system

Abstract

The system according to this embodiment aims to quickly and appropriately create greeting messages suitable for various situations. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, and a modification unit. The collection unit collects scenes, necessary information, and message direction. The generation unit generates a greeting message based on the information collected by the collection unit. The modification unit modifies the greeting message generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to quickly and appropriately create greeting sentences suitable for various scenes.

[0005] The system according to the embodiment aims to quickly and appropriately create greeting sentences suitable for various scenes.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, a generation unit, and a correction unit. The collection unit collects scenes, necessary information, and message directions. The generation unit generates a greeting sentence based on the information collected by the collection unit. The correction unit corrects the greeting sentence generated by the generation unit.

Effects of the Invention

[0007] The system according to this embodiment can quickly and appropriately create greeting messages suitable for various situations. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The greeting message generation system according to an embodiment of the present invention is a mechanism of an AI agent that generates greeting messages tailored to different scenes and company initiatives when giving greetings or speeches. This greeting message generation system can generate, modify, and check greeting messages in the following three patterns. First, in pre-generation, the AI ​​agent automatically generates a greeting message when the user inputs the scene, necessary information, message direction, etc. in advance. The generated greeting message is then modified by the user and the AI ​​agent as needed to create the optimal content. Next, in real-time generation, the AI ​​agent generates a greeting message in real time based on meeting audio and materials. Multiple patterns are generated, and the user can modify the message and other aspects as needed. Finally, in content checking, the AI ​​agent scores the content of the greeting message or speech that the user has thought of and provides feedback and modifications. By making modifications according to the user's wishes, higher quality greeting messages can be created. For example, the greeting message generation system automatically generates a greeting message when the user inputs the scene, necessary information, message direction, etc. in advance. The generated greeting message is then modified by the user and the AI ​​agent as needed to create the optimal content. Next, the greeting message generation system uses an AI agent to generate greeting messages in real time based on meeting audio and materials. It generates multiple patterns, allowing users to modify the message and other aspects as needed. Finally, when a user inputs their own greeting message or speech content into the system, the AI ​​agent scores the content and provides feedback and revisions. By making revisions according to the user's wishes, higher-quality greeting messages can be created. In this way, the greeting message generation system can generate and revise greeting messages tailored to different situations and company initiatives.

[0029] The greeting message generation system according to the embodiment comprises a collection unit, a generation unit, and a modification unit. The collection unit collects scenes, necessary information, and the direction of the message. For example, the collection unit can collect scenes, necessary information, and the direction of the message entered by the user. For example, the collection unit can collect specific types of scenes, such as business scenes and casual scenes, based on scenes entered by the user. For example, the collection unit can collect specific details, such as date and time, location, and participants, based on necessary information entered by the user. For example, the collection unit can collect specific details, such as expressing gratitude or making a request, based on the direction of the message entered by the user. The generation unit generates a greeting message based on the information collected by the collection unit. For example, the generation unit can generate specific formats and content, such as business greetings and casual greetings, based on the collected scenes, necessary information, and the direction of the message. For example, the generation unit can adjust the style and expression of the greeting message based on the collected information. For example, the generation unit can adjust the length and level of detail of the greeting message based on the collected information. The modification unit modifies the greeting message generated by the generation unit. The editing unit can, for example, generate greetings in multiple patterns, allowing the user to modify the message and other aspects. The editing unit can, for example, modify the grammar and content of the generated greetings. The editing unit can, for example, adjust the expression and level of detail of the generated greetings. As a result, the greeting generation system according to this embodiment can generate and modify greetings tailored to different scenes and company initiatives.

[0030] The data collection unit gathers the scene, necessary information, and message direction. Specifically, based on the scene entered by the user, it can collect specific types such as business scenes, casual scenes, formal events, and private gatherings. For example, business scenes include greetings at the beginning and end of meetings, and messages of gratitude to business partners. Casual scenes include greetings at gatherings with friends or casual parties. Based on the necessary information entered by the user, the data collection unit can collect specific details such as the date and time, location, participants, and purpose of the event. For example, it collects the date and time and location of a meeting, the titles and names of participants, and the purpose of the event (e.g., a new product announcement, a project progress report). Furthermore, based on the message direction entered by the user, the data collection unit can collect specific details such as expressing gratitude, making requests, apologizing, or offering congratulations. This allows the data collection unit to efficiently gather the detailed information necessary to generate greeting messages based on user input. The collected information is centrally managed within the system and made accessible to the generation and modification units. This provides the data collection unit with a foundation for generating customized greeting messages that meet the user's needs.

[0031] The generation unit generates greeting messages based on information collected by the collection unit. Specifically, based on the collected scene, necessary information, and message direction, it can generate specific formats and content such as business greetings, casual greetings, formal greetings, and private greetings. For example, a business greeting would use polite language and formal expressions to generate a message expressing gratitude to a client or superior. A casual greeting would use friendly language and casual expressions to generate a relaxed message to a friend or colleague. The generation unit can adjust the style and expression of the greeting message based on the collected information. For example, in formal situations, it would use honorifics and polite expressions frequently, while in casual situations, it would use friendly expressions and a style that incorporates humor. Furthermore, the generation unit can adjust the length and level of detail of the greeting message based on the collected information. For example, if a short greeting is required, it would generate a concise message summarizing the main points; if detailed explanations are needed, it would generate a longer greeting message including specific content and background information. This allows the generation unit to quickly and accurately generate a variety of greetings tailored to the user's needs. The generation unit utilizes AI technology to ensure natural phrasing and grammatical accuracy, providing high-quality greetings that satisfy users.

[0032] The editing unit modifies the greetings generated by the generation unit. Specifically, it generates greetings in multiple patterns, allowing users to modify the message and expression. For example, the grammar and content of the generated greetings can be corrected. Users can review the generated greetings and correct grammatical errors, add or delete content as needed. The editing unit can adjust the expression and level of detail of the generated greetings. For example, the tone of the greeting can be changed, or specific examples or anecdotes can be added. This allows the editing unit to customize the generated greetings to suit the user's needs and preferences. Furthermore, the editing unit can collect user feedback and use it as data to improve the generation unit's algorithm. For example, by analyzing user modifications and feedback and training the generation unit's AI model, it becomes possible to generate higher-quality greetings. This allows the editing unit to not only improve user satisfaction but also continuously improve the overall system performance. The editing unit provides an easy-to-use interface, allowing users to intuitively modify greetings. This enables the editing unit to help users quickly and effectively modify generated greetings and create optimal messages.

[0033] The collection unit can collect meeting audio and materials. For example, the collection unit can collect meeting audio using a recording device. For example, the collection unit can collect meeting audio using speech recognition technology. For example, the collection unit can collect meeting materials in the form of presentation materials or meeting minutes. As a result, by collecting meeting audio and materials, greeting messages can be generated in real time. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can collect meeting audio with a recording device, input the audio data into a generation AI, and have the generation AI generate text data from the audio data.

[0034] The generation unit can generate greeting messages based on collected meeting audio and materials. For example, the generation unit can convert collected meeting audio into text data using speech recognition technology and generate greeting messages based on that text data. For example, the generation unit can analyze collected meeting materials and generate greeting messages based on their content. For example, the generation unit can generate multiple patterns of greeting messages based on collected meeting audio and materials. This allows for the provision of appropriate greeting messages in real time by generating greeting messages based on meeting audio and materials. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input collected meeting audio data into a generation AI and have the generation AI perform the generation of greeting messages.

[0035] The editing unit generates greetings in multiple patterns, allowing the user to modify the message and other aspects. For example, the editing unit can generate greetings in multiple patterns with different styles and content. For example, the editing unit allows the user to modify the message of the generated greetings. For example, the editing unit allows the user to modify the grammar and content of the generated greetings. This allows the system to generate greetings in multiple patterns and provide the optimal greeting by allowing the user to modify the message and other aspects. Some or all of the above processing in the editing unit may be performed using a generation AI, for example, or without a generation AI. For example, the editing unit can input multiple patterns of the generated greetings into a generation AI and have the generation AI execute suggestions for modifications by the user.

[0036] The collection unit can collect greetings entered by the user. The collection unit can collect greetings entered by the user as text, for example. The collection unit can collect greetings entered by the user as voice input, for example. The collection unit can collect the format and content of greetings entered by the user, for example. By collecting greetings entered by the user, it is possible to generate greetings tailored to the user's preferences. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the greetings entered by the user into a generation AI and have the generation AI perform the collection of greetings.

[0037] The generation unit can assign scores to the collected greetings. The generation unit can assign scores based on, for example, the grammatical accuracy of the collected greetings. The generation unit can assign scores based on, for example, the appropriateness of the content of the collected greetings. The generation unit can assign scores based on, for example, the message content of the collected greetings. In this way, the quality of the greetings can be evaluated by assigning scores to the collected greetings. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the collected greetings into a generation AI and have the generation AI perform the scoring of the greetings.

[0038] The editing unit can modify the scored greeting. For example, the editing unit can modify the parts of the scored greeting that have low scores. For example, the editing unit can modify grammatical errors in the scored greeting. For example, the editing unit can modify inappropriate parts of the scored greeting. By modifying the scored greeting, higher quality greetings can be provided. Some or all of the above processing in the editing unit may be performed using, for example, a generation AI, or without a generation AI. For example, the editing unit can input the scored greeting into a generation AI and have the generation AI perform the modification of the greeting.

[0039] The data collection unit can analyze the user's past greeting history and select the optimal data collection method. For example, the data collection unit can analyze patterns in greetings the user has used in the past and collect information that can be used in similar situations. For example, the data collection unit can extract elements of greetings the user has successfully used in the past and collect information based on them. For example, the data collection unit can analyze the causes of greetings the user has failed at in the past and collect information to avoid them. In this way, the optimal information collection method can be selected by analyzing the user's past greeting history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past greeting history data into a generating AI and have the generating AI select the optimal data collection method.

[0040] The data collection unit can filter data based on the user's current projects and areas of interest during collection. For example, the data collection unit can prioritize collecting information related to the user's current projects. For example, the data collection unit can filter and collect highly relevant information based on the user's areas of interest. For example, the data collection unit can collect relevant information based on topics the user has shown interest in in the past. This allows for the collection of highly relevant information by filtering it based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's projects and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0041] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of information related to that region. For example, if the user is on the move, the data collection unit can collect highly relevant information based on the user's current location. For example, if the user is participating in a specific event, the data collection unit can collect information related to that event. In this way, by collecting information while considering the user's geographical location information, highly relevant information can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI determine the priority of the information.

[0042] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, the data collection unit can collect relevant information based on information shared by the user on social media. For example, the data collection unit can collect relevant information by analyzing the content of posts from accounts the user follows. For example, the data collection unit can collect relevant information by analyzing the activities of online communities the user participates in. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the information collection.

[0043] The generation unit can adjust the level of detail in a greeting based on the importance of the scene when generating a greeting. For example, in the case of an important meeting, the generation unit can generate a greeting that includes detailed information. For example, in the case of a casual event, the generation unit can generate a concise and to-the-point greeting. For example, in the case of a formal situation, the generation unit can generate a greeting that includes appropriate honorifics and politeness. By adjusting the level of detail in the greeting based on the importance of the scene, a more appropriate greeting can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input scene importance data into the generation AI and have the generation AI perform the adjustment of the level of detail in the greeting.

[0044] The generation unit can apply different generation algorithms depending on the scene category when generating greetings. For example, in the case of a wedding, the generation unit can apply an algorithm that generates greetings centered on words of congratulations. For example, in the case of a funeral, the generation unit can apply an algorithm that generates greetings expressing condolences. For example, in the case of a business meeting, the generation unit can apply an algorithm that generates greetings related to performance and goals. By applying different generation algorithms depending on the scene category, more appropriate greetings can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input scene category data into the generation AI and have the generation AI execute the application of the greeting generation algorithm.

[0045] The generation unit can determine the priority of greetings based on the timing of the scene's occurrence when generating greetings. For example, the generation unit can prioritize generating greetings for recent events. For example, the generation unit can prioritize generating greetings for regularly held meetings. For example, the generation unit can prioritize generating greetings related to specific seasons or holidays. By prioritizing greetings based on the timing of the scene's occurrence, more appropriate greetings can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input scene occurrence data into the generation AI and have the generation AI perform the determination of greeting priority.

[0046] The generation unit can adjust the order of greetings based on the relevance of the scenes when generating greetings. For example, the generation unit can generate a greeting that conveys the important points first. For example, the generation unit can generate a greeting that conveys gratitude first. For example, the generation unit can generate a greeting that conveys the conclusion first. By adjusting the order of greetings based on the relevance of the scenes, more appropriate greetings can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input scene relevance data into the generation AI and have the generation AI perform the adjustment of the order of greetings.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] The data collection unit can analyze a user's past speech history to understand their speaking trends and preferences. For example, it can extract specific phrases and expressions the user has used in the past and generate greeting messages based on them. It can also identify expressions and phrases the user has avoided in the past and exclude them from greeting messages. Furthermore, it can analyze patterns of successful past speeches the user has made and use that as a reference to generate greeting messages. This allows for the generation of more personalized greeting messages by leveraging the user's past speech history.

[0049] The generation unit can adjust the content of the greeting message based on the user's occupation and position. For example, if the user is a business executive, the generation unit can generate a greeting message that includes content related to the vision and strategy. If the user is a sales representative, the generation unit can generate a greeting message that emphasizes building relationships with customers and the appeal of the product. Furthermore, if the user is an engineer, the generation unit can generate a greeting message that includes technical details and project progress. This allows for the generation of appropriate greeting messages tailored to the user's occupation and position.

[0050] The editing function can reflect user feedback in real time and revise greeting messages. For example, if a user requests a change to a part of a greeting message, the editing function can immediately reflect that request and revise the message. Furthermore, if a user wishes to change the tone or style of the greeting message, the editing function can revise it accordingly. Additionally, if a user requests to adjust the length of the greeting message, the editing function can shorten or lengthen it accordingly. This allows for rapid incorporation of user feedback and provides more satisfying greeting messages.

[0051] The data collection unit can collect information based on the user's cultural background and language. For example, if the user belongs to a specific cultural sphere, the unit can collect expressions and phrases appropriate to that culture. Furthermore, if the user speaks multiple languages, the unit can collect information appropriate to each language. Additionally, if the user lives in a specific region, the unit can collect information based on the customs and trends of that region. This allows for the collection of appropriate information tailored to the user's cultural background and language, thereby improving the quality of greetings.

[0052] The following briefly describes the processing flow for example form 1.

[0053] Step 1: The data collection unit gathers the scene, necessary information, and message direction. For example, based on the scene entered by the user, it can collect specific types such as business scenes and casual scenes. It can also collect specific details such as the date, time, place, and participants based on the necessary information entered by the user. Furthermore, based on the message direction entered by the user, it can collect specific details such as expressing gratitude or making a request. Step 2: The generation unit generates a greeting message based on the information collected by the collection unit. For example, based on the collected scene, necessary information, and message direction, it can generate specific formats and content such as business greetings and casual greetings. It can also adjust the style, expression, length, and level of detail of the greeting message based on the collected information. Step 3: The editing unit modifies the greeting text generated by the generation unit. For example, it can generate greeting texts in multiple patterns, allowing the user to modify the message and other aspects. It can also adjust the grammar, content, expression, and level of detail of the generated greeting text.

[0054] (Example of form 2) The greeting message generation system according to an embodiment of the present invention is a mechanism of an AI agent that generates greeting messages tailored to different scenes and company initiatives when giving greetings or speeches. This greeting message generation system can generate, modify, and check greeting messages in the following three patterns. First, in pre-generation, the AI ​​agent automatically generates a greeting message when the user inputs the scene, necessary information, message direction, etc. in advance. The generated greeting message is then modified by the user and the AI ​​agent as needed to create the optimal content. Next, in real-time generation, the AI ​​agent generates a greeting message in real time based on meeting audio and materials. Multiple patterns are generated, and the user can modify the message and other aspects as needed. Finally, in content checking, the AI ​​agent scores the content of the greeting message or speech that the user has thought of and provides feedback and modifications. By making modifications according to the user's wishes, higher quality greeting messages can be created. For example, the greeting message generation system automatically generates a greeting message when the user inputs the scene, necessary information, message direction, etc. in advance. The generated greeting message is then modified by the user and the AI ​​agent as needed to create the optimal content. Next, the greeting message generation system uses an AI agent to generate greeting messages in real time based on meeting audio and materials. It generates multiple patterns, allowing users to modify the message and other aspects as needed. Finally, when a user inputs their own greeting message or speech content into the system, the AI ​​agent scores the content and provides feedback and revisions. By making revisions according to the user's wishes, higher-quality greeting messages can be created. In this way, the greeting message generation system can generate and revise greeting messages tailored to different situations and company initiatives.

[0055] The greeting message generation system according to the embodiment comprises a collection unit, a generation unit, and a modification unit. The collection unit collects scenes, necessary information, and the direction of the message. For example, the collection unit can collect scenes, necessary information, and the direction of the message entered by the user. For example, the collection unit can collect specific types of scenes, such as business scenes and casual scenes, based on scenes entered by the user. For example, the collection unit can collect specific details, such as date and time, location, and participants, based on necessary information entered by the user. For example, the collection unit can collect specific details, such as expressing gratitude or making a request, based on the direction of the message entered by the user. The generation unit generates a greeting message based on the information collected by the collection unit. For example, the generation unit can generate specific formats and content, such as business greetings and casual greetings, based on the collected scenes, necessary information, and the direction of the message. For example, the generation unit can adjust the style and expression of the greeting message based on the collected information. For example, the generation unit can adjust the length and level of detail of the greeting message based on the collected information. The modification unit modifies the greeting message generated by the generation unit. The editing unit can, for example, generate greetings in multiple patterns, allowing the user to modify the message and other aspects. The editing unit can, for example, modify the grammar and content of the generated greetings. The editing unit can, for example, adjust the expression and level of detail of the generated greetings. As a result, the greeting generation system according to this embodiment can generate and modify greetings tailored to different scenes and company initiatives.

[0056] The data collection unit gathers the scene, necessary information, and message direction. Specifically, based on the scene entered by the user, it can collect specific types such as business scenes, casual scenes, formal events, and private gatherings. For example, business scenes include greetings at the beginning and end of meetings, and messages of gratitude to business partners. Casual scenes include greetings at gatherings with friends or casual parties. Based on the necessary information entered by the user, the data collection unit can collect specific details such as the date and time, location, participants, and purpose of the event. For example, it collects the date and time and location of a meeting, the titles and names of participants, and the purpose of the event (e.g., a new product announcement, a project progress report). Furthermore, based on the message direction entered by the user, the data collection unit can collect specific details such as expressing gratitude, making requests, apologizing, or offering congratulations. This allows the data collection unit to efficiently gather the detailed information necessary to generate greeting messages based on user input. The collected information is centrally managed within the system and made accessible to the generation and modification units. This provides the data collection unit with a foundation for generating customized greeting messages that meet the user's needs.

[0057] The generation unit generates greeting messages based on information collected by the collection unit. Specifically, based on the collected scene, necessary information, and message direction, it can generate specific formats and content such as business greetings, casual greetings, formal greetings, and private greetings. For example, a business greeting would use polite language and formal expressions to generate a message expressing gratitude to a client or superior. A casual greeting would use friendly language and casual expressions to generate a relaxed message to a friend or colleague. The generation unit can adjust the style and expression of the greeting message based on the collected information. For example, in formal situations, it would use honorifics and polite expressions frequently, while in casual situations, it would use friendly expressions and a style that incorporates humor. Furthermore, the generation unit can adjust the length and level of detail of the greeting message based on the collected information. For example, if a short greeting is required, it would generate a concise message summarizing the main points; if detailed explanations are needed, it would generate a longer greeting message including specific content and background information. This allows the generation unit to quickly and accurately generate a variety of greetings tailored to the user's needs. The generation unit utilizes AI technology to ensure natural phrasing and grammatical accuracy, providing high-quality greetings that satisfy users.

[0058] The editing unit modifies the greetings generated by the generation unit. Specifically, it generates greetings in multiple patterns, allowing users to modify the message and expression. For example, the grammar and content of the generated greetings can be corrected. Users can review the generated greetings and correct grammatical errors, add or delete content as needed. The editing unit can adjust the expression and level of detail of the generated greetings. For example, the tone of the greeting can be changed, or specific examples or anecdotes can be added. This allows the editing unit to customize the generated greetings to suit the user's needs and preferences. Furthermore, the editing unit can collect user feedback and use it as data to improve the generation unit's algorithm. For example, by analyzing user modifications and feedback and training the generation unit's AI model, it becomes possible to generate higher-quality greetings. This allows the editing unit to not only improve user satisfaction but also continuously improve the overall system performance. The editing unit provides an easy-to-use interface, allowing users to intuitively modify greetings. This enables the editing unit to help users quickly and effectively modify generated greetings and create optimal messages.

[0059] The collection unit can collect meeting audio and materials. For example, the collection unit can collect meeting audio using a recording device. For example, the collection unit can collect meeting audio using speech recognition technology. For example, the collection unit can collect meeting materials in the form of presentation materials or meeting minutes. As a result, by collecting meeting audio and materials, greeting messages can be generated in real time. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can collect meeting audio with a recording device, input the audio data into a generation AI, and have the generation AI generate text data from the audio data.

[0060] The generation unit can generate greeting messages based on collected meeting audio and materials. For example, the generation unit can convert collected meeting audio into text data using speech recognition technology and generate greeting messages based on that text data. For example, the generation unit can analyze collected meeting materials and generate greeting messages based on their content. For example, the generation unit can generate multiple patterns of greeting messages based on collected meeting audio and materials. This allows for the provision of appropriate greeting messages in real time by generating greeting messages based on meeting audio and materials. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input collected meeting audio data into a generation AI and have the generation AI perform the generation of greeting messages.

[0061] The editing unit generates greetings in multiple patterns, allowing the user to modify the message and other aspects. For example, the editing unit can generate greetings in multiple patterns with different styles and content. For example, the editing unit allows the user to modify the message of the generated greetings. For example, the editing unit allows the user to modify the grammar and content of the generated greetings. This allows the system to generate greetings in multiple patterns and provide the optimal greeting by allowing the user to modify the message and other aspects. Some or all of the above processing in the editing unit may be performed using a generation AI, for example, or without a generation AI. For example, the editing unit can input multiple patterns of the generated greetings into a generation AI and have the generation AI execute suggestions for modifications by the user.

[0062] The collection unit can collect greetings entered by the user. The collection unit can collect greetings entered by the user as text, for example. The collection unit can collect greetings entered by the user as voice input, for example. The collection unit can collect the format and content of greetings entered by the user, for example. By collecting greetings entered by the user, it is possible to generate greetings tailored to the user's preferences. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the greetings entered by the user into a generation AI and have the generation AI perform the collection of greetings.

[0063] The generation unit can assign scores to the collected greetings. The generation unit can assign scores based on, for example, the grammatical accuracy of the collected greetings. The generation unit can assign scores based on, for example, the appropriateness of the content of the collected greetings. The generation unit can assign scores based on, for example, the message content of the collected greetings. In this way, the quality of the greetings can be evaluated by assigning scores to the collected greetings. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the collected greetings into a generation AI and have the generation AI perform the scoring of the greetings.

[0064] The editing unit can modify the scored greeting. For example, the editing unit can modify the parts of the scored greeting that have low scores. For example, the editing unit can modify grammatical errors in the scored greeting. For example, the editing unit can modify inappropriate parts of the scored greeting. By modifying the scored greeting, higher quality greetings can be provided. Some or all of the above processing in the editing unit may be performed using, for example, a generation AI, or without a generation AI. For example, the editing unit can input the scored greeting into a generation AI and have the generation AI perform the modification of the greeting.

[0065] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is nervous, the data collection unit can prioritize collecting information to help them relax. For example, if the user is confident, the data collection unit can collect more detailed information to improve the quality of the greeting. For example, if the user is anxious, the data collection unit can prioritize collecting concise and to-the-point information. This allows for the collection of more appropriate information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0066] The data collection unit can analyze the user's past greeting history and select the optimal data collection method. For example, the data collection unit can analyze patterns in greetings the user has used in the past and collect information that can be used in similar situations. For example, the data collection unit can extract elements of greetings the user has successfully used in the past and collect information based on them. For example, the data collection unit can analyze the causes of greetings the user has failed at in the past and collect information to avoid them. In this way, the optimal information collection method can be selected by analyzing the user's past greeting history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past greeting history data into a generating AI and have the generating AI select the optimal data collection method.

[0067] The data collection unit can filter data based on the user's current projects and areas of interest during collection. For example, the data collection unit can prioritize collecting information related to the user's current projects. For example, the data collection unit can filter and collect highly relevant information based on the user's areas of interest. For example, the data collection unit can collect relevant information based on topics the user has shown interest in in the past. This allows for the collection of highly relevant information by filtering it based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's projects and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0068] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit can delay the timing of information collection to collect detailed information. For example, if the user is in a hurry, the data collection unit can quickly collect the necessary information. For example, if the user is focused, the data collection unit can adjust the timing of information collection to provide information efficiently. This allows information to be collected at a more appropriate time by adjusting the timing of information collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of information collection.

[0069] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of information related to that region. For example, if the user is on the move, the data collection unit can collect highly relevant information based on the user's current location. For example, if the user is participating in a specific event, the data collection unit can collect information related to that event. In this way, by collecting information while considering the user's geographical location information, highly relevant information can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI determine the priority of the information.

[0070] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, the data collection unit can collect relevant information based on information shared by the user on social media. For example, the data collection unit can collect relevant information by analyzing the content of posts from accounts the user follows. For example, the data collection unit can collect relevant information by analyzing the activities of online communities the user participates in. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the information collection.

[0071] The generation unit can estimate the user's emotions and adjust the expression of the greeting based on the estimated emotions. For example, if the user is nervous, the generation unit can use a simple and calm expression. For example, if the user is confident, the generation unit can use a strong and persuasive expression. For example, if the user is relaxed, the generation unit can use a friendly and gentle expression. In this way, by adjusting the expression of the greeting based on the user's emotions, a more appropriate greeting can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the expression of the greeting.

[0072] The generation unit can adjust the level of detail in a greeting based on the importance of the scene when generating a greeting. For example, in the case of an important meeting, the generation unit can generate a greeting that includes detailed information. For example, in the case of a casual event, the generation unit can generate a concise and to-the-point greeting. For example, in the case of a formal situation, the generation unit can generate a greeting that includes appropriate honorifics and politeness. By adjusting the level of detail in the greeting based on the importance of the scene, a more appropriate greeting can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input scene importance data into the generation AI and have the generation AI perform the adjustment of the level of detail in the greeting.

[0073] The generation unit can apply different generation algorithms depending on the scene category when generating greetings. For example, in the case of a wedding, the generation unit can apply an algorithm that generates greetings centered on words of congratulations. For example, in the case of a funeral, the generation unit can apply an algorithm that generates greetings expressing condolences. For example, in the case of a business meeting, the generation unit can apply an algorithm that generates greetings related to performance and goals. By applying different generation algorithms depending on the scene category, more appropriate greetings can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input scene category data into the generation AI and have the generation AI execute the application of the greeting generation algorithm.

[0074] The generation unit can estimate the user's emotions and adjust the length of the greeting based on the estimated emotions. For example, if the user is nervous, the generation unit can generate a short, to-the-point greeting. For example, if the user is relaxed, the generation unit can generate a longer greeting that includes detailed explanations. For example, if the user is in a hurry, the generation unit can generate a short greeting that includes what needs to be conveyed quickly. By adjusting the length of the greeting based on the user's emotions, a more appropriate greeting can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the length of the greeting.

[0075] The generation unit can determine the priority of greetings based on the timing of the scene's occurrence when generating greetings. For example, the generation unit can prioritize generating greetings for recent events. For example, the generation unit can prioritize generating greetings for regularly held meetings. For example, the generation unit can prioritize generating greetings related to specific seasons or holidays. By prioritizing greetings based on the timing of the scene's occurrence, more appropriate greetings can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input scene occurrence data into the generation AI and have the generation AI perform the determination of greeting priority.

[0076] The generation unit can adjust the order of greetings based on the relevance of the scenes when generating greetings. For example, the generation unit can generate a greeting that conveys the important points first. For example, the generation unit can generate a greeting that conveys gratitude first. For example, the generation unit can generate a greeting that conveys the conclusion first. By adjusting the order of greetings based on the relevance of the scenes, more appropriate greetings can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input scene relevance data into the generation AI and have the generation AI perform the adjustment of the order of greetings.

[0077] The correction unit can estimate the user's emotions and adjust the correction method based on the estimated emotions. For example, if the user is nervous, the correction unit can suggest a simple and easy-to-understand correction method. For example, if the user is confident, the correction unit can suggest a detailed correction method. For example, if the user is relaxed, the correction unit can suggest a flexible correction method. This allows for more appropriate corrections by adjusting the correction method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the correction unit may be performed using a generative AI, or not. For example, the correction unit can input user emotion data into a generative AI and have the generative AI adjust the correction method.

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

[0079] The data collection unit can analyze a user's past speech history to understand their speaking trends and preferences. For example, it can extract specific phrases and expressions the user has used in the past and generate greeting messages based on them. It can also identify expressions and phrases the user has avoided in the past and exclude them from greeting messages. Furthermore, it can analyze patterns of successful past speeches the user has made and use that as a reference to generate greeting messages. This allows for the generation of more personalized greeting messages by leveraging the user's past speech history.

[0080] The generation unit can adjust the content of the greeting message based on the user's occupation and position. For example, if the user is a business executive, the generation unit can generate a greeting message that includes content related to the vision and strategy. If the user is a sales representative, the generation unit can generate a greeting message that emphasizes building relationships with customers and the appeal of the product. Furthermore, if the user is an engineer, the generation unit can generate a greeting message that includes technical details and project progress. This allows for the generation of appropriate greeting messages tailored to the user's occupation and position.

[0081] The editing function can reflect user feedback in real time and revise greeting messages. For example, if a user requests a change to a part of a greeting message, the editing function can immediately reflect that request and revise the message. Furthermore, if a user wishes to change the tone or style of the greeting message, the editing function can revise it accordingly. Additionally, if a user requests to adjust the length of the greeting message, the editing function can shorten or lengthen it accordingly. This allows for rapid incorporation of user feedback and provides more satisfying greeting messages.

[0082] The data collection unit can collect information based on the user's cultural background and language. For example, if the user belongs to a specific cultural sphere, the unit can collect expressions and phrases appropriate to that culture. Furthermore, if the user speaks multiple languages, the unit can collect information appropriate to each language. Additionally, if the user lives in a specific region, the unit can collect information based on the customs and trends of that region. This allows for the collection of appropriate information tailored to the user's cultural background and language, thereby improving the quality of greetings.

[0083] The generation unit can analyze the user's past emotional history and adjust the content of the greeting message accordingly. For example, if the user has previously given an emotional speech, the generation unit can generate a greeting message that reflects that emotion. Similarly, if the user has previously given a calm speech, the generation unit can generate a greeting message that reflects that calmness. Furthermore, if the user has previously given a speech expressing a specific emotion, the generation unit can generate a greeting message that emphasizes that emotion. This allows for the generation of more emotionally appropriate greeting messages by leveraging the user's past emotional history.

[0084] The editing function can estimate the user's emotions and determine the priority of corrections based on those emotions. For example, if the editing function is stressed, it can prioritize important corrections to help the user feel at ease. If the editing function is confident, it can postpone minor corrections and focus on the overall flow. Furthermore, if the editing function is anxious, it can quickly make corrections to help the user calm down. In this way, by adjusting the priority of corrections based on the user's emotions, more effective corrections can be made.

[0085] The data collection unit can estimate the user's emotions and adjust the level of detail of the information it collects based on those emotions. For example, if the user is relaxed, the unit can collect detailed information to improve the quality of the greeting. If the user is nervous, the unit can collect concise and to-the-point information. Furthermore, if the user is anxious, the unit can prioritize collecting information that can be retrieved quickly. By adjusting the level of detail of information based on the user's emotions, more appropriate information can be collected.

[0086] The generation unit can estimate the user's emotions and adjust the tone of the greeting based on those emotions. For example, if the user is nervous, the generation unit can generate a calm-toned greeting. If the user is confident, the generation unit can generate a strong-toned greeting. Furthermore, if the user is relaxed, the generation unit can generate a friendly-toned greeting. By adjusting the tone of the greeting based on the user's emotions, a more appropriate greeting can be generated.

[0087] The correction unit can estimate the user's emotions and adjust the timing of corrections based on those emotions. For example, if the user is relaxed, the correction unit can delay the timing of corrections and perform detailed corrections. If the user is in a hurry, the correction unit can perform corrections quickly and provide the necessary information. Furthermore, if the user is focused, the correction unit can adjust the timing of corrections to perform them efficiently. In this way, by adjusting the timing of corrections based on the user's emotions, more appropriate corrections can be made.

[0088] The generation unit can estimate the user's emotions and adjust the content of the greeting based on those emotions. For example, if the user is nervous, the generation unit can generate a simple and easy-to-understand greeting. If the user is confident, the generation unit can generate a detailed and persuasive greeting. Furthermore, if the user is relaxed, the generation unit can generate a friendly and gentle greeting. In this way, by adjusting the content of the greeting based on the user's emotions, a more appropriate greeting can be generated.

[0089] The following briefly describes the processing flow for example form 2.

[0090] Step 1: The data collection unit gathers the scene, necessary information, and message direction. For example, based on the scene entered by the user, it can collect specific types such as business scenes and casual scenes. It can also collect specific details such as the date, time, place, and participants based on the necessary information entered by the user. Furthermore, based on the message direction entered by the user, it can collect specific details such as expressing gratitude or making a request. Step 2: The generation unit generates a greeting message based on the information collected by the collection unit. For example, based on the collected scene, necessary information, and message direction, it can generate specific formats and content such as business greetings and casual greetings. It can also adjust the style, expression, length, and level of detail of the greeting message based on the collected information. Step 3: The editing unit modifies the greeting text generated by the generation unit. For example, it can generate greeting texts in multiple patterns, allowing the user to modify the message and other aspects. It can also adjust the grammar, content, expression, and level of detail of the generated greeting text.

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

[0092] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

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

[0094] Each of the multiple elements, including the collection unit, generation unit, and modification unit described above, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects the scene, necessary information, and message direction entered by the user. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a greeting message based on the collected information. The modification unit is implemented by the control unit 46A of the smart device 14 and modifies the generated greeting message. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0095] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0096] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0098] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0099] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0101] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0102] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0103] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0104] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0105] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0106] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0109] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0110] Each of the multiple elements, including the collection unit, generation unit, and modification unit described above, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects the scene, necessary information, and message direction entered by the user. The generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates a greeting message based on the collected information. The modification unit is implemented by the control unit 46A of the smart glasses 214 and modifies the generated greeting message. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0111] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0112] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0113] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0114] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0117] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0118] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0119] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0120] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0121] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0123] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0125] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the collection unit, generation unit, and modification unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects the scene, necessary information, and message direction entered by the user. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a greeting message based on the collected information. The modification unit is implemented by the control unit 46A of the headset terminal 314 and modifies the generated greeting message. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0128] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0135] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements, including the collection unit, generation unit, and modification unit described above, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects the scene, necessary information, and message direction entered by the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a greeting message based on the collected information. The modification unit is implemented by the control unit 46A of the robot 414 and modifies the generated greeting message. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0144] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0145] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0146] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0147] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0148] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0149] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0150] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0151] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0152] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0153] 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.

[0154] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0155] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0156] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0157] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0158] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0159] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0160] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0161] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0162] (Note 1) A collection unit that gathers scenes, necessary information, and message direction, A generation unit generates a greeting message based on the information collected by the collection unit, The system includes a correction unit for correcting the greeting text generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect audio and document recordings from meetings. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate greeting messages based on collected meeting audio and materials. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned modification section is, The system generates greeting messages in multiple variations, allowing users to modify the message and other aspects. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect greeting messages entered by users. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is The collected greetings will be scored. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned modification section is, Revise the scored greeting message. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past greeting history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the user's social media activity is analyzed to gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is The system estimates the user's emotions and adjusts the wording of the greeting based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating a greeting message, adjust the level of detail in the greeting message based on the importance of the scene. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating greeting messages, different generation algorithms are applied depending on the scene category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is The system estimates the user's emotions and adjusts the length of the greeting message based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating greeting messages, the priority of the greeting messages is determined based on when the scene occurred. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating greeting messages, adjust the order of the messages based on the relevance of the scene. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned modification section is, It estimates the user's emotions and adjusts the correction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0163] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that gathers scenes, necessary information, and message direction, A generation unit generates a greeting message based on the information collected by the collection unit, The system includes a correction unit for correcting the greeting text generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect audio and document recordings from meetings. The system according to feature 1.

3. The generating unit is Generate greeting messages based on collected meeting audio and materials. The system according to feature 1.

4. The aforementioned modification section is, The system generates greeting messages in multiple variations, allowing users to modify the message and other aspects. The system according to feature 1.

5. The aforementioned collection unit is Collect greeting messages entered by users. The system according to feature 1.

6. The generating unit is The collected greetings will be scored. The system according to feature 1.

7. The aforementioned modification section is, Revise the scored greeting message. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze the user's past greeting history and select the optimal collection method. The system according to feature 1.

10. The aforementioned collection unit is During data collection, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

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