text. Message categories include, for example, business communications and private messages. For example, the analysis unit applies a business analysis algorithm to business-related messages. The analysis unit can also apply a casual analysis algorithm to casual messages. Furthermore, the analysis unit can select and apply an optimal analysis algorithm depending on the message category. This improves analysis accuracy by applying the optimal analysis algorithm depending on the message category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input message category data to the generation AI and cause the generation AI to select and apply the optimal analysis algorithm.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the sender's past message analysis results when analyzing a sentence. For example, the analysis unit can improve the accuracy of the analysis by referring to the sender's past message analysis results when analyzing a sentence. Past message analysis results include, for example, an analysis log and a database of analysis results. For example, the analysis unit optimizes the analysis algorithm based on the sender's past message analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the sender's past message analysis results. Furthermore, the analysis unit can perform a more accurate analysis by utilizing the sender's past message analysis results. In this way, the accuracy of the analysis is improved by referring to the sender's past message analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the sender's past message analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] The analysis unit can determine the analysis priority based on the time when the message was sent when analyzing the text. For example, the analysis unit determines the analysis priority based on the time when the message was sent when analyzing the text. The time when the message was sent includes, for example, a timestamp, a sending history, etc. For example, the analysis unit prioritizes analysis of messages that were sent at an important time. The analysis unit can also postpone analysis of messages that were sent at a later time. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the time when the message was sent. In this way, by determining the analysis priority based on the time when the message was sent, important messages can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input message sending time data to the generation AI and have the generation AI determine the analysis priority.

[0045] The analysis unit can adjust the analysis order based on the relevance of messages when analyzing text. The analysis unit, for example, adjusts the analysis order based on the relevance of messages when analyzing text. Criteria for determining the relevance of messages include, for example, similarity of content and the relationship between senders. For example, the analysis unit prioritizes analysis of highly relevant messages. The analysis unit can also postpone analysis of less relevant messages. Furthermore, the analysis unit can dynamically adjust the analysis order based on the relevance of messages. In this way, by adjusting the analysis order based on the relevance of messages, highly relevant messages can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input message relevance data to the generation AI and cause the generation AI to adjust the analysis order.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the sender's level of expertise when analyzing a text. For example, the analysis unit can adjust the use of technical terms in the analysis according to the sender's level of expertise when analyzing a text. Criteria for determining the level of expertise include, for example, qualifications and work experience. For example, if the sender has technical expertise, the analysis unit can use a lot of technical terms in the analysis. Furthermore, if the sender does not have technical expertise, the analysis unit can avoid technical terms in the analysis. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the sender's level of expertise. This allows for adjusting the use of technical terms according to the sender's level of expertise, thereby providing analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the sender's level of expertise data into the generation AI and have the generation AI adjust the use of technical terms.

[0047] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the message when proposing an addition or correction. For example, the suggestion unit adjusts the level of detail of the suggestion based on the importance of the message when proposing an addition or correction. Criteria for determining the importance of a message include, for example, the sender's job title and the content of the message. For example, the suggestion unit can suggest detailed additions or corrections for a message with high importance. The suggestion unit can also suggest concise additions or corrections for a message with low importance. Furthermore, the suggestion unit can dynamically adjust the level of detail of the suggestion based on the importance of the message. This enables efficient suggestions by adjusting the level of detail of the suggestion based on the importance of the message. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input message importance data to a generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0048] The suggestion unit can apply different suggestion algorithms depending on the message category when suggesting additions or corrections. For example, the suggestion unit applies different suggestion algorithms depending on the message category when suggesting additions or corrections. Message categories include, for example, business communications and private messages. For example, the suggestion unit applies a business suggestion algorithm to business-related messages. The suggestion unit can also apply a casual suggestion algorithm to casual messages. Furthermore, the suggestion unit can select and apply an optimal suggestion algorithm depending on the message category. This improves the accuracy of suggestions by applying the optimal suggestion algorithm depending on the message category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input message category data to a generation AI and cause the generation AI to select and apply an optimal suggestion algorithm.

[0049] The suggestion unit can improve the accuracy of the proposal by referring to the sender's past proposal results when proposing an addition or correction. For example, the suggestion unit improves the accuracy of the proposal by referring to the sender's past proposal results when proposing an addition or correction. Past proposal results include, for example, proposal history and feedback results. For example, the suggestion unit optimizes the proposal algorithm based on the sender's past proposal results. The suggestion unit can also improve the accuracy of the proposal by referring to the sender's past proposal results. Furthermore, the suggestion unit can make more accurate proposals by utilizing the sender's past proposal results. As a result, the accuracy of the proposal is improved by referring to the sender's past proposal results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the sender's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0050] The suggestion unit can determine the priority of suggestions based on the time when the message was sent when proposing an addition or correction. For example, the suggestion unit determines the priority of suggestions based on the time when the message was sent when proposing an addition or correction. The time when the message was sent includes, for example, a timestamp, a sending history, etc. For example, the suggestion unit can prioritize suggestions for additions or corrections for messages that are sent at an important time. The suggestion unit can also postpone suggestions for additions or corrections for messages that are sent at a later time. Furthermore, the suggestion unit can dynamically adjust the priority of suggestions based on the time when the message was sent. In this way, important messages can be prioritized by determining the priority of suggestions based on the time when the message was sent. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input message sending time data to the generation AI and cause the generation AI to determine the priority of suggestions.

[0051] The suggestion unit can adjust the order of suggestions based on the relevance of the messages when suggesting additions or corrections. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the messages when suggesting additions or corrections. Criteria for determining the relevance of messages include, for example, similarity of content and the relationship of the senders. For example, the suggestion unit can prioritize suggesting additions or corrections to highly relevant messages. The suggestion unit can also postpone suggesting additions or corrections to less relevant messages. Furthermore, the suggestion unit can dynamically adjust the order of suggestions based on the relevance of the messages. In this way, by adjusting the order of suggestions based on the relevance of the messages, highly relevant messages can be prioritized. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input message relevance data to a generation AI and cause the generation AI to adjust the order of suggestions.

[0052] The suggestion unit may adjust the use of technical terminology in the proposal according to the sender's level of expertise when proposing additions or corrections. For example, the suggestion unit may adjust the use of technical terminology in the proposal according to the sender's level of expertise when proposing additions or corrections. Criteria for determining the level of expertise include, for example, qualifications and work experience. For example, if the sender has technical expertise, the suggestion unit may suggest additions or corrections using a lot of technical terminology. Furthermore, if the sender does not have technical expertise, the suggestion unit may suggest additions or corrections while avoiding technical terminology. Furthermore, the suggestion unit may dynamically adjust the use of technical terminology in the proposal according to the sender's level of expertise. This allows for more understandable suggestions by adjusting the use of technical terminology according to the sender's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit may input the sender's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The analysis unit can also analyze how a sender's message affects the cultural background of the recipient. For example, if a particular expression could be misleading to a recipient from a different culture, the analysis unit can notify the sender and suggest appropriate modifications. The analysis unit can also adjust the tone and content of the message based on the recipient's cultural background. Furthermore, the analysis unit can facilitate international communication by suggesting appropriate expressions based on cultural background.

[0055] The suggestion unit can also make suggestions by taking into account how the sender's message will affect the recipient's current work situation. For example, if the recipient is in a busy situation, the suggestion unit can suggest a concise and to-the-point message. Alternatively, if the recipient is in a relaxed situation, the suggestion unit can suggest a message with detailed explanations. Furthermore, the suggestion unit can adjust the priority of the message depending on the recipient's work situation.

[0056] The suggestion unit can also make suggestions based on how the sender's message is perceived based on the recipient's past feedback. For example, if the recipient has provided negative feedback about a particular expression in the past, the suggestion unit can suggest avoiding that expression. Alternatively, if the recipient has previously preferred a particular expression, the suggestion unit can suggest using that expression more positively. Furthermore, the suggestion unit can optimize the content and tone of the message based on the recipient's past feedback.

[0057] The collection unit can also collect information taking into account how the sender's message will affect the recipient's current project progress. For example, if the recipient is approaching a project deadline, it can prioritize collecting important information. Alternatively, if the recipient is in the early stages of a project, it can collect detailed information. Furthermore, the collection unit can adjust how it collects information depending on the recipient's project progress.

[0058] The analysis unit can also analyze how the sender's message affects the recipient's job description. For example, if the recipient works in a technical position, it can suggest a message that includes technical details. Or, if the recipient works in a managerial position, it can suggest a concise message that gets straight to the point. Furthermore, the analysis unit can tailor the content and tone of the message depending on the recipient's job description, resulting in more effective communication.

[0059] The collection unit can also collect information taking into account how the sender's message will be received based on the recipient's past communication history. For example, if the recipient has had a negative reaction to a particular expression in the past, information can be collected to avoid that expression. Also, if the recipient has liked a particular expression in the past, information can be collected to encourage the recipient to actively use that expression. Furthermore, the collection unit can optimize the method of collecting information by referring to the recipient's past communication history.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection department collects background information about the sender. This information includes, for example, age, work history, and hometown. The collection department collects this information from the company's internal personnel database. The collection department also analyzes the sender's past communication history and selects the optimal collection method. For example, it prioritizes collection of communication methods (email, chat, etc.) that the sender has frequently used in the past. Step 2: The analyzer uses natural language processing technology to analyze the text the sender is about to send based on the information collected by the collector. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. For example, the analyzer analyzes the message the sender is about to send and predicts how the message will be received by the recipient. Step 3: The suggestion unit suggests appropriate clarifications or corrections to the sender based on the results of the analysis by the analysis unit. For example, if a message a junior employee is about to send to their boss is perceived as rude, the suggestion unit notifies the sender and suggests appropriate clarifications or corrections. Also, if a boss is about to send a message to a junior employee that is perceived as accusatory, the suggestion unit notifies the sender and suggests appropriate clarifications or corrections.

[0062] (Example 2) An application according to an embodiment of the present invention is a system that supplements internal email and chat messages by taking into account the sender's background. This system collects background information about the sender, analyzes the message the sender is about to send, and suggests appropriate supplements and corrections. For example, the system collects information such as the sender's age, work history, and hometown, and predicts how the message the sender is about to send will be received by the recipient. Next, based on the analysis results, the system suggests appropriate supplements and corrections to the sender. This facilitates internal communication and prevents unfortunate outcomes such as mental health problems and employee resignations. This application facilitates internal communication and prevents unfortunate outcomes such as mental health problems and employee resignations. For example, it can prevent a junior employee from receiving a message from their superior from being perceived as rude. It can also prevent a superior from receiving a message from their superior from being perceived as accusatory.

[0063] A communication supplementation system according to an embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects background information about the sender. The background information about the sender includes, but is not limited to, information about the sender's age, work history, and hometown. The collection unit may collect information about the sender's age, work history, and hometown from, for example, an internal personnel database. The collection unit may also analyze the sender's past communication history and select an optimal collection method. For example, the collection unit may prioritize collection of communication methods (email, chat, etc.) frequently used by the sender in the past. The analysis unit uses natural language processing technology to analyze a sentence the sender is about to send based on the information collected by the collection unit. Examples of natural language processing technology include, but are not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit analyzes a message the sender is about to send and predicts how the message will be received by the recipient. The suggestion unit suggests appropriate supplements or corrections to the sender based on the results of the analysis by the analysis unit. For example, if a message that a junior employee is about to send to a superior is perceived as rude, the suggestion unit notifies the sender of this and suggests appropriate supplements or corrections. Furthermore, if a message that a superior is about to send to a junior employee is perceived as accusatory, the suggestion unit notifies the sender of this and suggests appropriate supplements or corrections. Thus, the communication supplement system according to the embodiment can facilitate internal communication by considering the sender's background information and suggesting appropriate supplements or corrections.

[0064] The collection unit can collect information on the sender's age, work history, and hometown from an internal personnel database. The collection unit, for example, collects information on the sender's age, work history, and hometown from an internal personnel database. The information on age, work history, and hometown is collected, for example, using a database acquisition method or privacy protection means. In this way, by collecting information from the internal personnel database, it is possible to accurately understand the sender's background information. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit may input information acquired from the internal personnel database into a generation AI and have the generation AI analyze the information.

[0065] The analysis unit can analyze the text that the sender is about to send using natural language processing technology. The analysis unit analyzes the text that the sender is about to send using, for example, natural language processing technology. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit analyzes the message that the sender is about to send and predicts how the message will be received by the recipient. The analysis unit can also improve the accuracy of the analysis by referring to the sender's past message analysis results. For example, the analysis unit optimizes the analysis algorithm based on the sender's past message analysis results. In this way, the use of natural language processing technology improves the accuracy of text analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the text that the sender is about to send into a generation AI and have the generation AI analyze the text.

[0066] The suggestion unit can suggest appropriate supplements or corrections to the sender based on the analysis results. The suggestion unit, for example, suggests appropriate supplements or corrections to the sender based on the analysis results. Appropriate supplements or corrections include, for example, grammatical corrections and content corrections. For example, if a message a junior employee is about to send to their boss is perceived as rude, the suggestion unit notifies the sender and suggests appropriate supplements or corrections. Also, if a message a boss is about to send to a junior employee is perceived as accusatory, the suggestion unit notifies the sender and suggests appropriate supplements or corrections. In this way, misunderstandings can be prevented by suggesting appropriate supplements or corrections based on the analysis results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit inputs the analysis results into a generation AI and causes the generation AI to suggest appropriate supplements or corrections.

[0067] If a message that a junior employee is about to send to his / her boss is perceived as rude, the suggestion unit can notify the sender of this and suggest appropriate supplements or corrections. For example, if a message that a junior employee is about to send to his / her boss is perceived as rude, the suggestion unit can notify the sender of this and suggest appropriate supplements or corrections. Criteria for being perceived as rude include, for example, the use of honorific language and the tone of expression. For example, if the message that a junior employee is about to send to his / her boss does not use honorific language, the suggestion unit can notify the sender of this and suggest that the junior employee use honorific language. Furthermore, if the message that a junior employee is about to send to his / her boss contains too strong language, the suggestion unit can notify the sender of this and suggest that the junior employee tone down the language. This can prevent junior employees from sending rude messages to their boss. Some or all of the above-described processing by the suggestion unit can be performed, for example, using AI or without AI. For example, the proposal department can input a message that a young employee is about to send to their boss into the generation AI, and have the AI ​​determine whether the message would be perceived as rude and suggest appropriate additions or corrections.

[0068] If a message that a supervisor is about to send to a junior employee is perceived as blaming, the suggestion unit can notify the sender of this and suggest appropriate clarification or correction. For example, if a message that a supervisor is about to send to a junior employee is perceived as blaming, the suggestion unit can notify the sender of this and suggest appropriate clarification or correction. Criteria for being perceived as blaming include, for example, strong language and negative expressions. For example, if a message that a supervisor is about to send to a junior employee contains strong language, the suggestion unit can notify the sender of this and suggest that the language be softened. Furthermore, if a message that a supervisor is about to send to a junior employee contains negative language, the suggestion unit can notify the sender of this and suggest that the language be revised to be more positive. This can prevent a supervisor from sending a message that is perceived as blaming to a junior employee. Some or all of the above-described processing by the suggestion unit may be performed, for example, using AI or without using AI. For example, the proposal department can input a message that a supervisor is about to send to a junior employee into the generation AI, and have the AI ​​determine whether the message is perceived as blaming and suggest appropriate additions or corrections.

[0069] The collection unit can estimate the user's emotions and adjust the timing of background information collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of background information collection based on the estimated user emotions. Methods for estimating emotions include, for example, facial expression recognition and voice analysis. For example, if the user is feeling stressed, the collection unit delays the collection timing and collects information when the user is relaxed. Furthermore, if the user is relaxed, the collection unit can immediately collect background information, enabling a prompt response. Furthermore, if the user is in a hurry, the collection unit can optimize the collection timing and quickly collect the minimum amount of information necessary. This allows information to be collected at a more appropriate time by adjusting the collection timing according to the user's emotions. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's emotion data into a generation AI and cause the generation AI to estimate emotions and adjust the collection timing.

[0070] The collection unit can analyze the sender's past communication history and select the optimal collection method. The collection unit, for example, analyzes the sender's past communication history and selects the optimal collection method. The past communication history includes, for example, email history and chat logs. For example, the collection unit prioritizes collection of communication methods (email, chat, etc.) that the sender frequently used in the past. The collection unit can also analyze the content of the sender's past messages and collect information based on a specific tone or style. Furthermore, the collection unit can select the optimal collection method by referring to the language and expressions used by the sender in the past. In this way, the optimal collection method can be selected by analyzing the past communication history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the sender's past communication history into a generation AI and cause the generation AI to select the optimal collection method.

[0071] The collection unit may filter the background information based on the sender's current project and job title when collecting the background information. For example, the collection unit may filter the background information based on the sender's current project and job title. Filtering criteria may include, for example, the type of project and the job title. For example, the collection unit may prioritize collecting information related to the project the sender is currently in charge of. The collection unit may also filter necessary information based on the sender's job title and collect only appropriate information. Furthermore, the collection unit may collect highly relevant information according to the sender's current job content. Thus, highly relevant information can be collected by filtering information based on the sender's current project and job title. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input information about the sender's current project and job title into the generation AI and have the generation AI perform the filtering.

[0072] The collection unit can select an appropriate collection means depending on the input method of the sender when collecting background information. For example, when collecting background information, the collection unit selects an appropriate collection means depending on the input method of the sender (voice, text, image, etc.). Input methods include, for example, voice input, text input, and image input. For example, if the sender uses voice input, the collection unit can collect information by analyzing voice data. Also, if the sender uses text input, the collection unit can collect information by analyzing text data. Furthermore, if the sender uses images, the collection unit can collect information using image analysis technology. This allows information to be collected efficiently by selecting the optimal collection means depending on the sender's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the sender's input data to a generation AI and cause the generation AI to select the optimal collection means.

[0073] The collection unit can estimate the user's emotions and determine the priority of the background information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of the background information to be collected based on the estimated user emotions. Methods for estimating emotions include, for example, facial expression recognition and voice analysis. For example, when the user is stressed, the collection unit prioritizes collecting information of high importance. Furthermore, when the user is relaxed, the collection unit can also collect detailed information and provide comprehensive data. Furthermore, when the user is in a hurry, the collection unit can quickly collect the minimum necessary information. Thus, by determining the priority of information according to the user's emotions, important information can be collected preferentially. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's emotion data into a generation AI and cause the generation AI to estimate emotions and determine the priority of information.

[0074] When collecting background information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the sender. For example, when collecting background information, the collection unit prioritizes collecting highly relevant information by taking into account the geographical location information of the sender. Geographical location information includes, for example, GPS data, IP address, etc. For example, if the sender is in a specific area, the collection unit prioritizes collecting information related to that area. The collection unit can also filter and collect highly relevant information based on the sender's current location. Furthermore, the collection unit can also collect optimal information by taking into account the geographical location information of the sender. In this way, highly relevant information can be prioritized by taking into account the geographical location information of the sender. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the sender to the generation AI and cause the generation AI to collect highly relevant information.

[0075] The collection unit can analyze the sender's social media activity and collect relevant information when collecting background information. For example, the collection unit can analyze the sender's social media activity and collect relevant information when collecting background information. Social media activity includes, for example, post content and follower information. For example, the collection unit can collect relevant background information based on information shared by the sender on social media. The collection unit can also analyze the sender's social media activity history and collect highly relevant information. Furthermore, the collection unit can also collect relevant information by referring to the sender's friendships on social media. In this way, highly relevant information can be collected by analyzing the sender's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the sender's social media activity data into a generation AI and cause the generation AI to collect related information.

[0076] The collection unit can customize the collection method by reflecting the sender's past feedback when collecting background information. For example, the collection unit customizes the collection method by reflecting the sender's past feedback when collecting background information. Past feedback includes, for example, survey results, user reviews, etc. For example, the collection unit optimizes the collection method based on feedback provided by the sender in the past. The collection unit can also adjust the type and amount of information to be collected by referring to the sender's past feedback. Furthermore, the collection unit can customize the collection method by reflecting the sender's feedback and collect more appropriate information. In this way, the collection method can be optimized by reflecting the sender's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the sender's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0077] The analysis unit can estimate the user's emotions and adjust the expression method of the sentence analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the expression method of the sentence analysis based on the estimated user emotions. Methods for estimating emotions include, for example, facial expression recognition and voice analysis. For example, if the user is nervous, the analysis unit analyzes the sentence using a simple and clear expression method. Furthermore, if the user is relaxed, the analysis unit can analyze the sentence using an expression method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can analyze the sentence using a concise expression method that focuses on the main points. This allows for more appropriate analysis results by adjusting the expression method according to the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's emotion data into a generation AI and have the generation AI estimate the emotion and adjust the expression method.

[0078] The analysis unit can adjust the level of detail of the analysis based on the importance of the message when analyzing the text. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the message when analyzing the text. Criteria for determining the importance of a message include, for example, the sender's job title and the content of the message. For example, the analysis unit performs a detailed analysis of a message with high importance and provides comprehensive information. The analysis unit can also perform a concise analysis of a message with low importance and provide the minimum necessary information. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis based on the importance of the message. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the message. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input message importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0079] The analysis unit can apply different analysis algorithms depending on the message category when analyzing text. For example, the analysis unit applies different analysis algorithms depending on the message category when analyzing text. Message categories include, for example, business communications and private messages. For example, the analysis unit applies a business analysis algorithm to business-related messages. The analysis unit can also apply a casual analysis algorithm to casual messages. Furthermore, the analysis unit can select and apply an optimal analysis algorithm depending on the message category. This improves analysis accuracy by applying the optimal analysis algorithm depending on the message category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input message category data to the generation AI and cause the generation AI to select and apply the optimal analysis algorithm.

[0080] The analysis unit can improve the accuracy of the analysis by referring to the sender's past message analysis results when analyzing a sentence. For example, the analysis unit can improve the accuracy of the analysis by referring to the sender's past message analysis results when analyzing a sentence. Past message analysis results include, for example, an analysis log and a database of analysis results. For example, the analysis unit optimizes the analysis algorithm based on the sender's past message analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the sender's past message analysis results. Furthermore, the analysis unit can perform a more accurate analysis by utilizing the sender's past message analysis results. In this way, the accuracy of the analysis is improved by referring to the sender's past message analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the sender's past message analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. Methods for estimating emotions include facial expression recognition and voice analysis. For example, if the user is in a hurry, the analysis unit can perform a short, concise analysis. If the user is relaxed, the analysis unit can perform a detailed analysis and provide comprehensive information. Furthermore, if the user is excited, the analysis unit can perform an analysis that adds visually stimulating effects. This enables efficient analysis by adjusting the length of the analysis according to the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's emotion data into a generation AI and have the generation AI estimate the emotion and adjust the length of the analysis.

[0082] The analysis unit can determine the analysis priority based on the time when the message was sent when analyzing the text. For example, the analysis unit determines the analysis priority based on the time when the message was sent when analyzing the text. The time when the message was sent includes, for example, a timestamp, a sending history, etc. For example, the analysis unit prioritizes analysis of messages that were sent at an important time. The analysis unit can also postpone analysis of messages that were sent at a later time. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the time when the message was sent. In this way, by determining the analysis priority based on the time when the message was sent, important messages can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input message sending time data to the generation AI and have the generation AI determine the analysis priority.

[0083] The analysis unit can adjust the analysis order based on the relevance of messages when analyzing text. The analysis unit, for example, adjusts the analysis order based on the relevance of messages when analyzing text. Criteria for determining the relevance of messages include, for example, similarity of content and the relationship between senders. For example, the analysis unit prioritizes analysis of highly relevant messages. The analysis unit can also postpone analysis of less relevant messages. Furthermore, the analysis unit can dynamically adjust the analysis order based on the relevance of messages. In this way, by adjusting the analysis order based on the relevance of messages, highly relevant messages can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input message relevance data to the generation AI and cause the generation AI to adjust the analysis order.

[0084] The analysis unit can adjust the use of technical terms in the analysis according to the sender's level of expertise when analyzing a text. For example, the analysis unit can adjust the use of technical terms in the analysis according to the sender's level of expertise when analyzing a text. Criteria for determining the level of expertise include, for example, qualifications and work experience. For example, if the sender has technical expertise, the analysis unit can use a lot of technical terms in the analysis. Furthermore, if the sender does not have technical expertise, the analysis unit can avoid technical terms in the analysis. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the sender's level of expertise. This allows for adjusting the use of technical terms according to the sender's level of expertise, thereby providing analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the sender's level of expertise data into the generation AI and have the generation AI adjust the use of technical terms.

[0085] The suggestion unit can estimate the user's emotions and adjust the method of supplementation or correction based on the estimated user emotions. For example, the suggestion unit can estimate the user's emotions and adjust the method of supplementation or correction based on the estimated user emotions. Methods for estimating emotions include, for example, facial expression recognition and voice analysis. For example, the suggestion unit can suggest simple and clear supplementation or correction if the user is nervous. The suggestion unit can also suggest detailed supplementation or correction if the user is relaxed. Furthermore, the suggestion unit can suggest concise supplementation or correction that focuses on the main points if the user is in a hurry. This enables more appropriate suggestions by adjusting the method of supplementation or correction according to the user's emotions. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion and adjust the method of supplementation or correction.

[0086] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the message when proposing an addition or correction. For example, the suggestion unit adjusts the level of detail of the suggestion based on the importance of the message when proposing an addition or correction. Criteria for determining the importance of a message include, for example, the sender's job title and the content of the message. For example, the suggestion unit can suggest detailed additions or corrections for a message with high importance. The suggestion unit can also suggest concise additions or corrections for a message with low importance. Furthermore, the suggestion unit can dynamically adjust the level of detail of the suggestion based on the importance of the message. This enables efficient suggestions by adjusting the level of detail of the suggestion based on the importance of the message. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input message importance data to a generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0087] The suggestion unit can apply different suggestion algorithms depending on the message category when suggesting additions or corrections. For example, the suggestion unit applies different suggestion algorithms depending on the message category when suggesting additions or corrections. Message categories include, for example, business communications and private messages. For example, the suggestion unit applies a business suggestion algorithm to business-related messages. The suggestion unit can also apply a casual suggestion algorithm to casual messages. Furthermore, the suggestion unit can select and apply an optimal suggestion algorithm depending on the message category. This improves the accuracy of suggestions by applying the optimal suggestion algorithm depending on the message category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input message category data to a generation AI and cause the generation AI to select and apply an optimal suggestion algorithm.

[0088] The suggestion unit can improve the accuracy of the proposal by referring to the sender's past proposal results when proposing an addition or correction. For example, the suggestion unit improves the accuracy of the proposal by referring to the sender's past proposal results when proposing an addition or correction. Past proposal results include, for example, proposal history and feedback results. For example, the suggestion unit optimizes the proposal algorithm based on the sender's past proposal results. The suggestion unit can also improve the accuracy of the proposal by referring to the sender's past proposal results. Furthermore, the suggestion unit can make more accurate proposals by utilizing the sender's past proposal results. As a result, the accuracy of the proposal is improved by referring to the sender's past proposal results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the sender's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0089] The suggestion unit can estimate the user's emotions and adjust the length of the supplement or correction based on the estimated user's emotions. The suggestion unit can, for example, estimate the user's emotions and adjust the length of the supplement or correction based on the estimated user's emotions. Methods for estimating emotions include, for example, facial expression recognition and voice analysis. For example, if the user is in a hurry, the suggestion unit can suggest a short and to-the-point supplement or correction. The suggestion unit can also suggest a detailed supplement or correction if the user is relaxed. Furthermore, if the user is excited, the suggestion unit can suggest a supplement or correction with a visually stimulating effect. This enables efficient suggestions by adjusting the length of the supplement or correction according to the user's emotions. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's emotion data into a generation AI and cause the generation AI to estimate the emotion and adjust the length of the supplement or correction.

[0090] The suggestion unit can determine the priority of suggestions based on the time when the message was sent when proposing an addition or correction. For example, the suggestion unit determines the priority of suggestions based on the time when the message was sent when proposing an addition or correction. The time when the message was sent includes, for example, a timestamp, a sending history, etc. For example, the suggestion unit can prioritize suggestions for additions or corrections for messages that are sent at an important time. The suggestion unit can also postpone suggestions for additions or corrections for messages that are sent at a later time. Furthermore, the suggestion unit can dynamically adjust the priority of suggestions based on the time when the message was sent. In this way, important messages can be prioritized by determining the priority of suggestions based on the time when the message was sent. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input message sending time data to the generation AI and cause the generation AI to determine the priority of suggestions.

[0091] The suggestion unit can adjust the order of suggestions based on the relevance of the messages when suggesting additions or corrections. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the messages when suggesting additions or corrections. Criteria for determining the relevance of messages include, for example, similarity of content and the relationship of the senders. For example, the suggestion unit can prioritize suggesting additions or corrections to highly relevant messages. The suggestion unit can also postpone suggesting additions or corrections to less relevant messages. Furthermore, the suggestion unit can dynamically adjust the order of suggestions based on the relevance of the messages. In this way, by adjusting the order of suggestions based on the relevance of the messages, highly relevant messages can be prioritized. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input message relevance data to a generation AI and cause the generation AI to adjust the order of suggestions.

[0092] The suggestion unit may adjust the use of technical terminology in the proposal according to the sender's level of expertise when proposing additions or corrections. For example, the suggestion unit may adjust the use of technical terminology in the proposal according to the sender's level of expertise when proposing additions or corrections. Criteria for determining the level of expertise include, for example, qualifications and work experience. For example, if the sender has technical expertise, the suggestion unit may suggest additions or corrections using a lot of technical terminology. Furthermore, if the sender does not have technical expertise, the suggestion unit may suggest additions or corrections while avoiding technical terminology. Furthermore, the suggestion unit may dynamically adjust the use of technical terminology in the proposal according to the sender's level of expertise. This allows for more understandable suggestions by adjusting the use of technical terminology according to the sender's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit may input the sender's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects background information of the sender using the camera 42 or microphone 38B of the smart device 14 and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the sender's writing based on the collected information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests appropriate additions or corrections based on the analysis results. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the analysis unit and suggestion unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects background information of the sender using the camera 42 and microphone 238 of the smart glasses 214 and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the sender's writing based on the collected information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests appropriate additions or corrections based on the analysis results. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the analysis unit and suggestion unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects background information of the sender using the camera 42 or microphone 238 of the headset-type terminal 314 and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the sender's writing based on the collected information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests appropriate additions or corrections based on the analysis results. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the analysis unit and suggestion unit may be realized, for example, by the control unit 46A of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects background information of the sender using the camera 42 or microphone 238 of the robot 414 and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the sender's writing based on the collected information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests appropriate additions or corrections based on the analysis results. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the analysis unit and suggestion unit may be realized, for example, by the control unit 46A of the robot 414.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The analysis unit can also analyze how a sender's message affects the cultural background of the recipient. For example, if a particular expression could be misleading to a recipient from a different culture, the analysis unit can notify the sender and suggest appropriate modifications. The analysis unit can also adjust the tone and content of the message based on the recipient's cultural background. Furthermore, the analysis unit can facilitate international communication by suggesting appropriate expressions based on cultural background.

[0095] The suggestion unit can also make suggestions by taking into account how the sender's message will affect the recipient's current work situation. For example, if the recipient is in a busy situation, the suggestion unit can suggest a concise and to-the-point message. Alternatively, if the recipient is in a relaxed situation, the suggestion unit can suggest a message with detailed explanations. Furthermore, the suggestion unit can adjust the priority of the message depending on the recipient's work situation.

[0096] The collection unit can also analyze the emotional tone of the sender's past messages and adjust the tone of the current message. For example, if the past messages had a positive tone, it can suggest that the current message maintain a positive tone. Also, if the past messages had a negative tone, it can suggest that the current message be modified to have a more neutral or positive tone. Furthermore, the collection unit can optimize the sender's communication style by referring to the tone of the past messages.

[0097] The analysis unit can also analyze how the sender's message affects the recipient's current emotional state. For example, if the recipient is feeling stressed, the analysis unit can suggest a message with a softer tone. Alternatively, if the recipient is feeling relaxed, the analysis unit can suggest a message with more detailed information. Furthermore, the analysis unit can adjust the content and tone of the message depending on the recipient's emotional state, thereby achieving more effective communication.

[0098] The suggestion unit can also make suggestions based on how the sender's message is perceived based on the recipient's past feedback. For example, if the recipient has provided negative feedback about a particular expression in the past, the suggestion unit can suggest avoiding that expression. Alternatively, if the recipient has previously preferred a particular expression, the suggestion unit can suggest using that expression more positively. Furthermore, the suggestion unit can optimize the content and tone of the message based on the recipient's past feedback.

[0099] The collection unit can also collect information taking into account how the sender's message will affect the recipient's current project progress. For example, if the recipient is approaching a project deadline, it can prioritize collecting important information. Alternatively, if the recipient is in the early stages of a project, it can collect detailed information. Furthermore, the collection unit can adjust how it collects information depending on the recipient's project progress.

[0100] The analysis unit can also analyze how the sender's message affects the recipient's job description. For example, if the recipient works in a technical position, it can suggest a message that includes technical details. Or, if the recipient works in a managerial position, it can suggest a concise message that gets straight to the point. Furthermore, the analysis unit can tailor the content and tone of the message depending on the recipient's job description, resulting in more effective communication.

[0101] The suggestion unit may also make suggestions based on how the sender's message will be perceived based on the recipient's current emotional state. For example, if the recipient is stressed, the suggestion unit may suggest softening the tone of the message. Alternatively, if the recipient is relaxed, the suggestion unit may suggest a message that includes more detailed information. Furthermore, the suggestion unit may adjust the content and tone of the message depending on the recipient's emotional state, thereby achieving more effective communication.

[0102] The collection unit can also collect information taking into account how the sender's message will be received based on the recipient's past communication history. For example, if the recipient has had a negative reaction to a particular expression in the past, information can be collected to avoid that expression. Also, if the recipient has liked a particular expression in the past, information can be collected to encourage the recipient to actively use that expression. Furthermore, the collection unit can optimize the method of collecting information by referring to the recipient's past communication history.

[0103] The analysis unit can also analyze how the sender's message will be received based on the recipient's current emotional state. For example, if the recipient is stressed, the analysis unit can suggest a tone-down on the message. Alternatively, if the recipient is relaxed, the analysis unit can suggest a message with more detailed information. Furthermore, the analysis unit can adjust the content and tone of the message depending on the recipient's emotional state, thereby achieving more effective communication.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The collection department collects background information about the sender. This information includes, for example, age, work history, and hometown. The collection department collects this information from the company's internal personnel database. The collection department also analyzes the sender's past communication history and selects the optimal collection method. For example, it prioritizes collection of communication methods (email, chat, etc.) that the sender has frequently used in the past. Step 2: The analyzer uses natural language processing technology to analyze the text the sender is about to send based on the information collected by the collector. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. For example, the analyzer analyzes the message the sender is about to send and predicts how the message will be received by the recipient. Step 3: The suggestion unit suggests appropriate clarifications or corrections to the sender based on the results of the analysis by the analysis unit. For example, if a message a junior employee is about to send to their boss is perceived as rude, the suggestion unit notifies the sender and suggests appropriate clarifications or corrections. Also, if a boss is about to send a message to a junior employee that is perceived as accusatory, the suggestion unit notifies the sender and suggests appropriate clarifications or corrections.

[0106] 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.

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

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 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 collection unit for collecting background information of the sender; an analysis unit that analyzes a text that a sender is about to send based on the information collected by the collection unit; a suggestion unit that suggests appropriate supplements or corrections based on the results of the analysis by the analysis unit; Equipped with A system characterized by:

2. The collecting unit Collect information about the sender's age, work history, and hometown from the company's personnel database 2. The system of claim 1.

3. The analysis unit Use natural language processing technology to analyze the text the sender is trying to send 2. The system of claim 1.

4. The proposal unit Based on the analysis results, we suggest appropriate corrections or modifications to the sender.

2. The system of claim 1.

5. The proposal unit If a junior employee is about to send a message to their boss that is perceived as rude, notify the sender and suggest appropriate corrections or revisions.

2. The system of claim 1.

6. The proposal unit If a message a manager is about to send to a junior employee comes across as accusatory, notify the sender and suggest appropriate clarifications or corrections.

2. The system of claim 1.

7. The collecting unit The method estimates the user's emotions and adjusts the timing of collecting background information based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze the sender's past communication history and select the appropriate collection method 2. The system of claim 1.

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