system

Abstract

The system according to the embodiment aims to support linking answers and suggestions generated by generative AI to real-world implementation. [Solution] A system according to an embodiment includes a reception unit, a generation unit, and an identification unit. The reception unit receives questions and ideas from users. The generation unit analyzes the questions and ideas received by the reception unit and generates answers and proposals. The identification unit identifies relevant internal parties based on the answers and proposals generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not provide sufficient support for linking the answers and suggestions generated by generative AI to real-world implementation, and risk relying on individual capabilities and connections.

[0005] The system according to the embodiment aims to support linking answers and suggestions generated by generative AI to real-world implementation. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and an identification unit. The reception unit receives questions and ideas from users. The generation unit analyzes the questions and ideas received by the reception unit and generates answers and proposals. The identification unit identifies relevant internal parties based on the answers and proposals generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can assist in linking answers and suggestions provided by generative AI to real-world implementation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses a generative AI to resolve questions and propose various ideas. In this system, the generative AI provides appropriate answers and suggestions to questions and ideas entered by the user. However, this alone has limitations in terms of realizing the idea, as it relies on individual skills and connections. Therefore, the system adds a function that introduces internal stakeholders related to the answer content to further promote realization. For example, a user inputs a question or idea, such as, "I'm thinking of an idea for a new project. How should I proceed?" This question is input to the generative AI. The generative AI then analyzes the input question and generates an appropriate answer or suggestion. For example, it generates an answer such as, "When proceeding with a project, it is important to first clarify the goal, then identify the necessary resources, and finally create a schedule." Furthermore, the generative AI identifies relevant internal stakeholders based on the answer content. For example, it identifies employees knowledgeable in project management or resource management. Finally, the system introduces the user to relevant internal stakeholders along with the answer or suggestion generated by the generative AI. This provides support for the user in taking specific actions, facilitating realization. This system allows users to take specific actions based on suggestions made by the generative AI, enabling more realistic realization without relying on personal abilities or connections. This allows the system to generate appropriate answers and suggestions to users' questions and ideas, and identify relevant internal stakeholders to support real-world realization.

[0029] A system according to an embodiment includes a reception unit, a generation unit, and an identification unit. The reception unit receives questions and ideas from a user. For example, a user can input a question such as, "I'm thinking of an idea for a new project. How should I proceed?" The reception unit inputs the user's questions and ideas to a generation AI. The generation unit uses the generation AI to analyze the questions and ideas received by the reception unit and generate appropriate answers and proposals. For example, the generation AI generates an answer such as, "When proceeding with a project, it is important to first clarify the goals, then identify the necessary resources, and finally create a schedule." The generation unit can use the generation AI to analyze the content of the questions and ideas and generate appropriate answers and proposals. The generation AI analyzes the content of the questions and ideas using a text generation AI (e.g., LLM) or a multimodal generation AI, and generates appropriate answers and proposals. The identification unit identifies relevant internal stakeholders based on the answers and proposals generated by the generation unit. For example, the identification unit identifies employees who are knowledgeable in project management or resource management. The identification unit can identify relevant company stakeholders based on the answers and suggestions generated by the generation AI. As a result, the system according to the embodiment can generate appropriate answers and suggestions for user questions and ideas and identify relevant company stakeholders, thereby supporting realization of the ideas.

[0030] The identification unit can identify relevant internal stakeholders based on the answers and suggestions generated by the generation AI. For example, the identification unit can identify employees who are knowledgeable in project management or employees who are well-versed in resource management based on the answers and suggestions generated by the generation AI. The identification unit can identify relevant internal stakeholders based on the answers and suggestions generated by the generation AI. For example, the identification unit can identify relevant internal stakeholders using an AI model that inputs answers and suggestions generated by the generation AI and outputs relevant internal stakeholders. In this way, by identifying relevant internal stakeholders based on the answers and suggestions generated by the generation AI, the user can receive support for taking specific actions.

[0031] The feedback unit allows the user to provide feedback on the answers of the generation AI, allowing the generation AI to learn. For example, the feedback unit allows the user to provide evaluations and comments on the answers of the generation AI. The feedback unit allows the generation AI to learn based on user feedback. For example, the feedback unit can use user evaluations and comments as input and use them as learning data for the generation AI. This allows the generation AI to learn based on user feedback, improving the accuracy of the system.

[0032] The contact unit allows the user to contact the introduced party. For example, the contact unit allows the user to contact the introduced party via email, chat, telephone, etc. The contact unit provides a means for the user to contact the introduced party. For example, the contact unit provides contact information of the introduced party so that the user can contact the introduced party directly. This allows the user to contact the introduced party, thereby facilitating realistic realization.

[0033] The reception unit can analyze the user's past question and idea submission history and select an appropriate reception method. For example, the reception unit preferentially suggests reception methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can analyze the user's past question and idea submission history and select an appropriate reception method. For example, the reception unit provides related templates based on the content of the user's past questions and ideas. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past submission history. This improves user convenience by selecting the optimal reception method based on the user's past history.

[0034] The reception unit can filter questions and ideas based on the user's current project and areas of interest when receiving the questions and ideas. For example, the reception unit can only accept questions and ideas related to the user's current project and areas of interest when receiving the questions and ideas. For example, the reception unit can preferentially accept questions and ideas related to the user's areas of interest. The reception unit can also filter appropriate questions and ideas according to the progress of the user's project. In this way, by filtering based on the user's current project and areas of interest, highly relevant questions and ideas can be preferentially accepted.

[0035] When receiving questions or ideas, the reception unit can prioritize receiving highly relevant questions or ideas by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving questions or ideas related to that area. When receiving questions or ideas, the reception unit can prioritize receiving highly relevant questions or ideas by taking into account the user's geographical location information. For example, when the user is on a business trip, the reception unit prioritizes receiving questions or ideas related to the business trip destination. Furthermore, when the user is at home, the reception unit can also prioritize receiving questions or ideas related to the user's home. In this way, by taking into account the user's geographical location information, highly relevant questions or ideas can be prioritized.

[0036] The reception unit can analyze the user's social media activity when receiving a question or idea and receive related questions or ideas. The reception unit can, for example, receive related questions or ideas based on information shared by the user on social media. The reception unit can analyze the user's social media activity when receiving a question or idea and receive related questions or ideas. For example, the reception unit can analyze the user's social media activity history and preferentially receive related questions or ideas. The reception unit can also receive related questions or ideas based on topics the user is following on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially receive highly relevant questions and ideas.

[0037] The generation unit can adjust the level of detail when generating an answer or a proposal based on the importance of the question or idea. For example, the generation unit generates a detailed answer or proposal for a question or idea with a high level of importance. The generation unit can adjust the level of detail when generating an answer or a proposal based on the importance of the question or idea. For example, the generation unit generates a concise answer or proposal for a question or idea with a low level of importance. The generation unit can also generate answers or proposals with an appropriate level of detail depending on the importance of the question or idea. In this way, by adjusting the level of detail based on the importance of the question or idea, answers or proposals with an appropriate level are generated.

[0038] The generation unit can apply different generation algorithms depending on the category of the question or idea when generating an answer or proposal. For example, the generation unit applies a specialized technical generation algorithm to a technical question. The generation unit can apply different generation algorithms depending on the category of the question or idea when generating an answer or proposal. For example, the generation unit can apply a specialized business generation algorithm to a business-related idea. The generation unit can also apply a specialized creative generation algorithm to a creative idea. In this way, by applying different generation algorithms depending on the category of the question or idea, more appropriate answers and proposals can be generated.

[0039] The generation unit can determine the priority based on the time of submission of the question or idea when generating answers or proposals. For example, the generation unit generates answers or proposals by prioritizing recently submitted questions or ideas. The generation unit can determine the priority based on the time of submission of the question or idea when generating answers or proposals. For example, the generation unit generates answers or proposals for questions or ideas that were submitted earlier at a later date. The generation unit can also generate answers or proposals with appropriate priority based on the time of submission. In this way, by determining the priority based on the time of submission of the question or idea, answers or proposals are generated at an appropriate time.

[0040] The generation unit can adjust the order based on the relevance of the questions and ideas when generating answers and proposals. For example, the generation unit generates answers and proposals with priority to questions and ideas with high relevance. The generation unit can adjust the order based on the relevance of the questions and ideas when generating answers and proposals. For example, the generation unit generates answers and proposals later for questions and ideas with low relevance. The generation unit can also generate answers and proposals in an appropriate order depending on the relevance of the questions and ideas. In this way, by adjusting the order based on the relevance of the questions and ideas, highly relevant answers and proposals are generated with priority.

[0041] When identifying internal stakeholders, the identification department can select the most suitable stakeholders by referring to their past project participation histories. For example, the identification department preferentially identifies internal stakeholders who have participated in similar projects in the past. When identifying internal stakeholders, the identification department can select the most suitable stakeholders by referring to their past project participation histories. For example, the identification department preferentially identifies internal stakeholders with a high project success rate. The identification department can also preferentially identify internal stakeholders who have received high evaluations in past projects. In this way, the most suitable stakeholders are identified by referring to their past project participation histories.

[0042] When identifying internal stakeholders, the identification department can perform the identification based on the stakeholders' fields of expertise and skill sets. For example, the identification department identifies internal stakeholders who have the specialized knowledge required for the project. When identifying internal stakeholders, the identification department can perform the identification based on the stakeholders' fields of expertise and skill sets. For example, the identification department preferentially identifies internal stakeholders who have a specific skill set. The identification department can also identify internal stakeholders who have the optimal skill set according to the requirements of the project. In this way, by performing the identification based on the stakeholders' fields of expertise and skill sets, the optimal stakeholders for the project are identified.

[0043] When identifying internal stakeholders, the identification unit can select the most appropriate stakeholders by taking into account the geographic location information of the stakeholders. For example, the identification unit preferentially identifies internal stakeholders who are close to the user. When identifying internal stakeholders, the identification unit can select the most appropriate stakeholders by taking into account the geographic location information of the stakeholders. For example, in the case of a project related to a specific region, the identification unit preferentially identifies internal stakeholders who are in that region. Furthermore, when the user is on a business trip, the identification unit can also preferentially identify internal stakeholders who are at the business trip destination. In this way, the most appropriate stakeholders are identified by taking into account the geographic location information of the stakeholders.

[0044] When identifying internal stakeholders, the identification department can analyze the social media activities of the stakeholders to improve the accuracy of identification. For example, the identification department analyzes the social media activity history of the stakeholders to identify stakeholders suitable for related projects. When identifying internal stakeholders, the identification department can analyze the social media activities of the stakeholders to improve the accuracy of identification. For example, the identification department can improve the accuracy of identification based on information shared by the stakeholders on social media. The identification department can also improve the accuracy of identification by taking into account the number of followers and influence of the stakeholders on social media. In this way, the accuracy of identification is improved by analyzing the social media activities of the stakeholders.

[0045] When receiving feedback, the feedback unit can select the optimal reception method by referring to past feedback history. For example, the feedback unit preferentially suggests feedback methods (text, voice, etc.) that the user has used in the past. When receiving feedback, the feedback unit can select the optimal reception method by referring to past feedback history. For example, the feedback unit provides a related template based on the content of the user's past feedback. The feedback unit can also suggest the optimal reception method for a specific time period from the user's past feedback history. In this way, the optimal reception method is selected by referring to the past feedback history.

[0046] The feedback unit may select an optimal feedback reception method in consideration of device information of the user when receiving feedback. For example, if the user is using a smartphone, the feedback unit may provide a feedback form optimized for mobile devices. The feedback unit may select an optimal feedback reception method in consideration of device information of the user when receiving feedback. For example, if the user is using a tablet, the feedback unit may provide a feedback form optimized for a large screen. Furthermore, if the user is using a desktop computer, the feedback unit may provide a detailed feedback form. In this way, the optimal feedback reception method is selected by taking into consideration device information of the user.

[0047] When making contact, the contact unit can select the optimal contact method by referring to past contact history. For example, the contact unit preferentially suggests contact methods (email, telephone, etc.) that the user has used in the past. When making contact, the contact unit can select the optimal contact method by referring to past contact history. For example, the contact unit provides a related contact method based on the content of the user's past contact. The contact unit can also suggest the optimal contact method for a specific time period from the user's past contact history. In this way, the optimal contact method is selected by referring to the past contact history.

[0048] The contact unit can select the optimal contact method by taking into account device information of the user when making contact. For example, if the user is using a smartphone, the contact unit provides a contact method optimized for mobile devices. The contact unit can select the optimal contact method by taking into account device information of the user when making contact. For example, if the user is using a tablet, the contact unit provides a contact method optimized for a large screen. Furthermore, if the user is using a desktop, the contact unit can also provide detailed contact methods. In this way, the optimal contact method is selected by taking into account device information of the user.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The reception unit can analyze the user's past question and idea submission history and customize the method for receiving questions and ideas based on the user's preferences and tendencies. For example, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. Also, if the user has previously submitted questions or ideas during a specific time period, the reception unit can send notifications during that time period. This provides a more personalized reception method based on the user's past behavioral patterns.

[0051] The Identification Department monitors the progress of a user's current project in real time and can identify the most appropriate internal stakeholders depending on the project's phase. For example, in the early stages of a project, it can identify employees who are good at planning and generating ideas, and in the middle stages, it can identify employees who are well-versed in resource management. It can also identify employees who are good at quality control and delivery date management in the final stages of a project. This allows it to provide the most appropriate support depending on the project's progress.

[0052] The feedback section can analyze user-provided feedback in real time and immediately reflect it in the generation AI. For example, if a user provides feedback that the generation AI's answer "lacks specific examples," the generation AI will learn to include more specific examples in its next answer. Also, if a user provides feedback that "the answer is too long," the generation AI can adjust its next answer to aim for concise expression. This allows user feedback to be reflected quickly, improving the accuracy of the system.

[0053] The communication unit can automatically record the contact history when the user contacts the introduced parties, so that it can be referenced the next time the user contacts them. For example, if the user contacts them by email, the content and date and time of the email are recorded and displayed the next time the user contacts them. The communication unit can also record the contact history via chat or telephone in the same way, so that the user can easily check past interactions. This allows for smooth communication by referring to the contact history.

[0054] The reception unit can analyze the user's past question and idea submission history and customize the method for receiving questions and ideas based on the user's preferences and tendencies. For example, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. Also, if the user has previously submitted questions or ideas during a specific time period, the reception unit can send notifications during that time period. This provides a more personalized reception method based on the user's past behavioral patterns.

[0055] When receiving questions or ideas, the reception unit can filter them based on the user's current project or area of ​​interest. For example, the reception unit can only accept questions or ideas related to the user's current project. When receiving questions or ideas, the reception unit can filter them based on the user's current project or area of ​​interest. For example, the reception unit can preferentially accept questions or ideas related to the user's area of ​​interest. The reception unit can also filter appropriate questions or ideas according to the progress of the user's project. In this way, by filtering based on the user's current project or area of ​​interest, highly relevant questions or ideas can be preferentially accepted.

[0056] When receiving questions or ideas, the reception unit can prioritize receiving highly relevant questions or ideas by taking into account the user's geographical location information. For example, if the user is in a specific area, questions or ideas related to that area are prioritized. When receiving questions or ideas, the reception unit can prioritize receiving highly relevant questions or ideas by taking into account the user's geographical location information. For example, if the user is on a business trip, the reception unit can prioritize receiving questions or ideas related to the business trip destination. Furthermore, if the user is at home, the reception unit can also prioritize receiving questions or ideas related to the user's home. In this way, by taking into account the user's geographical location information, highly relevant questions or ideas can be prioritized.

[0057] The reception unit can analyze the user's social media activity when receiving a question or idea and receive related questions or ideas. For example, the reception unit can receive related questions or ideas based on information shared by the user on social media. The reception unit can analyze the user's social media activity when receiving a question or idea and receive related questions or ideas. For example, the reception unit can analyze the user's social media activity history and preferentially receive related questions or ideas. The reception unit can also receive related questions or ideas based on topics the user is following on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially receive highly relevant questions or ideas.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception unit accepts questions and ideas from users. For example, a user can input a question such as, "I'm thinking of an idea for a new project. How should I go about it?" The reception unit inputs the user's question or idea into the generation AI. Step 2: The generation unit uses a generation AI to analyze the questions and ideas received by the reception unit and generate appropriate answers and proposals. For example, the generation AI might generate an answer such as, "When it comes to progressing with a project, it's important to first clarify the goal, then identify the necessary resources, and finally create a schedule." The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to analyze the content of the questions and ideas and generate appropriate answers and proposals. Step 3: The Identification Department identifies relevant internal stakeholders based on the answers and suggestions generated by the Generation Department. For example, the Identification Department identifies employees who are knowledgeable about project management or resource management.

[0060] (Example 2) A system according to an embodiment of the present invention uses a generative AI to resolve questions and propose various ideas. In this system, the generative AI provides appropriate answers and suggestions to questions and ideas entered by the user. However, this alone has limitations in terms of realizing the idea, as it relies on individual skills and connections. Therefore, the system adds a function that introduces internal stakeholders related to the answer content to further promote realization. For example, a user inputs a question or idea, such as, "I'm thinking of an idea for a new project. How should I proceed?" This question is input to the generative AI. The generative AI then analyzes the input question and generates an appropriate answer or suggestion. For example, it generates an answer such as, "When proceeding with a project, it is important to first clarify the goal, then identify the necessary resources, and finally create a schedule." Furthermore, the generative AI identifies relevant internal stakeholders based on the answer content. For example, it identifies employees knowledgeable in project management or resource management. Finally, the system introduces the user to relevant internal stakeholders along with the answer or suggestion generated by the generative AI. This provides support for the user in taking specific actions, facilitating realization. This system allows users to take specific actions based on suggestions made by the generative AI, enabling more realistic realization without relying on personal abilities or connections. This allows the system to generate appropriate answers and suggestions to users' questions and ideas, and identify relevant internal stakeholders to support real-world realization.

[0061] A system according to an embodiment includes a reception unit, a generation unit, and an identification unit. The reception unit receives questions and ideas from a user. For example, a user can input a question such as, "I'm thinking of an idea for a new project. How should I proceed?" The reception unit inputs the user's questions and ideas to a generation AI. The generation unit uses the generation AI to analyze the questions and ideas received by the reception unit and generate appropriate answers and proposals. For example, the generation AI generates an answer such as, "When proceeding with a project, it is important to first clarify the goals, then identify the necessary resources, and finally create a schedule." The generation unit can use the generation AI to analyze the content of the questions and ideas and generate appropriate answers and proposals. The generation AI analyzes the content of the questions and ideas using a text generation AI (e.g., LLM) or a multimodal generation AI, and generates appropriate answers and proposals. The identification unit identifies relevant internal stakeholders based on the answers and proposals generated by the generation unit. For example, the identification unit identifies employees who are knowledgeable in project management or resource management. The identification unit can identify relevant company stakeholders based on the answers and suggestions generated by the generation AI. As a result, the system according to the embodiment can generate appropriate answers and suggestions for user questions and ideas and identify relevant company stakeholders, thereby supporting realization of the ideas.

[0062] The identification unit can identify relevant internal stakeholders based on the answers and suggestions generated by the generation AI. For example, the identification unit can identify employees who are knowledgeable in project management or employees who are well-versed in resource management based on the answers and suggestions generated by the generation AI. The identification unit can identify relevant internal stakeholders based on the answers and suggestions generated by the generation AI. For example, the identification unit can identify relevant internal stakeholders using an AI model that inputs answers and suggestions generated by the generation AI and outputs relevant internal stakeholders. In this way, by identifying relevant internal stakeholders based on the answers and suggestions generated by the generation AI, the user can receive support for taking specific actions.

[0063] The feedback unit allows the user to provide feedback on the answers of the generation AI, allowing the generation AI to learn. For example, the feedback unit allows the user to provide evaluations and comments on the answers of the generation AI. The feedback unit allows the generation AI to learn based on user feedback. For example, the feedback unit can use user evaluations and comments as input and use them as learning data for the generation AI. This allows the generation AI to learn based on user feedback, improving the accuracy of the system.

[0064] The contact unit allows the user to contact the introduced party. For example, the contact unit allows the user to contact the introduced party via email, chat, telephone, etc. The contact unit provides a means for the user to contact the introduced party. For example, the contact unit provides contact information of the introduced party so that the user can contact the introduced party directly. This allows the user to contact the introduced party, thereby facilitating realistic realization.

[0065] The reception unit can estimate the user's emotions and adjust the timing of accepting questions and ideas based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit accepts questions and ideas during times when the user is able to relax. The reception unit can estimate the user's emotions and adjust the timing of accepting questions and ideas based on the estimated user emotions. For example, if the user is excited, the reception unit can immediately accept questions and ideas on the spot. Also, if the user is tired, the reception unit can accept questions and ideas after the user has rested. This allows questions and ideas to be accepted at a more appropriate time by adjusting the timing of accepting questions and ideas according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0066] The reception unit can analyze the user's past question and idea submission history and select an appropriate reception method. For example, the reception unit preferentially suggests reception methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can analyze the user's past question and idea submission history and select an appropriate reception method. For example, the reception unit provides related templates based on the content of the user's past questions and ideas. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past submission history. This improves user convenience by selecting the optimal reception method based on the user's past history.

[0067] The reception unit can filter questions and ideas based on the user's current project and areas of interest when receiving the questions and ideas. For example, the reception unit can only accept questions and ideas related to the user's current project and areas of interest when receiving the questions and ideas. For example, the reception unit can preferentially accept questions and ideas related to the user's areas of interest. The reception unit can also filter appropriate questions and ideas according to the progress of the user's project. In this way, by filtering based on the user's current project and areas of interest, highly relevant questions and ideas can be preferentially accepted.

[0068] The reception unit can estimate the user's emotions and determine the priority of questions and ideas to be received based on the estimated user emotions. For example, if the user is nervous, the reception unit prioritizes receiving questions and ideas with high importance. The reception unit can estimate the user's emotions and determine the priority of questions and ideas to be received based on the estimated user emotions. For example, if the user is relaxed, the reception unit can receive questions and ideas with normal priority. Furthermore, if the user is in a hurry, the reception unit can also prioritize receiving questions and ideas that require a quick response. This enables more appropriate responses by prioritizing questions and ideas according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] When receiving questions or ideas, the reception unit can prioritize receiving highly relevant questions or ideas by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving questions or ideas related to that area. When receiving questions or ideas, the reception unit can prioritize receiving highly relevant questions or ideas by taking into account the user's geographical location information. For example, when the user is on a business trip, the reception unit prioritizes receiving questions or ideas related to the business trip destination. Furthermore, when the user is at home, the reception unit can also prioritize receiving questions or ideas related to the user's home. In this way, by taking into account the user's geographical location information, highly relevant questions or ideas can be prioritized.

[0070] The reception unit can analyze the user's social media activity when receiving a question or idea and receive related questions or ideas. The reception unit can, for example, receive related questions or ideas based on information shared by the user on social media. The reception unit can analyze the user's social media activity when receiving a question or idea and receive related questions or ideas. For example, the reception unit can analyze the user's social media activity history and preferentially receive related questions or ideas. The reception unit can also receive related questions or ideas based on topics the user is following on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially receive highly relevant questions and ideas.

[0071] The generation unit can estimate the user's emotions and adjust the way answers and suggestions are expressed based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates answers and suggestions using detailed and polite expressions. The generation unit can estimate the user's emotions and adjust the way answers and suggestions are expressed based on the estimated user emotions. For example, if the user is in a hurry, the generation unit generates answers and suggestions that are concise and to the point. Furthermore, if the user is excited, the generation unit can generate answers and suggestions that add visually appealing effects. In this way, by adjusting the way answers and suggestions are expressed based on the user's emotions, more appropriate answers and suggestions are generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0072] The generation unit can adjust the level of detail when generating an answer or a proposal based on the importance of the question or idea. For example, the generation unit generates a detailed answer or proposal for a question or idea with a high level of importance. The generation unit can adjust the level of detail when generating an answer or a proposal based on the importance of the question or idea. For example, the generation unit generates a concise answer or proposal for a question or idea with a low level of importance. The generation unit can also generate answers or proposals with an appropriate level of detail depending on the importance of the question or idea. In this way, by adjusting the level of detail based on the importance of the question or idea, answers or proposals with an appropriate level are generated.

[0073] The generation unit can apply different generation algorithms depending on the category of the question or idea when generating an answer or proposal. For example, the generation unit applies a specialized technical generation algorithm to a technical question. The generation unit can apply different generation algorithms depending on the category of the question or idea when generating an answer or proposal. For example, the generation unit can apply a specialized business generation algorithm to a business-related idea. The generation unit can also apply a specialized creative generation algorithm to a creative idea. In this way, by applying different generation algorithms depending on the category of the question or idea, more appropriate answers and proposals can be generated.

[0074] The generation unit can estimate the user's emotions and adjust the length of answers or suggestions based on the estimated user emotions. For example, if the user is in a hurry, the generation unit generates short and to-the-point answers or suggestions. The generation unit can estimate the user's emotions and adjust the length of answers or suggestions based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate longer answers or suggestions with detailed explanations. Furthermore, if the user is excited, the generation unit can generate answers or suggestions with visually stimulating effects. In this way, by adjusting the length of answers or suggestions according to the user's emotions, more appropriate answers or suggestions are generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] The generation unit can determine the priority based on the time of submission of the question or idea when generating answers or proposals. For example, the generation unit generates answers or proposals by prioritizing recently submitted questions or ideas. The generation unit can determine the priority based on the time of submission of the question or idea when generating answers or proposals. For example, the generation unit generates answers or proposals for questions or ideas that were submitted earlier at a later date. The generation unit can also generate answers or proposals with appropriate priority based on the time of submission. In this way, by determining the priority based on the time of submission of the question or idea, answers or proposals are generated at an appropriate time.

[0076] The generation unit can adjust the order based on the relevance of the questions and ideas when generating answers and proposals. For example, the generation unit generates answers and proposals with priority to questions and ideas with high relevance. The generation unit can adjust the order based on the relevance of the questions and ideas when generating answers and proposals. For example, the generation unit generates answers and proposals later for questions and ideas with low relevance. The generation unit can also generate answers and proposals in an appropriate order depending on the relevance of the questions and ideas. In this way, by adjusting the order based on the relevance of the questions and ideas, highly relevant answers and proposals are generated with priority.

[0077] The identification unit can estimate the user's emotions and determine the priority of company personnel to be identified based on the estimated user emotions. For example, if the user is nervous, the identification unit prioritizes identifying friendly company personnel. The identification unit can estimate the user's emotions and determine the priority of company personnel to be identified based on the estimated user emotions. For example, if the user is relaxed, the identification unit prioritizes identifying company personnel with specialized knowledge. Furthermore, if the user is in a hurry, the identification unit can prioritize identifying company personnel who can respond quickly. In this way, by prioritizing company personnel according to the user's emotions, more appropriate company personnel can be identified. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] When identifying internal stakeholders, the identification department can select the most suitable stakeholders by referring to their past project participation histories. For example, the identification department preferentially identifies internal stakeholders who have participated in similar projects in the past. When identifying internal stakeholders, the identification department can select the most suitable stakeholders by referring to their past project participation histories. For example, the identification department preferentially identifies internal stakeholders with a high project success rate. The identification department can also preferentially identify internal stakeholders who have received high evaluations in past projects. In this way, the most suitable stakeholders are identified by referring to their past project participation histories.

[0079] When identifying internal stakeholders, the identification department can perform the identification based on the stakeholders' fields of expertise and skill sets. For example, the identification department identifies internal stakeholders who have the specialized knowledge required for the project. When identifying internal stakeholders, the identification department can perform the identification based on the stakeholders' fields of expertise and skill sets. For example, the identification department preferentially identifies internal stakeholders who have a specific skill set. The identification department can also identify internal stakeholders who have the optimal skill set according to the requirements of the project. In this way, by performing the identification based on the stakeholders' fields of expertise and skill sets, the optimal stakeholders for the project are identified.

[0080] The identification unit can estimate the user's emotions and adjust the display method of the identified company associates based on the estimated user emotions. For example, if the user is nervous, the identification unit displays a friendly profile picture. The identification unit can estimate the user's emotions and adjust the display method of the identified company associates based on the estimated user emotions. For example, if the user is relaxed, the identification unit displays detailed profile information. Also, if the user is in a hurry, the identification unit can display concise profile information. In this way, by adjusting the display method of the company associates according to the user's emotions, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] When identifying internal stakeholders, the identification unit can select the most appropriate stakeholders by taking into account the geographic location information of the stakeholders. For example, the identification unit preferentially identifies internal stakeholders who are close to the user. When identifying internal stakeholders, the identification unit can select the most appropriate stakeholders by taking into account the geographic location information of the stakeholders. For example, in the case of a project related to a specific region, the identification unit preferentially identifies internal stakeholders who are in that region. Furthermore, when the user is on a business trip, the identification unit can also preferentially identify internal stakeholders who are at the business trip destination. In this way, the most appropriate stakeholders are identified by taking into account the geographic location information of the stakeholders.

[0082] When identifying internal stakeholders, the identification department can analyze the social media activities of the stakeholders to improve the accuracy of identification. For example, the identification department analyzes the social media activity history of the stakeholders to identify stakeholders suitable for related projects. When identifying internal stakeholders, the identification department can analyze the social media activities of the stakeholders to improve the accuracy of identification. For example, the identification department can improve the accuracy of identification based on information shared by the stakeholders on social media. The identification department can also improve the accuracy of identification by taking into account the number of followers and influence of the stakeholders on social media. In this way, the accuracy of identification is improved by analyzing the social media activities of the stakeholders.

[0083] The feedback unit can estimate the user's emotions and adjust the feedback acceptance method based on the estimated user's emotions. For example, if the user is nervous, the feedback unit provides a simple feedback form. The feedback unit can estimate the user's emotions and adjust the feedback acceptance method based on the estimated user's emotions. For example, if the user is relaxed, the feedback unit provides a detailed feedback form. Furthermore, if the user is in a hurry, the feedback unit can also preferentially accept voice feedback. In this way, by adjusting the feedback acceptance method according to the user's emotions, more appropriate feedback can be obtained. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] When receiving feedback, the feedback unit can select the optimal reception method by referring to past feedback history. For example, the feedback unit preferentially suggests feedback methods (text, voice, etc.) that the user has used in the past. When receiving feedback, the feedback unit can select the optimal reception method by referring to past feedback history. For example, the feedback unit provides a related template based on the content of the user's past feedback. The feedback unit can also suggest the optimal reception method for a specific time period from the user's past feedback history. In this way, the optimal reception method is selected by referring to the past feedback history.

[0085] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. For example, if the user is nervous, the feedback unit prioritizes receiving feedback with a high level of importance. The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. For example, if the user is relaxed, the feedback unit can receive feedback with a normal priority. Also, if the user is in a hurry, the feedback unit can prioritize receiving feedback that requires a quick response. In this way, by determining the priority of feedback according to the user's emotions, more important feedback is processed preferentially. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] The feedback unit may select an optimal feedback reception method in consideration of device information of the user when receiving feedback. For example, if the user is using a smartphone, the feedback unit may provide a feedback form optimized for mobile devices. The feedback unit may select an optimal feedback reception method in consideration of device information of the user when receiving feedback. For example, if the user is using a tablet, the feedback unit may provide a feedback form optimized for a large screen. Furthermore, if the user is using a desktop computer, the feedback unit may provide a detailed feedback form. In this way, the optimal feedback reception method is selected by taking into consideration device information of the user.

[0087] The communication unit can estimate the user's emotions and adjust the contact method based on the estimated user's emotions. For example, if the user is nervous, the communication unit prioritizes contact by email. The communication unit can estimate the user's emotions and adjust the contact method based on the estimated user's emotions. For example, if the user is relaxed, the communication unit suggests contact by phone. Also, if the user is in a hurry, the communication unit can prioritize contact by chat. This allows for more appropriate contact by adjusting the contact method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0088] When making contact, the contact unit can select the optimal contact method by referring to past contact history. For example, the contact unit preferentially suggests contact methods (email, telephone, etc.) that the user has used in the past. When making contact, the contact unit can select the optimal contact method by referring to past contact history. For example, the contact unit provides a related contact method based on the content of the user's past contact. The contact unit can also suggest the optimal contact method for a specific time period from the user's past contact history. In this way, the optimal contact method is selected by referring to the past contact history.

[0089] The communication unit can estimate the user's emotions and determine the priority of communications based on the estimated user emotions. For example, if the user is nervous, the communication unit prioritizes important communications. The communication unit can estimate the user's emotions and determine the priority of communications based on the estimated user emotions. For example, if the user is relaxed, the communication unit can prioritize communications with normal priority. Furthermore, if the user is in a hurry, the communication unit can also prioritize communications that require a quick response. In this way, by determining the priority of communications according to the user's emotions, more important communications are prioritized. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] The contact unit can select the optimal contact method by taking into account device information of the user when making contact. For example, if the user is using a smartphone, the contact unit provides a contact method optimized for mobile devices. The contact unit can select the optimal contact method by taking into account device information of the user when making contact. For example, if the user is using a tablet, the contact unit provides a contact method optimized for a large screen. Furthermore, if the user is using a desktop, the contact unit can also provide detailed contact methods. In this way, the optimal contact method is selected by taking into account device information of the user. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and identification unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives questions and ideas from a user. The generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and generates appropriate answers and suggestions using a generation AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies relevant internal parties based on the generated answers and suggestions. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and identification unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives questions and ideas from a user. The generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and generates appropriate answers and suggestions using a generation AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies relevant internal parties based on the generated answers and suggestions. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and identification unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives questions and ideas from a user. The generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and generates appropriate answers and suggestions using a generation AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies relevant internal parties based on the generated answers and suggestions. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and identification unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives questions and ideas from a user. The generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and generates appropriate answers and suggestions using a generation AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies relevant internal parties based on the generated answers and suggestions.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The reception unit can analyze the user's past question and idea submission history and customize the method for receiving questions and ideas based on the user's preferences and tendencies. For example, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. Also, if the user has previously submitted questions or ideas during a specific time period, the reception unit can send notifications during that time period. This provides a more personalized reception method based on the user's past behavioral patterns.

[0093] The Identification Department monitors the progress of a user's current project in real time and can identify the most appropriate internal stakeholders depending on the project's phase. For example, in the early stages of a project, it can identify employees who are good at planning and generating ideas, and in the middle stages, it can identify employees who are well-versed in resource management. It can also identify employees who are good at quality control and delivery date management in the final stages of a project. This allows it to provide the most appropriate support depending on the project's progress.

[0094] The feedback section can analyze user-provided feedback in real time and immediately reflect it in the generation AI. For example, if a user provides feedback that the generation AI's answer "lacks specific examples," the generation AI will learn to include more specific examples in its next answer. Also, if a user provides feedback that "the answer is too long," the generation AI can adjust its next answer to aim for concise expression. This allows user feedback to be reflected quickly, improving the accuracy of the system.

[0095] The communication unit can automatically record the contact history when the user contacts the introduced parties, so that it can be referenced the next time the user contacts them. For example, if the user contacts them by email, the content and date and time of the email are recorded and displayed the next time the user contacts them. The communication unit can also record the contact history via chat or telephone in the same way, so that the user can easily check past interactions. This allows for smooth communication by referring to the contact history.

[0096] The reception unit can estimate the user's emotions and customize the method for receiving questions and ideas based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can receive questions and ideas while playing relaxing music. Alternatively, if the user is excited, the reception unit can receive questions and ideas using interactive animation. This provides the optimal reception method according to the user's emotions, improving the user experience.

[0097] The reception unit can analyze the user's past question and idea submission history and customize the method for receiving questions and ideas based on the user's preferences and tendencies. For example, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. Also, if the user has previously submitted questions or ideas during a specific time period, the reception unit can send notifications during that time period. This provides a more personalized reception method based on the user's past behavioral patterns.

[0098] When receiving questions or ideas, the reception unit can filter them based on the user's current project or area of ​​interest. For example, the reception unit can only accept questions or ideas related to the user's current project. When receiving questions or ideas, the reception unit can filter them based on the user's current project or area of ​​interest. For example, the reception unit can preferentially accept questions or ideas related to the user's area of ​​interest. The reception unit can also filter appropriate questions or ideas according to the progress of the user's project. In this way, by filtering based on the user's current project or area of ​​interest, highly relevant questions or ideas can be preferentially accepted.

[0099] The reception unit can estimate the user's emotions and determine the priority of questions and ideas to be received based on the estimated user emotions. For example, if the user is nervous, it prioritizes questions and ideas with high importance. The reception unit can estimate the user's emotions and determine the priority of questions and ideas to be received based on the estimated user emotions. For example, if the user is relaxed, the reception unit can accept questions and ideas with normal priority. Furthermore, if the user is in a hurry, the reception unit can also prioritize questions and ideas that require a quick response. This enables more appropriate responses by prioritizing questions and ideas according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0100] When receiving questions or ideas, the reception unit can prioritize receiving highly relevant questions or ideas by taking into account the user's geographical location information. For example, if the user is in a specific area, questions or ideas related to that area are prioritized. When receiving questions or ideas, the reception unit can prioritize receiving highly relevant questions or ideas by taking into account the user's geographical location information. For example, if the user is on a business trip, the reception unit can prioritize receiving questions or ideas related to the business trip destination. Furthermore, if the user is at home, the reception unit can also prioritize receiving questions or ideas related to the user's home. In this way, by taking into account the user's geographical location information, highly relevant questions or ideas can be prioritized.

[0101] The reception unit can analyze the user's social media activity when receiving a question or idea and receive related questions or ideas. For example, the reception unit can receive related questions or ideas based on information shared by the user on social media. The reception unit can analyze the user's social media activity when receiving a question or idea and receive related questions or ideas. For example, the reception unit can analyze the user's social media activity history and preferentially receive related questions or ideas. The reception unit can also receive related questions or ideas based on topics the user is following on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially receive highly relevant questions or ideas.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The reception unit accepts questions and ideas from users. For example, a user can input a question such as, "I'm thinking of an idea for a new project. How should I go about it?" The reception unit inputs the user's question or idea into the generation AI. Step 2: The generation unit uses a generation AI to analyze the questions and ideas received by the reception unit and generate appropriate answers and proposals. For example, the generation AI might generate an answer such as, "When it comes to progressing with a project, it's important to first clarify the goal, then identify the necessary resources, and finally create a schedule." The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to analyze the content of the questions and ideas and generate appropriate answers and proposals. Step 3: The Identification Department identifies relevant internal stakeholders based on the answers and suggestions generated by the Generation Department. For example, the Identification Department identifies employees who are knowledgeable about project management or resource management.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0127] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0148] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0160] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0165] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0166] 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.

[0167] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0168] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0169] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0170] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives questions and ideas from users; a generation unit that analyzes the questions and ideas received by the reception unit and generates answers and suggestions; an identification unit that identifies relevant company personnel based on the answers and suggestions generated by the generation unit; A system characterized by:

2. Equipped with an identification unit that identifies relevant internal stakeholders based on the answers and suggestions generated by the generation AI 2. The system of claim 1.

3. The system includes a feedback section where the user provides feedback on the answers of the generated AI, and the generated AI learns from the feedback.

2. The system of claim 1.

4. A contact section is provided that allows users to contact the introduced parties.

2. The system of claim 1.

5. The reception unit Estimate user emotions and adjust the timing of accepting questions and ideas based on the estimated user emotions 2. The system of claim 1.

6. The reception unit Analyze users' past question and idea submission history and select the appropriate reception method 2. The system of claim 1.

7. The reception unit Filter your questions and ideas based on your current projects and interests 2. The system of claim 1.

8. The reception unit Estimate user emotions and prioritize accepted questions and ideas based on the estimated user emotions 2. The system of claim 1.

9. The reception unit When accepting questions or ideas, the system takes into account the user's geographic location information to prioritize relevant questions and ideas.

2. The system of claim 1.

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