system

Abstract

The system according to this embodiment aims to suggest action options based on the flow of conversation. [Solution] The system according to the embodiment comprises a registration unit, an acquisition unit, and a suggestion unit. The registration unit registers the user's voice. The acquisition unit acquires the conversation. The suggestion unit suggests action options based on the flow of the conversation acquired by the acquisition unit.

Description

Technical Field

[0006] , , ,

[0005] ,

[0007] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that multiple options may not appear during conversation response, making decision-making difficult.

[0005] The system according to the embodiment aims to propose action options based on the conversation flow. <00​​​​​​​​​​​ The system according to this embodiment can suggest action options based on the flow of conversation. [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 novel game-type AI agent system according to an embodiment of the present invention is a system that facilitates decision-making by presenting multiple options during conversations. The aim of this system is to enable easy decision-making by presenting options on a display in a novel game style. For example, the user registers their voice. Next, the AI ​​agent constantly listens to the conversation and suggests action options on the screen based on the flow of the conversation. For example, it can be used in a wide range of scenarios, such as family conversations, customer negotiations, call center operations, and presentations. The options are presented like in a novel game, and the user can choose the best option from among them. For example, in a conversation with one's wife, in response to the statement, "It looks like the weather will be nice next Sunday," options such as "Wait and see," "Do nothing," "Increase favorability," and "Decrease favorability" are presented. In a call center, in response to the statement, "The power won't turn on, isn't it broken?", options such as "Lower the temperature" and "Ask where to buy it" are presented. This mechanism allows the user to choose a good option without choosing an option that is disadvantageous to them. Furthermore, by projecting it onto a PC screen or smart glasses screen, decision-making can be done quickly. Even in situations where nervousness prevents you from thinking of other options, this tool allows you to quickly learn about alternative choices. This enables the novel-game-style AI agent system to facilitate user decision-making.

[0029] The novel game-type AI agent system according to this embodiment comprises a registration unit, an acquisition unit, and a suggestion unit. The registration unit registers the user's voice. The user's voice includes, but is not limited to, voice data, text data, and feedback. For example, the registration unit allows the user to record their voice and save it as voice data. The registration unit also allows the user to input their voice as text data. Furthermore, the registration unit can receive feedback from the user and reflect it in the voice registration. The acquisition unit acquires conversations. Conversations include, but are not limited to, voice conversations, text chats, and video conversations. For example, the acquisition unit acquires the voice of a user when they engage in a voice conversation in real time. The acquisition unit can also acquire the content of text chats. Furthermore, the acquisition unit can acquire the audio and video of video conversations. The suggestion unit proposes action options based on the flow of conversation acquired by the acquisition unit. Action options include, but are not limited to, the type of action proposed and the number of options. For example, the suggestion unit presents the user with multiple options based on the content of the conversation. Furthermore, the suggestion function can propose the most suitable option to the user from among the available choices. In addition, the suggestion function can adjust the presentation method of the choices, displaying them in a format that is easy for the user to understand. This allows the novel-game type AI agent system to facilitate decision-making by registering the user's voice, capturing conversations, and suggesting action options.

[0030] The registration unit registers user voices. User voices include, but are not limited to, audio data, text data, and feedback. For example, the registration unit can receive voice recordings from users and save them as audio data. The registration unit can also receive voice input from users as text data. Furthermore, the registration unit can receive feedback from users and incorporate it into voice registration. Specifically, users can use a dedicated application to record their voices and upload the audio data to a cloud server. The audio data is pre-processed for noise reduction and sound quality improvement before being stored in the registration unit. If a user inputs voice data as text, speech recognition technology can be used to convert the speech to text and store it in the registration unit. Furthermore, user feedback can include evaluations of the quality of the audio data and the accuracy of the text data. This allows the registration unit to register user voices in various formats and respond flexibly to user needs. In addition, the registration unit manages the registration history of user voices and can understand user preferences and trends by referring to past data. This allows the registration unit to efficiently and effectively register user feedback, thereby improving the overall performance of the system.

[0031] The acquisition unit acquires conversations. These conversations include, but are not limited to, voice conversations, text chats, and video conversations. For example, the acquisition unit acquires audio in real time when a user engages in a voice conversation. It can also acquire the content of text chats. Furthermore, it can acquire audio and video from video conversations. Specifically, when a user engages in a voice conversation, the acquisition unit acquires audio data through a microphone and analyzes it in real time. The audio data is pre-processed for noise reduction and sound quality improvement before being sent to the analysis unit. Additionally, when acquiring text chat content, the acquisition unit can collect text data entered by the user in real time and send it to the analysis unit. Furthermore, when acquiring audio and video from video conversations, the acquisition unit collects data through a camera and microphone and analyzes it in real time. This allows the acquisition unit to acquire diverse formats of conversation data in real time, improving the overall system performance. Furthermore, the acquisition unit centrally manages the acquired conversation data and can collaborate with other systems and departments as needed. For example, the acquired data can be stored on a cloud server and made accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data acquisition, flexible responses to specific situations and conditions become possible. This allows the acquisition unit to efficiently and effectively acquire conversational data, improving the overall system performance.

[0032] The suggestion unit proposes action options based on the conversation flow captured by the acquisition unit. These action options include, but are not limited to, the type of action proposed and the number of options. For example, the suggestion unit presents multiple options to the user based on the conversation content. It can also suggest the most suitable option from among these choices. Furthermore, the suggestion unit can adjust the presentation method of the options to make them easier for the user to understand. Specifically, the suggestion unit analyzes the conversation data transmitted from the acquisition unit to understand the conversation flow and context. Using AI, it analyzes the conversation content and the user's intent to generate appropriate action options. For example, if a user is seeking advice on a specific problem, the suggestion unit can present multiple options for solving the problem. The suggestion unit can also consider the user's past selection history and preferences when suggesting the most suitable option from among the choices. Furthermore, the suggestion unit can adjust the presentation method of the options to make them easier for the user to understand. For example, it can graphically display options using a visual interface or explain options using voice guidance. This allows the suggestion unit to quickly propose appropriate action options to the user, facilitating decision-making. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. This allows the proposal department to provide users with high-quality proposals and improve the overall system performance.

[0033] The proposal section can present choices in a way similar to a visual novel game. For example, the proposal section can present choices using a visual interface. For instance, it can display choices on the screen, making them easier for the user to understand visually. Furthermore, the proposal section can adjust the display format of the choices to make them easier for the user to select. For example, it can display choices as buttons, allowing the user to select them with a single click. This presentation of choices, similar to a visual novel game, makes them easier for the user to understand visually.

[0034] The suggestion function can be used in a wide range of situations, including family conversations, customer negotiations, call center interactions, and presentations. For example, in conversations within the home, the suggestion function can present users with appropriate options. In business settings, it can also present users with appropriate options during customer negotiations and presentations. Furthermore, in call center interactions, the suggestion function can present operators with options to help them respond appropriately to customers. As a result, the suggestion function, usable in a wide range of situations, can support decision-making in various circumstances.

[0035] The suggestion function helps users choose good options and avoid unfavorable ones. For example, the suggestion function proposes the best option for the user from among the available choices. For instance, it prioritizes presenting options with low risk and a high probability of success. It can also prioritize presenting options with high benefits. This allows users to choose better options, thus improving the quality of decision-making.

[0036] The proposal unit can be projected onto a PC screen or smart glasses screen. For example, the proposal unit can display options on a PC screen, allowing the user to visually confirm their choices. Alternatively, the proposal unit can project options onto a smart glasses screen, enabling the user to visually confirm their choices. This allows users to visually confirm their choices by projecting them onto a PC screen or smart glasses screen.

[0037] The proposal department can quickly provide alternative options even in situations where nervousness might prevent users from thinking of other choices. For example, it can present appropriate options to users who are nervous during a presentation. It can also present appropriate options to users who are nervous during an interview. Furthermore, it can present appropriate options to users who are nervous during negotiations. This allows users to know about other options even in stressful situations, making decision-making easier.

[0038] The registration unit can analyze a user's past voice registration history and select the optimal registration method. For example, the registration unit will prioritize suggesting registration methods (voice, text, etc.) that the user has frequently used in the past. Furthermore, the registration unit can suggest the most suitable registration method for a specific time period based on the user's past registration history. In addition, the registration unit can analyze the user's past registration history and suggest the most efficient registration method. This allows the system to select the optimal registration method by analyzing past registration history.

[0039] The voice registration unit can filter out the user's current ambient noise during voice registration to remove noise. For example, if the user is in a noisy environment, the unit uses noise-canceling technology to register the voice clearly. It can also minimize ambient noise when the user is in a quiet environment. Furthermore, if the user is registering their voice while moving, the unit can automatically remove ambient noise. This allows for clear voice registration by filtering out ambient noise and removing unwanted sounds.

[0040] The registration unit can prioritize registering voices that are highly relevant, taking into account the user's geographical location during voice registration. For example, if the user is in a specific location, the registration unit will prioritize registering voices related to that location. Furthermore, if the user is traveling, the registration unit can prioritize registering voices related to their travel destination. Additionally, if the user is at home, the registration unit can prioritize registering voices related to their home. This allows for the prioritization of highly relevant voices by considering geographical location information.

[0041] The registration unit can analyze a user's social media activity when they register a voice and register relevant voices. For example, the registration unit can prioritize registering voices related to topics the user is discussing on social media. It can also prioritize registering voices related to accounts the user follows on social media. Furthermore, it can prioritize registering voices related to groups the user participates in on social media. In this way, relevant voices can be registered by analyzing social media activity.

[0042] The acquisition unit can analyze the user's past conversation history and select the optimal acquisition method. For example, the acquisition unit will prioritize suggesting acquisition methods (voice, text, etc.) that the user has frequently used in the past. Furthermore, the acquisition unit can suggest the optimal acquisition method for a specific time period based on the user's past conversation history. In addition, the acquisition unit can analyze the user's past conversation history and suggest the most efficient acquisition method. This allows the system to select the optimal acquisition method by analyzing past conversation history.

[0043] The acquisition unit can filter out the user's current ambient noise to remove noise when acquiring a conversation. For example, if the user is in a noisy environment, the acquisition unit can use noise cancellation technology to acquire the conversation clearly. Furthermore, if the user is in a quiet environment, the acquisition unit can minimize ambient noise to acquire the conversation. In addition, if the acquisition unit is acquiring a conversation while the user is moving, it can automatically remove ambient noise. This allows for clear conversation acquisition by filtering out ambient noise and removing unwanted sounds.

[0044] The acquisition unit can prioritize the acquisition of highly relevant conversations by considering the user's geographical location information when acquiring conversations. For example, if the user is in a specific location, the acquisition unit will prioritize the acquisition of conversations related to that location. Furthermore, if the user is traveling, the acquisition unit can prioritize the acquisition of conversations related to their travel destination. Additionally, if the user is at home, the acquisition unit can prioritize the acquisition of conversations related to their home. In this way, by considering geographical location information, highly relevant conversations can be prioritized.

[0045] The retrieval unit can analyze the user's social media activity when retrieving conversations and retrieve relevant conversations. For example, the retrieval unit can prioritize retrieving conversations related to topics the user is discussing on social media. It can also prioritize retrieving conversations related to accounts the user follows on social media. Furthermore, it can prioritize retrieving conversations related to groups the user participates in on social media. In this way, relevant conversations can be retrieved by analyzing social media activity.

[0046] The suggestion function can adjust the level of detail of the options based on the importance of the conversation. For example, in important conversations, the suggestion function will present detailed options. In general conversations, it can also present concise options. Furthermore, in urgent conversations, it can present options that allow for quick responses. By adjusting the level of detail of the options according to the importance of the conversation, the appropriate options can be presented.

[0047] The suggestion function can apply different suggestion algorithms depending on the category of the conversation. For example, in a business conversation, the suggestion function will present professional options. In a family conversation, it can also present friendly options. Furthermore, in an emergency conversation, it can present options that allow for a quick response. By applying a suggestion algorithm tailored to the category of the conversation, the system can present the most suitable options.

[0048] The suggestion function can prioritize options based on when the conversation began. For example, it will present the most relevant options immediately after an important conversation has occurred. It can also prioritize suggesting the next course of action while the conversation is ongoing. Furthermore, it can present follow-up options after the conversation has ended. This allows for the presentation of appropriate options by prioritizing them based on when the conversation began.

[0049] The suggestion function can adjust the order of options based on the relevance of the conversation when making a suggestion. For example, the suggestion function may present the most relevant option first. It can also postpone less relevant options. Furthermore, the suggestion function can dynamically adjust the order of options according to the flow of the conversation. This allows it to present the optimal option by adjusting the order of options based on the relevance of the conversation.

[0050] The suggestion function can present the most suitable options by referring to the user's past selection history. For example, it can present the best options based on the options the user has previously selected. Furthermore, the suggestion function can prioritize presenting the user's preferred options based on their past selection history. It can also analyze the user's past selection history and present the most effective options. This allows the suggestion function to present the most suitable options by referring to the user's past selection history.

[0051] The suggestion function can customize the options based on the user's current situation when making suggestions. For example, if the user is in a meeting, the suggestion function will prioritize options related to the meeting. It can also prioritize options related to travel if the user is traveling. Furthermore, if the user is taking a break, it can prioritize options that promote relaxation. This allows the system to present the most suitable options by customizing them based on the user's current situation.

[0052] The suggestion function can present the most suitable options by considering the user's geographical location. For example, if the user is in a specific location, the suggestion function will prioritize options related to that location. It can also prioritize options related to the user's travel destination if the user is traveling. Furthermore, if the user is at home, the suggestion function can prioritize options related to home. This allows the system to present the most suitable options by considering the user's geographical location.

[0053] The suggestion department can analyze the user's social media activity and propose options when making suggestions. For example, the suggestion department can prioritize options related to topics the user is discussing on social media. It can also prioritize options related to accounts the user follows on social media. Furthermore, it can prioritize options related to groups the user participates in on social media. In this way, by analyzing the user's social media activity, it can present the most suitable options.

[0054] The suggestion function can optimize its suggestion algorithm by receiving feedback on the user's past choices during the suggestion process. For example, the suggestion function optimizes the suggestion algorithm based on the results of choices the user has previously made. Furthermore, the suggestion function can prioritize presenting effective choices based on the user's past choices. In addition, the suggestion function can analyze the user's past choices and improve the suggestion algorithm. This allows for the optimization of the suggestion algorithm by providing feedback on the user's past choices.

[0055] The suggestion function can present the optimal options by considering the user's device information during the suggestion process. For example, if the user is using a smartphone, the suggestion function can provide options that match the screen size. If the user is using a tablet, the suggestion function can also provide options optimized for larger screens. Furthermore, if the user is using a smartwatch, the suggestion function can provide concise and highly visible options. In this way, the optimal options can be presented by considering the user's device information.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The suggestion function can analyze a user's past selection history and propose the optimal choice. For example, it can present the best option based on the user's past choices. It can also prioritize presenting preferred options based on the user's past selection history. Furthermore, it can analyze the user's past selection history and present the most effective option. In this way, it can present the optimal choice by referring to the user's past selection history.

[0058] The suggestion function can customize the options based on the user's current situation. For example, if the user is in a meeting, it can prioritize options related to the meeting. Similarly, if the user is traveling, it can prioritize options related to travel. Furthermore, if the user is on a break, it can prioritize options that promote relaxation. This allows the system to present the most suitable options by customizing them based on the user's current situation.

[0059] The proposal function can adjust the level of detail of the options based on the importance of the conversation. For example, in important conversations, it can present detailed options. In general conversations, it can present concise options. Furthermore, in urgent conversations, it can present options that allow for quick responses. By adjusting the level of detail of the options according to the importance of the conversation, it can present appropriate options.

[0060] The suggestion function can apply different suggestion algorithms depending on the category of the conversation. For example, in a business conversation, it can present professional options. In a family conversation, it can present friendly options. Furthermore, in an emergency conversation, it can present options that allow for a quick response. By applying a suggestion algorithm tailored to the category of the conversation, the system can present the most suitable options.

[0061] The suggestion function can optimize its suggestion algorithm by incorporating feedback from the user's past choices. For example, it can optimize the suggestion algorithm based on the results of choices the user has previously made. It can also prioritize presenting effective choices based on the user's past choices. Furthermore, it can analyze the user's past choices and improve the suggestion algorithm. In this way, the suggestion algorithm can be optimized by incorporating feedback from the user's past choices.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The registration section registers the user's voice. User voices include audio data, text data, and feedback. For example, users can record their voices and save them as audio data. Users can also input their voices as text data. Furthermore, feedback from users can be received and reflected in the voice registration. Step 2: The acquisition unit acquires the conversation. The conversation includes voice conversations, text chats, and video conversations. For example, when a user has a voice conversation, the audio is acquired in real time. It can also acquire the content of text chats. Furthermore, it can acquire the audio and video of video conversations. Step 3: The suggestion unit proposes action options based on the conversation flow captured by the acquisition unit. These action options include the type of action proposed and the number of options available. For example, it may present the user with multiple options based on the conversation content. It can also suggest the most suitable option to the user. Furthermore, the method of presenting the options can be adjusted to present them in a format that is easy for the user to understand.

[0064] (Example of form 2) The novel game-type AI agent system according to an embodiment of the present invention is a system that facilitates decision-making by presenting multiple options during conversations. The aim of this system is to enable easy decision-making by presenting options on a display in a novel game style. For example, the user registers their voice. Next, the AI ​​agent constantly listens to the conversation and suggests action options on the screen based on the flow of the conversation. For example, it can be used in a wide range of scenarios, such as family conversations, customer negotiations, call center operations, and presentations. The options are presented like in a novel game, and the user can choose the best option from among them. For example, in a conversation with one's wife, in response to the statement, "It looks like the weather will be nice next Sunday," options such as "Wait and see," "Do nothing," "Increase favorability," and "Decrease favorability" are presented. In a call center, in response to the statement, "The power won't turn on, isn't it broken?", options such as "Lower the temperature" and "Ask where to buy it" are presented. This mechanism allows the user to choose a good option without choosing an option that is disadvantageous to them. Furthermore, by projecting it onto a PC screen or smart glasses screen, decision-making can be done quickly. Even in situations where nervousness prevents you from thinking of other options, this tool allows you to quickly learn about alternative choices. This enables the novel-game-style AI agent system to facilitate user decision-making.

[0065] The novel game-type AI agent system according to this embodiment comprises a registration unit, an acquisition unit, and a suggestion unit. The registration unit registers the user's voice. The user's voice includes, but is not limited to, voice data, text data, and feedback. For example, the registration unit allows the user to record their voice and save it as voice data. The registration unit also allows the user to input their voice as text data. Furthermore, the registration unit can receive feedback from the user and reflect it in the voice registration. The acquisition unit acquires conversations. Conversations include, but are not limited to, voice conversations, text chats, and video conversations. For example, the acquisition unit acquires the voice of a user when they engage in a voice conversation in real time. The acquisition unit can also acquire the content of text chats. Furthermore, the acquisition unit can acquire the audio and video of video conversations. The suggestion unit proposes action options based on the flow of conversation acquired by the acquisition unit. Action options include, but are not limited to, the type of action proposed and the number of options. For example, the suggestion unit presents the user with multiple options based on the content of the conversation. Furthermore, the suggestion function can propose the most suitable option to the user from among the available choices. In addition, the suggestion function can adjust the presentation method of the choices, displaying them in a format that is easy for the user to understand. This allows the novel-game type AI agent system to facilitate decision-making by registering the user's voice, capturing conversations, and suggesting action options.

[0066] The registration unit registers user voices. User voices include, but are not limited to, audio data, text data, and feedback. For example, the registration unit can receive voice recordings from users and save them as audio data. The registration unit can also receive voice input from users as text data. Furthermore, the registration unit can receive feedback from users and incorporate it into voice registration. Specifically, users can use a dedicated application to record their voices and upload the audio data to a cloud server. The audio data is pre-processed for noise reduction and sound quality improvement before being stored in the registration unit. If a user inputs voice data as text, speech recognition technology can be used to convert the speech to text and store it in the registration unit. Furthermore, user feedback can include evaluations of the quality of the audio data and the accuracy of the text data. This allows the registration unit to register user voices in various formats and respond flexibly to user needs. In addition, the registration unit manages the registration history of user voices and can understand user preferences and trends by referring to past data. This allows the registration unit to efficiently and effectively register user feedback, thereby improving the overall performance of the system.

[0067] The acquisition unit acquires conversations. These conversations include, but are not limited to, voice conversations, text chats, and video conversations. For example, the acquisition unit acquires audio in real time when a user engages in a voice conversation. It can also acquire the content of text chats. Furthermore, it can acquire audio and video from video conversations. Specifically, when a user engages in a voice conversation, the acquisition unit acquires audio data through a microphone and analyzes it in real time. The audio data is pre-processed for noise reduction and sound quality improvement before being sent to the analysis unit. Additionally, when acquiring text chat content, the acquisition unit can collect text data entered by the user in real time and send it to the analysis unit. Furthermore, when acquiring audio and video from video conversations, the acquisition unit collects data through a camera and microphone and analyzes it in real time. This allows the acquisition unit to acquire diverse formats of conversation data in real time, improving the overall system performance. Furthermore, the acquisition unit centrally manages the acquired conversation data and can collaborate with other systems and departments as needed. For example, the acquired data can be stored on a cloud server and made accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data acquisition, flexible responses to specific situations and conditions become possible. This allows the acquisition unit to efficiently and effectively acquire conversational data, improving the overall system performance.

[0068] The suggestion unit proposes action options based on the conversation flow captured by the acquisition unit. These action options include, but are not limited to, the type of action proposed and the number of options. For example, the suggestion unit presents multiple options to the user based on the conversation content. It can also suggest the most suitable option from among these choices. Furthermore, the suggestion unit can adjust the presentation method of the options to make them easier for the user to understand. Specifically, the suggestion unit analyzes the conversation data transmitted from the acquisition unit to understand the conversation flow and context. Using AI, it analyzes the conversation content and the user's intent to generate appropriate action options. For example, if a user is seeking advice on a specific problem, the suggestion unit can present multiple options for solving the problem. The suggestion unit can also consider the user's past selection history and preferences when suggesting the most suitable option from among the choices. Furthermore, the suggestion unit can adjust the presentation method of the options to make them easier for the user to understand. For example, it can graphically display options using a visual interface or explain options using voice guidance. This allows the suggestion unit to quickly propose appropriate action options to the user, facilitating decision-making. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. This allows the proposal department to provide users with high-quality proposals and improve the overall system performance.

[0069] The proposal section can present choices in a way similar to a visual novel game. For example, the proposal section can present choices using a visual interface. For instance, it can display choices on the screen, making them easier for the user to understand visually. Furthermore, the proposal section can adjust the display format of the choices to make them easier for the user to select. For example, it can display choices as buttons, allowing the user to select them with a single click. This presentation of choices, similar to a visual novel game, makes them easier for the user to understand visually.

[0070] The suggestion function can be used in a wide range of situations, including family conversations, customer negotiations, call center interactions, and presentations. For example, in conversations within the home, the suggestion function can present users with appropriate options. In business settings, it can also present users with appropriate options during customer negotiations and presentations. Furthermore, in call center interactions, the suggestion function can present operators with options to help them respond appropriately to customers. As a result, the suggestion function, usable in a wide range of situations, can support decision-making in various circumstances.

[0071] The suggestion function helps users choose good options and avoid unfavorable ones. For example, the suggestion function proposes the best option for the user from among the available choices. For instance, it prioritizes presenting options with low risk and a high probability of success. It can also prioritize presenting options with high benefits. This allows users to choose better options, thus improving the quality of decision-making.

[0072] The proposal unit can be projected onto a PC screen or smart glasses screen. For example, the proposal unit can display options on a PC screen, allowing the user to visually confirm their choices. Alternatively, the proposal unit can project options onto a smart glasses screen, enabling the user to visually confirm their choices. This allows users to visually confirm their choices by projecting them onto a PC screen or smart glasses screen.

[0073] The proposal department can quickly provide alternative options even in situations where nervousness might prevent users from thinking of other choices. For example, it can present appropriate options to users who are nervous during a presentation. It can also present appropriate options to users who are nervous during an interview. Furthermore, it can present appropriate options to users who are nervous during negotiations. This allows users to know about other options even in stressful situations, making decision-making easier.

[0074] The registration unit can estimate the user's emotions and adjust the timing of voice registration based on the estimated emotions. For example, if the user is relaxed, the registration unit can send a notification prompting voice registration. The registration unit can also temporarily postpone voice registration if the user is stressed. Furthermore, if the user is focused, the registration unit can select the optimal timing for voice registration. This allows for voice registration at the optimal time by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The registration unit can analyze a user's past voice registration history and select the optimal registration method. For example, the registration unit will prioritize suggesting registration methods (voice, text, etc.) that the user has frequently used in the past. Furthermore, the registration unit can suggest the most suitable registration method for a specific time period based on the user's past registration history. In addition, the registration unit can analyze the user's past registration history and suggest the most efficient registration method. This allows the system to select the optimal registration method by analyzing past registration history.

[0076] The voice registration unit can filter out the user's current ambient noise during voice registration to remove noise. For example, if the user is in a noisy environment, the unit uses noise-canceling technology to register the voice clearly. It can also minimize ambient noise when the user is in a quiet environment. Furthermore, if the user is registering their voice while moving, the unit can automatically remove ambient noise. This allows for clear voice registration by filtering out ambient noise and removing unwanted sounds.

[0077] The registration unit can estimate the user's emotions and determine the priority of voices to register based on the estimated emotions. For example, if the user is nervous, the registration unit will prioritize the registration of important voices. It can also prioritize the registration of detailed voices if the user is relaxed. Furthermore, if the user is in a hurry, the registration unit can prioritize the registration of concise voices. This allows for the priority of important voices to be registered by determining voice priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The registration unit can prioritize registering voices that are highly relevant, taking into account the user's geographical location during voice registration. For example, if the user is in a specific location, the registration unit will prioritize registering voices related to that location. Furthermore, if the user is traveling, the registration unit can prioritize registering voices related to their travel destination. Additionally, if the user is at home, the registration unit can prioritize registering voices related to their home. This allows for the prioritization of highly relevant voices by considering geographical location information.

[0079] The registration unit can analyze a user's social media activity when they register a voice and register relevant voices. For example, the registration unit can prioritize registering voices related to topics the user is discussing on social media. It can also prioritize registering voices related to accounts the user follows on social media. Furthermore, it can prioritize registering voices related to groups the user participates in on social media. In this way, relevant voices can be registered by analyzing social media activity.

[0080] The acquisition unit can estimate the user's emotions and adjust the timing of conversation acquisition based on the estimated emotions. For example, if the user is relaxed, the acquisition unit can send a notification prompting conversation acquisition. The acquisition unit can also temporarily postpone conversation acquisition if the user is stressed. Furthermore, if the user is focused, the acquisition unit can select the optimal timing for conversation acquisition. This allows for conversation acquisition at the optimal time by adjusting the timing of conversation acquisition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The acquisition unit can analyze the user's past conversation history and select the optimal acquisition method. For example, the acquisition unit will prioritize suggesting acquisition methods (voice, text, etc.) that the user has frequently used in the past. Furthermore, the acquisition unit can suggest the optimal acquisition method for a specific time period based on the user's past conversation history. In addition, the acquisition unit can analyze the user's past conversation history and suggest the most efficient acquisition method. This allows the system to select the optimal acquisition method by analyzing past conversation history.

[0082] The acquisition unit can filter out the user's current ambient noise to remove noise when acquiring a conversation. For example, if the user is in a noisy environment, the acquisition unit can use noise cancellation technology to acquire the conversation clearly. Furthermore, if the user is in a quiet environment, the acquisition unit can minimize ambient noise to acquire the conversation. In addition, if the acquisition unit is acquiring a conversation while the user is moving, it can automatically remove ambient noise. This allows for clear conversation acquisition by filtering out ambient noise and removing unwanted sounds.

[0083] The acquisition unit can estimate the user's emotions and determine the priority of conversations to acquire based on the estimated emotions. For example, if the user is nervous, the acquisition unit will prioritize acquiring important conversations. It can also prioritize acquiring detailed conversations if the user is relaxed. Furthermore, if the user is in a hurry, the acquisition unit can prioritize acquiring concise conversations. This allows for the priority acquisition of important conversations by determining conversation priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The acquisition unit can prioritize the acquisition of highly relevant conversations by considering the user's geographical location information when acquiring conversations. For example, if the user is in a specific location, the acquisition unit will prioritize the acquisition of conversations related to that location. Furthermore, if the user is traveling, the acquisition unit can prioritize the acquisition of conversations related to their travel destination. Additionally, if the user is at home, the acquisition unit can prioritize the acquisition of conversations related to their home. In this way, by considering geographical location information, highly relevant conversations can be prioritized.

[0085] The retrieval unit can analyze the user's social media activity when retrieving conversations and retrieve relevant conversations. For example, the retrieval unit can prioritize retrieving conversations related to topics the user is discussing on social media. It can also prioritize retrieving conversations related to accounts the user follows on social media. Furthermore, it can prioritize retrieving conversations related to groups the user participates in on social media. In this way, relevant conversations can be retrieved by analyzing social media activity.

[0086] The suggestion function can estimate the user's emotions and adjust how options are presented based on those emotions. For example, if the user is nervous, the suggestion function will present simple, highly visible options. If the user is relaxed, the suggestion function can also present options with more detailed information. Furthermore, if the user is in a hurry, the suggestion function can present options that get straight to the point. By adjusting how options are presented according to the user's emotions, the optimal options can be presented. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The suggestion function can adjust the level of detail of the options based on the importance of the conversation. For example, in important conversations, the suggestion function will present detailed options. In general conversations, it can also present concise options. Furthermore, in urgent conversations, it can present options that allow for quick responses. By adjusting the level of detail of the options according to the importance of the conversation, the appropriate options can be presented.

[0088] The suggestion function can apply different suggestion algorithms depending on the category of the conversation. For example, in a business conversation, the suggestion function will present professional options. In a family conversation, it can also present friendly options. Furthermore, in an emergency conversation, it can present options that allow for a quick response. By applying a suggestion algorithm tailored to the category of the conversation, the system can present the most suitable options.

[0089] The suggestion function can estimate the user's emotions and adjust the length of the options based on the estimated emotions. For example, if the user is nervous, the suggestion function will present short, concise options. If the user is relaxed, the suggestion function can also present longer options with more detailed explanations. Furthermore, if the user is in a hurry, the suggestion function can present short options that can be chosen quickly. This allows the system to present the optimal option by adjusting the length of the options according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The suggestion function can prioritize options based on when the conversation began. For example, it will present the most relevant options immediately after an important conversation has occurred. It can also prioritize suggesting the next course of action while the conversation is ongoing. Furthermore, it can present follow-up options after the conversation has ended. This allows for the presentation of appropriate options by prioritizing them based on when the conversation began.

[0091] The suggestion function can adjust the order of options based on the relevance of the conversation when making a suggestion. For example, the suggestion function may present the most relevant option first. It can also postpone less relevant options. Furthermore, the suggestion function can dynamically adjust the order of options according to the flow of the conversation. This allows it to present the optimal option by adjusting the order of options based on the relevance of the conversation.

[0092] The suggestion function can present the most suitable options by referring to the user's past selection history. For example, it can present the best options based on the options the user has previously selected. Furthermore, the suggestion function can prioritize presenting the user's preferred options based on their past selection history. It can also analyze the user's past selection history and present the most effective options. This allows the suggestion function to present the most suitable options by referring to the user's past selection history.

[0093] The suggestion function can customize the options based on the user's current situation when making suggestions. For example, if the user is in a meeting, the suggestion function will prioritize options related to the meeting. It can also prioritize options related to travel if the user is traveling. Furthermore, if the user is taking a break, it can prioritize options that promote relaxation. This allows the system to present the most suitable options by customizing them based on the user's current situation.

[0094] The suggestion function can present the most suitable options by considering the user's geographical location. For example, if the user is in a specific location, the suggestion function will prioritize options related to that location. It can also prioritize options related to the user's travel destination if the user is traveling. Furthermore, if the user is at home, the suggestion function can prioritize options related to home. This allows the system to present the most suitable options by considering the user's geographical location.

[0095] The suggestion department can analyze the user's social media activity and propose options when making suggestions. For example, the suggestion department can prioritize options related to topics the user is discussing on social media. It can also prioritize options related to accounts the user follows on social media. Furthermore, it can prioritize options related to groups the user participates in on social media. In this way, by analyzing the user's social media activity, it can present the most suitable options.

[0096] The suggestion function can optimize its suggestion algorithm by receiving feedback on the user's past choices during the suggestion process. For example, the suggestion function optimizes the suggestion algorithm based on the results of choices the user has previously made. Furthermore, the suggestion function can prioritize presenting effective choices based on the user's past choices. In addition, the suggestion function can analyze the user's past choices and improve the suggestion algorithm. This allows for the optimization of the suggestion algorithm by providing feedback on the user's past choices.

[0097] The suggestion function can adjust how options are displayed based on the user's current emotional state when making suggestions. For example, if the user is tense, the suggestion function can provide a simple and highly visible display. If the user is relaxed, it can also provide a display that includes detailed information. Furthermore, if the user is in a hurry, it can provide a concise display. By adjusting how options are displayed based on the user's current emotional state, the optimal option can be presented. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The suggestion function can present the optimal options by considering the user's device information during the suggestion process. For example, if the user is using a smartphone, the suggestion function can provide options that match the screen size. If the user is using a tablet, the suggestion function can also provide options optimized for larger screens. Furthermore, if the user is using a smartwatch, the suggestion function can provide concise and highly visible options. In this way, the optimal options can be presented by considering the user's device information.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The suggestion function can estimate the user's emotions and adjust the order in which options are presented based on those emotions. For example, if the user is nervous, the simplest and lowest-risk option will be presented first. If the user is relaxed, options containing more detailed information may be prioritized. Furthermore, if the user is in a hurry, options that allow for a quick response may be presented first. In this way, the optimal option can be presented by adjusting the order in which options are presented according to the user's emotions.

[0101] The suggestion function can analyze a user's past selection history and propose the optimal choice. For example, it can present the best option based on the user's past choices. It can also prioritize presenting preferred options based on the user's past selection history. Furthermore, it can analyze the user's past selection history and present the most effective option. In this way, it can present the optimal choice by referring to the user's past selection history.

[0102] The proposal function can estimate the user's emotions and adjust the display format of the options based on those emotions. For example, if the user is nervous, it can provide a simple and highly visible display format. If the user is relaxed, it can provide a display format that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display format that gets straight to the point. In this way, by adjusting the display format of the options according to the user's emotions, the optimal option can be presented.

[0103] The suggestion function can customize the options based on the user's current situation. For example, if the user is in a meeting, it can prioritize options related to the meeting. Similarly, if the user is traveling, it can prioritize options related to travel. Furthermore, if the user is on a break, it can prioritize options that promote relaxation. This allows the system to present the most suitable options by customizing them based on the user's current situation.

[0104] The suggestion function can estimate the user's emotions and adjust the length of the options based on that estimation. For example, if the user is nervous, it can present short, concise options. If the user is relaxed, it can present longer options with more detailed explanations. Furthermore, if the user is in a hurry, it can present short options that can be chosen quickly. By adjusting the length of the options according to the user's emotions, the system can present the most suitable choice.

[0105] The proposal function can adjust the level of detail of the options based on the importance of the conversation. For example, in important conversations, it can present detailed options. In general conversations, it can present concise options. Furthermore, in urgent conversations, it can present options that allow for quick responses. By adjusting the level of detail of the options according to the importance of the conversation, it can present appropriate options.

[0106] The suggestion function can apply different suggestion algorithms depending on the category of the conversation. For example, in a business conversation, it can present professional options. In a family conversation, it can present friendly options. Furthermore, in an emergency conversation, it can present options that allow for a quick response. By applying a suggestion algorithm tailored to the category of the conversation, the system can present the most suitable options.

[0107] The suggestion function can estimate the user's emotions and determine the priority of options based on those emotions. For example, if the user is nervous, it can prioritize important options. If the user is relaxed, it can prioritize detailed options. Furthermore, if the user is in a hurry, it can prioritize concise options. In this way, by determining the priority of options according to the user's emotions, it can present important options preferentially.

[0108] The suggestion function can optimize its suggestion algorithm by incorporating feedback from the user's past choices. For example, it can optimize the suggestion algorithm based on the results of choices the user has previously made. It can also prioritize presenting effective choices based on the user's past choices. Furthermore, it can analyze the user's past choices and improve the suggestion algorithm. In this way, the suggestion algorithm can be optimized by incorporating feedback from the user's past choices.

[0109] The proposal function can adjust how options are displayed based on the user's current emotional state during the proposal process. For example, if the user is stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. By adjusting how options are displayed based on the user's current emotional state, the optimal option can be presented.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The registration section registers the user's voice. User voices include audio data, text data, and feedback. For example, users can record their voices and save them as audio data. Users can also input their voices as text data. Furthermore, feedback from users can be received and reflected in the voice registration. Step 2: The acquisition unit acquires the conversation. The conversation includes voice conversations, text chats, and video conversations. For example, when a user has a voice conversation, the audio is acquired in real time. It can also acquire the content of text chats. Furthermore, it can acquire the audio and video of video conversations. Step 3: The suggestion unit proposes action options based on the conversation flow captured by the acquisition unit. These action options include the type of action proposed and the number of options available. For example, it may present the user with multiple options based on the conversation content. It can also suggest the most suitable option to the user. Furthermore, the method of presenting the options can be adjusted to present them in a format that is easy for the user to understand.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the registration unit, acquisition unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the registration unit can record the user's voice using the control unit 46A of the smart device 14 and save it as voice data. The acquisition unit can acquire voice conversations in real time using the microphone 38B of the smart device 14. The proposal unit can generate multiple options based on the content of the conversation using the identification processing unit 290 of the data processing unit 12 and present them on the display 40A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the registration unit, acquisition unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the registration unit can record the user's voice using the control unit 46A of the smart glasses 214 and store it as voice data. The acquisition unit can acquire voice conversations in real time using the microphone 238 of the smart glasses 214. The proposal unit can generate multiple options based on the content of the conversation using the identification processing unit 290 of the data processing unit 12 and present them on the display of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the registration unit, acquisition unit, and proposal unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the registration unit can record the user's voice using the control unit 46A of the headset terminal 314 and save it as voice data. The acquisition unit can acquire voice conversations in real time using, for example, the microphone 238 of the headset terminal 314. The proposal unit can generate multiple options based on the content of the conversation using the identification processing unit 290 of the data processing unit 12 and present them on the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements, including the registration unit, acquisition unit, and proposal unit described above, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the registration unit can record the user's voice using the control unit 46A of the robot 414 and save it as voice data. The acquisition unit can acquire voice conversations in real time using, for example, the microphone 238 of the robot 414. The proposal unit can generate multiple options based on the content of the conversation using, for example, the identification processing unit 290 of the data processing unit 12 and present them on the display of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A registration section for registering user feedback, The unit that retrieves the conversation, A proposal unit that suggests action options based on the conversation flow acquired by the acquisition unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned proposal section is, Present choices like in a visual novel. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, It can be used in a wide range of situations, such as family conversations, customer negotiations, call center interactions, and presentations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, This allows users to choose good options and avoid choosing options that are disadvantageous to them. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Project onto a PC screen or smart glasses screen. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Even in situations where you can't think of other options when you're nervous, you can quickly learn about other options. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned registration unit is It estimates the user's emotions and adjusts the timing of voice registration based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned registration unit is Analyze the user's past voice registration history and select the optimal registration method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned registration unit is When registering your voice, the system filters out the user's current ambient noise to remove unwanted sounds. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned registration unit is The system estimates the user's emotions and determines the priority of voices to register based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned registration unit is When registering voices, the system prioritizes registering voices that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned registration unit is When registering a voice, the system analyzes the user's social media activity and registers relevant voices. The system described in Appendix 1, characterized by the features described herein. (Note 13) The acquisition unit is, It estimates the user's emotions and adjusts the timing of conversation acquisition based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The acquisition unit is, Analyze the user's past conversation history and select the optimal method for retrieving it. The system described in Appendix 1, characterized by the features described herein. (Note 15) The acquisition unit is, When acquiring conversations, the system filters out the user's current ambient noise to remove noise. The system described in Appendix 1, characterized by the features described herein. (Note 16) The acquisition unit is, It estimates the user's emotions and determines the priority of conversations to pursue based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The acquisition unit is, When retrieving conversations, the system prioritizes retrieving highly relevant conversations by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The acquisition unit is, When retrieving conversations, the system analyzes the user's social media activity and retrieves relevant conversations. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts how choices are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making suggestions, adjust the level of detail of the options based on their importance in the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the category of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the options based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, prioritize the options based on when the conversation took place. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making suggestions, adjust the order of options based on their relevance in the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making suggestions, the system refers to the user's past selection history to present the most suitable options. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making suggestions, customize the options based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, we will consider the user's geographical location to present the most suitable options. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and suggest options. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making suggestions, the user's past selections are fed back to optimize the suggestion algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making suggestions, adjust how options are displayed based on the user's current emotional state. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When making a proposal, we will consider the user's device information and present the most suitable options. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 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 registration section for registering user feedback, The unit that retrieves the conversation, A proposal unit that suggests action options based on the conversation flow acquired by the acquisition unit, Equipped with A system characterized by the following features.

2. The aforementioned proposal section is, Present choices like in a visual novel. The system according to feature 1.

3. The aforementioned proposal section is, It can be used in a wide range of situations, such as family conversations, customer negotiations, call center interactions, and presentations. The system according to feature 1.

4. The aforementioned proposal section is, This allows users to choose good options and avoid choosing options that are disadvantageous to them. The system according to feature 1.

5. The aforementioned proposal section is, Project onto a PC screen or smart glasses screen. The system according to feature 1.

6. The aforementioned proposal section is, Even in situations where you can't think of other options when you're nervous, you can quickly learn about other options. The system according to feature 1.

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

8. The aforementioned registration unit is Analyze the user's past voice registration history and select the optimal registration method. The system according to feature 1.

9. The aforementioned registration unit is When registering your voice, the system filters out the user's current ambient noise to remove unwanted sounds. The system according to feature 1.

Images