System

Abstract

An object of a system according to an embodiment is to select an optimal generation and AI service based on a user's request and provide an appropriate answer.SOLUTION: A system includes an analysis unit, a selection unit, and a provision unit. The analysis unit analyzes a user's request. The selection unit selects an appropriate generation and AI service based on the information analyzed by the analysis unit. The providing unit provides the answer generated by the generation and AI service selected by the selection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to select the most suitable generative AI service for a user's request from the many available services and obtain an appropriate answer.

[0005] The system according to the embodiment aims to select the most suitable generative AI service based on the user's request and provide an appropriate answer. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a selection unit, and a provision unit. The analysis unit analyzes a user's request. The selection unit selects an appropriate generation AI service based on the information analyzed by the analysis unit. The provision unit provides an answer generated by the generation AI service selected by the selection unit. [Effects of the Invention]

[0007] The system according to the embodiment can select the most suitable generative AI service based on the user's request and provide an appropriate answer. [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 multimodal generative AI service according to an embodiment of the present invention is a system that analyzes a user's request, selects an optimal generative AI service, and provides an answer. In a multimodal generative AI service, a user inputs a question or request, and the system analyzes the input and selects an optimal generative AI service. The selected generative AI service generates an answer based on the user's request and provides the answer to the user. For example, a user inputs a specific request such as, "Please give me some ideas for a business presentation." This input is sent to the system. The system then analyzes the input question or request. The system uses natural language processing technology to understand the user's intent and selects the optimal generative AI service. For example, in response to a request for ideas for a business presentation, the system selects a generative AI service specialized in creating presentations. The selected generative AI service generates an answer based on the user's request. For example, a generative AI service specialized in creating presentations generates specific presentation ideas based on the user's request. The generated answer is sent to the system. Finally, the system provides the generated answer to the user. The user can receive and use the answer provided by the system. For example, the user can create a business presentation based on the generated presentation ideas. This allows the multimodal generative AI service to analyze the user's request, select the optimal generative AI service, and provide an answer. This allows the user to use the optimal generative AI service from among many available, and obtain the answer they expect. Furthermore, because the system analyzes the user's request and selects the optimal generative AI service, the user does not need to know in detail about the types of generative AI services or how to use them. This makes generative AI services easier to use, promoting their use in both business and personal settings.

[0029] A multimodal generative AI service according to an embodiment includes an analysis unit, a selection unit, and a provision unit. The analysis unit analyzes a user's request. The user's request includes, but is not limited to, a question, a request, and feedback. The analysis unit understands the user's intent using, for example, natural language processing technology. The analysis unit can also analyze the user's request using data mining technology. The analysis unit can also analyze the user's request using a machine learning algorithm. For example, the analysis unit analyzes the user's question using natural language processing technology to understand the intent. The analysis unit can also analyze the user's request and extract relevant information using data mining technology. The analysis unit can also analyze the user's feedback using a machine learning algorithm to select an optimal analysis method. The selection unit selects an appropriate generative AI service based on the information analyzed by the analysis unit. The appropriate generative AI service includes, but is not limited to, text generation, image generation, and voice generation. The selection unit selects, for example, a text generation service. The selection unit can also select an image generation service. The selection unit can also select a voice generation service. For example, the selection unit selects a text generation service based on the information analyzed by the analysis unit. The selection unit can also select an image generation service based on the information analyzed by the analysis unit. The selection unit can also select a voice generation service based on the information analyzed by the analysis unit. The provision unit provides the user with an answer generated by the generation AI service selected by the selection unit. The provision can include, but is not limited to, text format, audio format, visual format, etc. The provision unit can provide the answer in, for example, text format. The provision unit can also provide the answer in audio format. The provision unit can also provide the answer in visual format. For example, the provision unit provides the user with a text format answer generated by the generation AI service selected by the selection unit. The provision unit can also provide the user with an audio format answer generated by the generation AI service selected by the selection unit.In addition, the providing unit can provide the user with a visual answer generated by the generation AI service selected by the selection unit. This allows the multimodal generation AI service according to the embodiment to analyze the user's request, select the optimal generation AI service, and provide an answer.

[0030] The analysis unit can understand the user's intention using natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis, for example. The analysis unit can analyze the user's question using morphological analysis. The analysis unit can also analyze the user's request using grammatical analysis. The analysis unit can also analyze the user's feedback using semantic analysis. For example, the analysis unit can extract words from the user's question using morphological analysis and understand their meaning. The analysis unit can also analyze the grammatical structure of the user's request using grammatical analysis and understand its intention. The analysis unit can also analyze the content of the user's feedback using semantic analysis and understand its intention. As a result, the use of natural language processing technology can accurately understand the user's intention.

[0031] The selection unit can select an appropriate generation AI service based on the information analyzed by the analysis unit. Appropriate generation AI services include, but are not limited to, text generation, image generation, and voice generation, for example. The selection unit selects, for example, a text generation service. The selection unit can also select an image generation service. The selection unit can also select a voice generation service. For example, the selection unit selects a text generation service based on the information analyzed by the analysis unit. The selection unit can also select an image generation service based on the information analyzed by the analysis unit. The selection unit can also select a voice generation service based on the information analyzed by the analysis unit. In this way, by selecting the optimal generation AI service based on the analyzed information, it is possible to provide an optimal answer to a user's request.

[0032] The providing unit can provide the user with an answer generated by the generation AI service selected by the selecting unit. Examples of formats for providing include, but are not limited to, text, audio, and visual formats. The providing unit can provide the answer, for example, in text format. The providing unit can also provide the answer in audio format. The providing unit can also provide the answer in visual format. For example, the providing unit can provide the user with a text format answer generated by the generation AI service selected by the selecting unit. The providing unit can also provide the user with an audio format answer generated by the generation AI service selected by the selecting unit. The providing unit can also provide the user with a visual format answer generated by the generation AI service selected by the selecting unit. In this way, by providing the user with an answer generated by the selected generation AI service, the user can obtain the answer they expected.

[0033] The analysis unit can analyze the user's past question history and select the optimal analysis method. For example, the analysis unit analyzes patterns of questions frequently asked by the user in the past and selects the optimal analysis method. Furthermore, if the user's past question history shows that there are many questions about a specific field, the analysis unit can select an analysis method specialized for that field. Furthermore, the analysis unit can select the most effective analysis method based on the user's past question history. For example, the analysis unit retrieves the user's past question history from a database and analyzes patterns of frequently asked questions. Furthermore, the analysis unit can analyze the user's past question history using log analysis technology and, if there are many questions about a specific field, select an analysis method specialized for that field. Furthermore, the analysis unit can use a machine learning algorithm to select the most effective analysis method based on the user's past question history. In this way, the optimal analysis method can be selected by analyzing the past question history.

[0034] When analyzing questions or requests, the analysis unit can filter based on the user's current project or area of ​​interest. For example, the analysis unit prioritizes analyzing questions related to the user's current project. The analysis unit can also prioritize analyzing related information based on the user's area of ​​interest. The analysis unit can also select the optimal analysis method depending on the progress of the user's project. For example, the analysis unit can use data from a project management tool to identify the user's current project and prioritize analyzing questions related to that project. The analysis unit can also identify an area of ​​interest based on information input by the user and prioritize analyzing information related to that area. The analysis unit can also use data from the project management tool to select the optimal analysis method depending on the progress of the user's project. This makes it possible to provide highly relevant information by filtering based on the user's current project or area of ​​interest.

[0035] When analyzing a question or request, the analysis unit can select an appropriate analysis means depending on the user's input method. For example, when the user inputs a question by voice, the analysis unit performs analysis using voice recognition technology. Furthermore, when the user inputs a question in text, the analysis unit can also perform analysis using natural language processing technology. Furthermore, when the user inputs an image, the analysis unit can also perform analysis using image recognition technology. For example, the analysis unit converts the user's voice input into text using voice recognition technology and performs analysis based on that text. Furthermore, the analysis unit can analyze the user's text input using natural language processing technology and understand its intent. Furthermore, the analysis unit can analyze the user's image input using image recognition technology and understand its content. This improves the accuracy of the analysis by selecting the optimal analysis means depending on the user's input method.

[0036] When analyzing a question or request, the analysis unit can prioritize analyzing highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the analysis unit prioritizes analyzing information related to that area. The analysis unit can also select the optimal analysis method based on the user's current location. The analysis unit can also prioritize analyzing highly relevant information based on the user's geographical location information. For example, the analysis unit can identify the user's current location using GPS data and prioritize analyzing information related to that area. The analysis unit can also identify the user's location using an IP address and prioritize analyzing information related to that area. The analysis unit can also prioritize analyzing geographically close information based on the user's geographical location information in order to prioritize analyzing highly relevant information. This allows highly relevant information to be provided preferentially by taking the user's geographical location information into account.

[0037] When analyzing a question or request, the analysis unit can analyze the user's social media activity and analyze related information. For example, the analysis unit analyzes the content posted by the user on social media and analyzes the related information. The analysis unit can also analyze the related information by referring to the activities of the user's friends on social media. The analysis unit can also analyze the related information based on the user's check-in information on social media. For example, the analysis unit analyzes the content posted by the user on social media and extracts related information. The analysis unit can also analyze the activities of the user's friends on social media and extract related information. The analysis unit can also analyze the user's check-in information on social media and extract related information. In this way, highly relevant information can be provided by analyzing the user's social media activity.

[0038] When analyzing a question or request, the analysis unit can customize the analysis method by reflecting the user's past feedback. The analysis unit customizes the analysis method, for example, based on feedback provided by the user in the past. The analysis unit can also select the optimal analysis method from the user's past feedback. The analysis unit can also improve the accuracy of the analysis by reflecting the user's feedback. For example, the analysis unit obtains the user's past feedback from a database and customizes the analysis method. The analysis unit can also select the optimal analysis method based on the user's past feedback. The analysis unit can also use a machine learning algorithm to reflect the user's feedback and improve the accuracy of the analysis. In this way, the analysis accuracy is improved by reflecting the user's past feedback.

[0039] When making a selection, the selection unit can improve the accuracy of the selection by taking into account the interrelationships between the generation AI services. For example, the selection unit analyzes the interrelationships between the generation AI services and selects the optimal combination. The selection unit can also select the most effective service by taking into account the interrelationships between the generation AI services. The selection unit can also improve the accuracy of the selection based on the interrelationships between the generation AI services. For example, the selection unit analyzes the dependencies between the generation AI services and selects the optimal combination. The selection unit can also select the most effective service by taking into account the interactions between the generation AI services. The selection unit can also adjust the algorithm to improve the accuracy of the selection based on the interrelationships between the generation AI services. In this way, the accuracy of the selection is improved by taking into account the interrelationships between the generation AI services.

[0040] When selecting a service, the selection unit can make the selection taking into consideration attribute information of the provider of the generation AI service. For example, the selection unit makes the selection taking into consideration the specialty of the provider of the generation AI service. The selection unit can also make the selection based on the past performance of the provider of the generation AI service. The selection unit can also select the optimal service taking into consideration attribute information of the provider of the generation AI service. For example, the selection unit selects the optimal service taking into consideration the specialty of the provider of the generation AI service. The selection unit can also select the optimal service based on the past performance of the provider of the generation AI service. The selection unit can also use a database to select the optimal service taking into consideration attribute information of the provider of the generation AI service. In this way, the optimal service can be selected by taking into consideration attribute information of the provider of the generation AI service.

[0041] When selecting a service, the selection unit can weight the selection based on the frequency of provision of the generation AI service. For example, if the frequency of provision of a generation AI service is high, the selection unit will preferentially select that service. The selection unit can also weight the selection based on the frequency of provision of the generation AI service. The selection unit can also select the optimal service, taking into account the frequency of provision of the generation AI service. For example, if the frequency of provision of a generation AI service is high, the selection unit will preferentially select that service. The selection unit can also weight the selection based on the frequency of provision of the generation AI service. The selection unit can also use a database to select the optimal service, taking into account the frequency of provision of the generation AI service. In this way, the optimal service can be selected by weighting based on the frequency of provision of the generation AI service.

[0042] When selecting a service, the selection unit can take into consideration the geographical distribution of the generation AI service. For example, the selection unit selects the optimal service by taking into consideration the area where the generation AI service is provided. The selection unit can also improve the accuracy of the selection based on the geographical distribution of the generation AI service. The selection unit can also select the optimal service based on the area where the generation AI service is provided. For example, the selection unit selects the optimal service by taking into consideration the area where the generation AI service is provided. The selection unit can also improve the accuracy of the selection based on the geographical distribution of the generation AI service. The selection unit can also use a database to select the optimal service based on the area where the generation AI service is provided. This allows the optimal service to be selected by taking into consideration the geographical distribution of the generation AI service.

[0043] When making a selection, the selection unit can improve the accuracy of the selection by referring to literature related to the generation AI service. For example, the selection unit refers to literature related to the generation AI service to select the optimal service. The selection unit can also improve the accuracy of the selection based on literature related to the generation AI service. The selection unit can also improve the accuracy of the selection by referring to literature related to the generation AI service. For example, the selection unit refers to literature related to the generation AI service to select the optimal service. The selection unit can also improve the accuracy of the selection based on literature related to the generation AI service. The selection unit can also use a database to select the optimal service by referring to literature related to the generation AI service. In this way, the accuracy of the selection is improved by referring to related literature.

[0044] When making a selection, the selection unit can take into consideration the market value of the generation AI service. For example, the selection unit selects the optimal service based on the market value of the generation AI service. The selection unit can also improve the accuracy of the selection by taking into consideration the market value of the generation AI service. The selection unit can also select the optimal service based on the market value of the generation AI service. For example, the selection unit selects the optimal service based on the market value of the generation AI service. The selection unit can also improve the accuracy of the selection by taking into consideration the market value of the generation AI service. The selection unit can also use a database to select the optimal service based on the market value of the generation AI service. This makes it possible to select the optimal service by taking market value into consideration.

[0045] The providing unit can adjust the level of detail of the information to be provided based on the importance of the generated answer when providing the answer. For example, the providing unit provides an answer with high importance in a form including detailed information. The providing unit can also provide an answer with low importance in a concise form. The providing unit can also adjust the level of detail of the information to be provided according to the importance. For example, the providing unit provides an answer with high importance in a form including detailed information. The providing unit can also provide an answer with low importance in a concise form. The providing unit can also use an algorithm to adjust the level of detail of the information to be provided according to the importance. In this way, by adjusting the level of detail of the information to be provided based on the importance of the answer, it is possible to provide optimal information for the user.

[0046] The providing unit can apply different providing algorithms depending on the category of the generated answer when providing the answer. For example, the providing unit can apply a providing algorithm including detailed data and analysis to business-related answers. The providing unit can also apply a concise and easy-to-understand providing algorithm to private-related answers. The providing unit can also select an optimal providing algorithm depending on the category. For example, the providing unit can apply a providing algorithm including detailed data and analysis to business-related answers. The providing unit can also apply a concise and easy-to-understand providing algorithm to private-related answers. The providing unit can also use a database to select an optimal providing algorithm depending on the category. In this way, the optimal providing algorithm can be applied depending on the category of the answer, thereby providing the optimal answer for the user.

[0047] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the answer. For example, the providing unit selects the optimal provision method based on answers provided by the user in the past. The providing unit can also analyze the user's past provision results and improve the accuracy of the provision. The providing unit can also improve the accuracy of the provision by reflecting user feedback. For example, the providing unit selects the optimal provision method based on answers provided by the user in the past. The providing unit can also analyze the user's past provision results and improve the accuracy of the provision. The providing unit can also use a machine learning algorithm to improve the accuracy of the provision by reflecting user feedback. In this way, the accuracy of the provision is improved by referring to the user's past provision results.

[0048] The providing unit can determine the priority of provision based on the submission time of the generated answers when providing the answers. For example, the providing unit provides the most recently generated answer with priority. The providing unit can also regenerate and provide answers that were submitted earlier as needed. The providing unit can also determine an optimal provision order based on the submission time. For example, the providing unit provides the most recently generated answer with priority. The providing unit can also regenerate and provide answers that were submitted earlier as needed. The providing unit can also use a database to determine the optimal provision order based on the submission time. In this way, by determining the priority of provision based on the submission time of the answers, the latest information can be provided with priority.

[0049] The providing unit can adjust the order of providing the generated answers based on the relevance of the answers when providing them. For example, the providing unit can provide the most relevant answer first. The providing unit can also postpone less relevant answers. The providing unit can also adjust the order of providing the answers based on the relevance. For example, the providing unit can provide the most relevant answer first. The providing unit can also postpone less relevant answers. The providing unit can also use an algorithm to adjust the order of providing the answers based on the relevance. In this way, by adjusting the order of providing the answers based on the relevance of the answers, it is possible to preferentially provide information that is most relevant to the user.

[0050] The providing unit can adjust the use of technical terminology when providing the answer according to the user's level of expertise. For example, if the user has technical expertise, the providing unit can provide an answer that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can also explain in simple terms. Furthermore, the providing unit can select an optimal expression method according to the user's level of expertise. For example, if the user has technical expertise, the providing unit can provide an answer that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can also explain in simple terms. Furthermore, the providing unit can use a database to select an optimal expression method according to the user's level of expertise. In this way, by adjusting the use of technical terminology according to the user's level of expertise, an answer that is easy for the user to understand can be provided.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The analysis unit can take into account the user's past behavioral history when analyzing a user's request. For example, it analyzes what questions the user has asked in the past and what answers they have received, and selects the optimal analysis method for the current request. The analysis unit can also understand the user's preferences and tendencies based on the user's past behavioral history, and perform more personalized analysis. Furthermore, the analysis unit can use the user's past behavioral history to predict future requests and prepare in advance. This allows for more accurate analysis by taking the user's past behavioral history into consideration.

[0053] When providing an answer generated by the generation AI service selected by the selection unit to a user, the provision unit can adjust the provision method according to the user's current situation. For example, if the user is on the move, the provision unit can provide the answer in audio format. Also, if the user is in a desktop environment, the provision unit can provide a detailed answer in text format. Furthermore, if the user is in a meeting, the provision unit can provide a concise answer in visual format. In this way, by selecting the optimal provision method according to the user's current situation, it is possible to provide an answer that is easy for the user to use.

[0054] When analyzing a user's request, the analysis unit can analyze the user's social media activity and analyze related information. For example, the analysis unit can analyze the content of the user's posts on social media and analyze related information. The analysis unit can also analyze related information by referring to the activities of the user's friends on social media. Furthermore, the analysis unit can analyze related information based on the user's check-in information on social media. In this way, highly relevant information can be provided by analyzing the user's social media activity.

[0055] The selection unit can take into account the attribute information of the provider of the generation AI service when selecting an appropriate generation AI service based on the information analyzed by the analysis unit. For example, the selection can be made taking into account the specialty of the provider of the generation AI service. The selection can also be made based on the past performance of the provider of the generation AI service. Furthermore, the attribute information of the provider of the generation AI service can be taken into account to select the optimal service. In this way, the attribute information of the provider of the generation AI service can be taken into account to select the optimal service.

[0056] When analyzing a user's request, the analysis unit can prioritize analysis of highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, information related to that area is prioritized for analysis. The analysis unit can also select the optimal analysis method based on the user's current location. Furthermore, it can also prioritize analysis of highly relevant information based on the user's geographical location information. This makes it possible to provide highly relevant information with priority by taking into account the user's geographical location information.

[0057] The selection unit can take into account the market value of the generation AI service when selecting an appropriate generation AI service based on the information analyzed by the analysis unit. For example, the selection unit selects the optimal service based on the market value of the generation AI service. The selection unit can also improve the accuracy of the selection by taking into account the market value of the generation AI service. Furthermore, the selection unit can also select the optimal service based on the market value of the generation AI service. This allows the optimal service to be selected by taking market value into account.

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

[0059] Step 1: The analysis unit analyzes the user's request. User requests include questions, requests, and feedback. The analysis unit uses natural language processing technology, data mining technology, and machine learning algorithms to understand the user's intent and analyze the request. Step 2: The selection unit selects an appropriate generative AI service based on the information analyzed by the analysis unit. The appropriate generative AI service may include text generation, image generation, speech generation, etc. Step 3: The providing unit provides the answer generated by the generation AI service selected by the selecting unit to the user in a text format, audio format, visual format, or the like.

[0060] (Example 2) A multimodal generative AI service according to an embodiment of the present invention is a system that analyzes a user's request, selects an optimal generative AI service, and provides an answer. In a multimodal generative AI service, a user inputs a question or request, and the system analyzes the input and selects an optimal generative AI service. The selected generative AI service generates an answer based on the user's request and provides the answer to the user. For example, a user inputs a specific request such as, "Please give me some ideas for a business presentation." This input is sent to the system. The system then analyzes the input question or request. The system uses natural language processing technology to understand the user's intent and selects the optimal generative AI service. For example, in response to a request for ideas for a business presentation, the system selects a generative AI service specialized in creating presentations. The selected generative AI service generates an answer based on the user's request. For example, a generative AI service specialized in creating presentations generates specific presentation ideas based on the user's request. The generated answer is sent to the system. Finally, the system provides the generated answer to the user. The user can receive and use the answer provided by the system. For example, the user can create a business presentation based on the generated presentation ideas. This allows the multimodal generative AI service to analyze the user's request, select the optimal generative AI service, and provide an answer. This allows the user to use the optimal generative AI service from among many available, and obtain the answer they expect. Furthermore, because the system analyzes the user's request and selects the optimal generative AI service, the user does not need to know in detail about the types of generative AI services or how to use them. This makes generative AI services easier to use, promoting their use in both business and personal settings.

[0061] A multimodal generative AI service according to an embodiment includes an analysis unit, a selection unit, and a provision unit. The analysis unit analyzes a user's request. The user's request includes, but is not limited to, a question, a request, and feedback. The analysis unit understands the user's intent using, for example, natural language processing technology. The analysis unit can also analyze the user's request using data mining technology. The analysis unit can also analyze the user's request using a machine learning algorithm. For example, the analysis unit analyzes the user's question using natural language processing technology to understand the intent. The analysis unit can also analyze the user's request and extract relevant information using data mining technology. The analysis unit can also analyze the user's feedback using a machine learning algorithm to select an optimal analysis method. The selection unit selects an appropriate generative AI service based on the information analyzed by the analysis unit. The appropriate generative AI service includes, but is not limited to, text generation, image generation, and voice generation. The selection unit selects, for example, a text generation service. The selection unit can also select an image generation service. The selection unit can also select a voice generation service. For example, the selection unit selects a text generation service based on the information analyzed by the analysis unit. The selection unit can also select an image generation service based on the information analyzed by the analysis unit. The selection unit can also select a voice generation service based on the information analyzed by the analysis unit. The provision unit provides the user with an answer generated by the generation AI service selected by the selection unit. The provision can include, but is not limited to, text format, audio format, visual format, etc. The provision unit can provide the answer in, for example, text format. The provision unit can also provide the answer in audio format. The provision unit can also provide the answer in visual format. For example, the provision unit provides the user with a text format answer generated by the generation AI service selected by the selection unit. The provision unit can also provide the user with an audio format answer generated by the generation AI service selected by the selection unit.In addition, the providing unit can provide the user with a visual answer generated by the generation AI service selected by the selection unit. This allows the multimodal generation AI service according to the embodiment to analyze the user's request, select the optimal generation AI service, and provide an answer.

[0062] The analysis unit can understand the user's intention using natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis, for example. The analysis unit can analyze the user's question using morphological analysis. The analysis unit can also analyze the user's request using grammatical analysis. The analysis unit can also analyze the user's feedback using semantic analysis. For example, the analysis unit can extract words from the user's question using morphological analysis and understand their meaning. The analysis unit can also analyze the grammatical structure of the user's request using grammatical analysis and understand its intention. The analysis unit can also analyze the content of the user's feedback using semantic analysis and understand its intention. As a result, the use of natural language processing technology can accurately understand the user's intention.

[0063] The selection unit can select an appropriate generation AI service based on the information analyzed by the analysis unit. Appropriate generation AI services include, but are not limited to, text generation, image generation, and voice generation, for example. The selection unit selects, for example, a text generation service. The selection unit can also select an image generation service. The selection unit can also select a voice generation service. For example, the selection unit selects a text generation service based on the information analyzed by the analysis unit. The selection unit can also select an image generation service based on the information analyzed by the analysis unit. The selection unit can also select a voice generation service based on the information analyzed by the analysis unit. In this way, by selecting the optimal generation AI service based on the analyzed information, it is possible to provide an optimal answer to a user's request.

[0064] The providing unit can provide the user with an answer generated by the generation AI service selected by the selecting unit. Examples of formats for providing include, but are not limited to, text, audio, and visual formats. The providing unit can provide the answer, for example, in text format. The providing unit can also provide the answer in audio format. The providing unit can also provide the answer in visual format. For example, the providing unit can provide the user with a text format answer generated by the generation AI service selected by the selecting unit. The providing unit can also provide the user with an audio format answer generated by the generation AI service selected by the selecting unit. The providing unit can also provide the user with a visual format answer generated by the generation AI service selected by the selecting unit. In this way, by providing the user with an answer generated by the selected generation AI service, the user can obtain the answer they expected.

[0065] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the emotions. For example, if the user is feeling stressed, the analysis unit can increase the accuracy of the analysis to provide a quicker answer. Furthermore, if the user is relaxed, the analysis unit can perform a more detailed analysis and provide more information. Furthermore, if the user is in a hurry, the analysis unit can adjust the accuracy of the analysis to provide a quicker result. For example, the analysis unit can analyze the user's facial expression to determine whether the user is feeling stressed. Furthermore, the analysis unit can analyze the user's voice to determine whether the user is relaxed. Furthermore, the analysis unit can analyze the user's text to determine whether the user is in a hurry. By adjusting the accuracy of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a 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 analysis unit can analyze the user's past question history and select the optimal analysis method. For example, the analysis unit analyzes patterns of questions frequently asked by the user in the past and selects the optimal analysis method. Furthermore, if the user's past question history shows that there are many questions about a specific field, the analysis unit can select an analysis method specialized for that field. Furthermore, the analysis unit can select the most effective analysis method based on the user's past question history. For example, the analysis unit retrieves the user's past question history from a database and analyzes patterns of frequently asked questions. Furthermore, the analysis unit can analyze the user's past question history using log analysis technology and, if there are many questions about a specific field, select an analysis method specialized for that field. Furthermore, the analysis unit can use a machine learning algorithm to select the most effective analysis method based on the user's past question history. In this way, the optimal analysis method can be selected by analyzing the past question history.

[0067] When analyzing questions or requests, the analysis unit can filter based on the user's current project or area of ​​interest. For example, the analysis unit prioritizes analyzing questions related to the user's current project. The analysis unit can also prioritize analyzing related information based on the user's area of ​​interest. The analysis unit can also select the optimal analysis method depending on the progress of the user's project. For example, the analysis unit can use data from a project management tool to identify the user's current project and prioritize analyzing questions related to that project. The analysis unit can also identify an area of ​​interest based on information input by the user and prioritize analyzing information related to that area. The analysis unit can also use data from the project management tool to select the optimal analysis method depending on the progress of the user's project. This makes it possible to provide highly relevant information by filtering based on the user's current project or area of ​​interest.

[0068] When analyzing a question or request, the analysis unit can select an appropriate analysis means depending on the user's input method. For example, when the user inputs a question by voice, the analysis unit performs analysis using voice recognition technology. Furthermore, when the user inputs a question in text, the analysis unit can also perform analysis using natural language processing technology. Furthermore, when the user inputs an image, the analysis unit can also perform analysis using image recognition technology. For example, the analysis unit converts the user's voice input into text using voice recognition technology and performs analysis based on that text. Furthermore, the analysis unit can analyze the user's text input using natural language processing technology and understand its intent. Furthermore, the analysis unit can analyze the user's image input using image recognition technology and understand its content. This improves the accuracy of the analysis by selecting the optimal analysis means depending on the user's input method.

[0069] The analysis unit can estimate the user's emotions and prioritize questions and requests to be analyzed based on the estimated user emotions. For example, if the user is stressed, the analysis unit can prioritize urgent questions. Furthermore, if the user is relaxed, the analysis unit can prioritize questions that require detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can prioritize questions that require a quick answer. For example, the analysis unit can analyze the user's facial expression to determine whether the user is stressed. Furthermore, the analysis unit can analyze the user's voice to determine whether the user is relaxed. Furthermore, the analysis unit can analyze the user's text to determine whether the user is in a hurry. Thus, by prioritizing questions and requests based on the user's emotions, urgent questions can be prioritized. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0070] When analyzing a question or request, the analysis unit can prioritize analyzing highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the analysis unit prioritizes analyzing information related to that area. The analysis unit can also select the optimal analysis method based on the user's current location. The analysis unit can also prioritize analyzing highly relevant information based on the user's geographical location information. For example, the analysis unit can identify the user's current location using GPS data and prioritize analyzing information related to that area. The analysis unit can also identify the user's location using an IP address and prioritize analyzing information related to that area. The analysis unit can also prioritize analyzing geographically close information based on the user's geographical location information in order to prioritize analyzing highly relevant information. This allows highly relevant information to be provided preferentially by taking the user's geographical location information into account.

[0071] When analyzing a question or request, the analysis unit can analyze the user's social media activity and analyze related information. For example, the analysis unit analyzes the content posted by the user on social media and analyzes the related information. The analysis unit can also analyze the related information by referring to the activities of the user's friends on social media. The analysis unit can also analyze the related information based on the user's check-in information on social media. For example, the analysis unit analyzes the content posted by the user on social media and extracts related information. The analysis unit can also analyze the activities of the user's friends on social media and extract related information. The analysis unit can also analyze the user's check-in information on social media and extract related information. In this way, highly relevant information can be provided by analyzing the user's social media activity.

[0072] When analyzing a question or request, the analysis unit can customize the analysis method by reflecting the user's past feedback. The analysis unit customizes the analysis method, for example, based on feedback provided by the user in the past. The analysis unit can also select the optimal analysis method from the user's past feedback. The analysis unit can also improve the accuracy of the analysis by reflecting the user's feedback. For example, the analysis unit obtains the user's past feedback from a database and customizes the analysis method. The analysis unit can also select the optimal analysis method based on the user's past feedback. The analysis unit can also use a machine learning algorithm to reflect the user's feedback and improve the accuracy of the analysis. In this way, the analysis accuracy is improved by reflecting the user's past feedback.

[0073] The selection unit can estimate the user's emotions and adjust the selection criteria based on the estimated user emotions. For example, if the user is stressed, the selection unit selects a generation AI service that can provide a quick answer. Furthermore, if the user is relaxed, the selection unit can select a generation AI service that can perform detailed analysis. Furthermore, if the user is in a hurry, the selection unit can select a generation AI service that can quickly provide results. For example, the selection unit can analyze the user's facial expression to determine whether the user is stressed. Furthermore, the selection unit can analyze the user's voice to determine whether the user is relaxed. Furthermore, the selection unit can analyze the user's text to determine whether the user is in a hurry. By adjusting the selection criteria based on the user's emotions, a more appropriate generation AI service can be selected. Emotion estimation is achieved 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.

[0074] When making a selection, the selection unit can improve the accuracy of the selection by taking into account the interrelationships between the generation AI services. For example, the selection unit analyzes the interrelationships between the generation AI services and selects the optimal combination. The selection unit can also select the most effective service by taking into account the interrelationships between the generation AI services. The selection unit can also improve the accuracy of the selection based on the interrelationships between the generation AI services. For example, the selection unit analyzes the dependencies between the generation AI services and selects the optimal combination. The selection unit can also select the most effective service by taking into account the interactions between the generation AI services. The selection unit can also adjust the algorithm to improve the accuracy of the selection based on the interrelationships between the generation AI services. In this way, the accuracy of the selection is improved by taking into account the interrelationships between the generation AI services.

[0075] When selecting a service, the selection unit can make the selection taking into consideration attribute information of the provider of the generation AI service. For example, the selection unit makes the selection taking into consideration the specialty of the provider of the generation AI service. The selection unit can also make the selection based on the past performance of the provider of the generation AI service. The selection unit can also select the optimal service taking into consideration attribute information of the provider of the generation AI service. For example, the selection unit selects the optimal service taking into consideration the specialty of the provider of the generation AI service. The selection unit can also select the optimal service based on the past performance of the provider of the generation AI service. The selection unit can also use a database to select the optimal service taking into consideration attribute information of the provider of the generation AI service. In this way, the optimal service can be selected by taking into consideration attribute information of the provider of the generation AI service.

[0076] When selecting a service, the selection unit can weight the selection based on the frequency of provision of the generation AI service. For example, if the frequency of provision of a generation AI service is high, the selection unit will preferentially select that service. The selection unit can also weight the selection based on the frequency of provision of the generation AI service. The selection unit can also select the optimal service, taking into account the frequency of provision of the generation AI service. For example, if the frequency of provision of a generation AI service is high, the selection unit will preferentially select that service. The selection unit can also weight the selection based on the frequency of provision of the generation AI service. The selection unit can also use a database to select the optimal service, taking into account the frequency of provision of the generation AI service. In this way, the optimal service can be selected by weighting based on the frequency of provision of the generation AI service.

[0077] The selection unit can estimate the user's emotions and adjust the order in which the selection results are displayed based on the estimated user emotions. For example, if the user is stressed, the selection unit can first display a service that can provide an answer most quickly. Furthermore, if the user is relaxed, the selection unit can first display a service that can provide a detailed analysis. Furthermore, if the user is in a hurry, the selection unit can first display a service that can provide a quick result. For example, the selection unit can analyze the user's facial expression to determine whether the user is stressed. Furthermore, the selection unit can analyze the user's voice to determine whether the user is relaxed. Furthermore, the selection unit can analyze the user's text to determine whether the user is in a hurry. By adjusting the display order of the selection results based on the user's emotions, the results can be displayed in an order that is optimal for the user. 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 selecting a service, the selection unit can take into consideration the geographical distribution of the generation AI service. For example, the selection unit selects the optimal service by taking into consideration the area where the generation AI service is provided. The selection unit can also improve the accuracy of the selection based on the geographical distribution of the generation AI service. The selection unit can also select the optimal service based on the area where the generation AI service is provided. For example, the selection unit selects the optimal service by taking into consideration the area where the generation AI service is provided. The selection unit can also improve the accuracy of the selection based on the geographical distribution of the generation AI service. The selection unit can also use a database to select the optimal service based on the area where the generation AI service is provided. This allows the optimal service to be selected by taking into consideration the geographical distribution of the generation AI service.

[0079] When making a selection, the selection unit can improve the accuracy of the selection by referring to literature related to the generation AI service. For example, the selection unit refers to literature related to the generation AI service to select the optimal service. The selection unit can also improve the accuracy of the selection based on literature related to the generation AI service. The selection unit can also improve the accuracy of the selection by referring to literature related to the generation AI service. For example, the selection unit refers to literature related to the generation AI service to select the optimal service. The selection unit can also improve the accuracy of the selection based on literature related to the generation AI service. The selection unit can also use a database to select the optimal service by referring to literature related to the generation AI service. In this way, the accuracy of the selection is improved by referring to related literature.

[0080] When making a selection, the selection unit can take into consideration the market value of the generation AI service. For example, the selection unit selects the optimal service based on the market value of the generation AI service. The selection unit can also improve the accuracy of the selection by taking into consideration the market value of the generation AI service. The selection unit can also select the optimal service based on the market value of the generation AI service. For example, the selection unit selects the optimal service based on the market value of the generation AI service. The selection unit can also improve the accuracy of the selection by taking into consideration the market value of the generation AI service. The selection unit can also use a database to select the optimal service based on the market value of the generation AI service. This makes it possible to select the optimal service by taking market value into consideration.

[0081] The providing unit can estimate the user's emotions and adjust the way the answer is presented based on the estimated user's emotions. For example, if the user is stressed, the providing unit can provide a concise and easy-to-understand expression. If the user is relaxed, the providing unit can also provide an expression that includes detailed information. If the user is in a hurry, the providing unit can also provide an expression that is quickly understandable. For example, the providing unit can analyze the user's facial expression to determine whether the user is stressed. The providing unit can also analyze the user's voice to determine whether the user is relaxed. The providing unit can also analyze the user's text to determine whether the user is in a hurry. This allows the answer to be presented in a more appropriate way by adjusting the way the answer is presented based on the user's emotions. Emotion estimation is achieved 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.

[0082] The providing unit can adjust the level of detail of the information to be provided based on the importance of the generated answer when providing the answer. For example, the providing unit provides an answer with high importance in a form including detailed information. The providing unit can also provide an answer with low importance in a concise form. The providing unit can also adjust the level of detail of the information to be provided according to the importance. For example, the providing unit provides an answer with high importance in a form including detailed information. The providing unit can also provide an answer with low importance in a concise form. The providing unit can also use an algorithm to adjust the level of detail of the information to be provided according to the importance. In this way, by adjusting the level of detail of the information to be provided based on the importance of the answer, it is possible to provide optimal information for the user.

[0083] The providing unit can apply different providing algorithms depending on the category of the generated answer when providing the answer. For example, the providing unit can apply a providing algorithm including detailed data and analysis to business-related answers. The providing unit can also apply a concise and easy-to-understand providing algorithm to private-related answers. The providing unit can also select an optimal providing algorithm depending on the category. For example, the providing unit can apply a providing algorithm including detailed data and analysis to business-related answers. The providing unit can also apply a concise and easy-to-understand providing algorithm to private-related answers. The providing unit can also use a database to select an optimal providing algorithm depending on the category. In this way, the optimal providing algorithm can be applied depending on the category of the answer, thereby providing the optimal answer for the user.

[0084] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the answer. For example, the providing unit selects the optimal provision method based on answers provided by the user in the past. The providing unit can also analyze the user's past provision results and improve the accuracy of the provision. The providing unit can also improve the accuracy of the provision by reflecting user feedback. For example, the providing unit selects the optimal provision method based on answers provided by the user in the past. The providing unit can also analyze the user's past provision results and improve the accuracy of the provision. The providing unit can also use a machine learning algorithm to improve the accuracy of the provision by reflecting user feedback. In this way, the accuracy of the provision is improved by referring to the user's past provision results.

[0085] The providing unit can estimate the user's emotions and adjust the length of the answer to be provided based on the estimated user emotions. For example, if the user is stressed, the providing unit can provide a concise and short answer. If the user is relaxed, the providing unit can provide a long answer with detailed information. If the user is in a hurry, the providing unit can provide a short answer that is quickly understandable. For example, the providing unit can analyze the user's facial expression to determine whether the user is stressed. The providing unit can also analyze the user's voice to determine whether the user is relaxed. The providing unit can also analyze the user's text to determine whether the user is in a hurry. This allows the length of the answer to be adjusted based on the user's emotions, thereby providing a more appropriate answer. Emotion estimation is achieved 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.

[0086] The providing unit can determine the priority of provision based on the submission time of the generated answers when providing the answers. For example, the providing unit provides the most recently generated answer with priority. The providing unit can also regenerate and provide answers that were submitted earlier as needed. The providing unit can also determine an optimal provision order based on the submission time. For example, the providing unit provides the most recently generated answer with priority. The providing unit can also regenerate and provide answers that were submitted earlier as needed. The providing unit can also use a database to determine the optimal provision order based on the submission time. In this way, by determining the priority of provision based on the submission time of the answers, the latest information can be provided with priority.

[0087] The providing unit can adjust the order of providing the generated answers based on the relevance of the answers when providing them. For example, the providing unit can provide the most relevant answer first. The providing unit can also postpone less relevant answers. The providing unit can also adjust the order of providing the answers based on the relevance. For example, the providing unit can provide the most relevant answer first. The providing unit can also postpone less relevant answers. The providing unit can also use an algorithm to adjust the order of providing the answers based on the relevance. In this way, by adjusting the order of providing the answers based on the relevance of the answers, it is possible to preferentially provide information that is most relevant to the user.

[0088] The providing unit can adjust the use of technical terminology when providing the answer according to the user's level of expertise. For example, if the user has technical expertise, the providing unit can provide an answer that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can also explain in simple terms. Furthermore, the providing unit can select an optimal expression method according to the user's level of expertise. For example, if the user has technical expertise, the providing unit can provide an answer that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can also explain in simple terms. Furthermore, the providing unit can use a database to select an optimal expression method according to the user's level of expertise. In this way, by adjusting the use of technical terminology according to the user's level of expertise, an answer that is easy for the user to understand can be provided. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, selection unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the selection unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, selection unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the selection unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, selection unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the selection unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, selection unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the selection unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The analysis unit can take into account the user's past behavioral history when analyzing a user's request. For example, it analyzes what questions the user has asked in the past and what answers they have received, and selects the optimal analysis method for the current request. The analysis unit can also understand the user's preferences and tendencies based on the user's past behavioral history, and perform more personalized analysis. Furthermore, the analysis unit can use the user's past behavioral history to predict future requests and prepare in advance. This allows for more accurate analysis by taking the user's past behavioral history into consideration.

[0091] The selection unit can take the user's current emotional state into consideration when selecting an appropriate generative AI service based on the information analyzed by the analysis unit. For example, if the user is feeling stressed, the selection unit can select a generative AI service that can provide a quick answer. Alternatively, if the user is relaxed, the selection unit can select a generative AI service that can perform a detailed analysis. Furthermore, if the user is in a hurry, the selection unit can select a generative AI service that can quickly produce results. In this way, by selecting the optimal generative AI service based on the user's emotional state, it is possible to provide answers that meet the user's needs.

[0092] When providing an answer generated by the generation AI service selected by the selection unit to a user, the provision unit can adjust the provision method according to the user's current situation. For example, if the user is on the move, the provision unit can provide the answer in audio format. Also, if the user is in a desktop environment, the provision unit can provide a detailed answer in text format. Furthermore, if the user is in a meeting, the provision unit can provide a concise answer in visual format. In this way, by selecting the optimal provision method according to the user's current situation, it is possible to provide an answer that is easy for the user to use.

[0093] When analyzing a user's request, the analysis unit can analyze the user's social media activity and analyze related information. For example, the analysis unit can analyze the content of the user's posts on social media and analyze related information. The analysis unit can also analyze related information by referring to the activities of the user's friends on social media. Furthermore, the analysis unit can analyze related information based on the user's check-in information on social media. In this way, highly relevant information can be provided by analyzing the user's social media activity.

[0094] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the emotions. For example, if the user is feeling stressed, the analysis accuracy can be increased to provide a quick answer. Alternatively, if the user is relaxed, a more detailed analysis can be performed to provide more information. Furthermore, if the user is in a hurry, the analysis accuracy can be adjusted to provide a quick result. In this way, by adjusting the analysis accuracy based on the user's emotions, more appropriate analysis results can be provided.

[0095] The selection unit can take into account the attribute information of the provider of the generation AI service when selecting an appropriate generation AI service based on the information analyzed by the analysis unit. For example, the selection can be made taking into account the specialty of the provider of the generation AI service. The selection can also be made based on the past performance of the provider of the generation AI service. Furthermore, the attribute information of the provider of the generation AI service can be taken into account to select the optimal service. In this way, the attribute information of the provider of the generation AI service can be taken into account to select the optimal service.

[0096] When providing a user with an answer generated by the generation AI service selected by the selection unit, the providing unit can estimate the user's emotions and adjust the way the answer is expressed based on the emotions. For example, if the user is feeling stressed, a concise and easy-to-understand expression can be provided. Also, if the user is relaxed, an expression that includes detailed information can be provided. Furthermore, if the user is in a hurry, an expression that can be quickly understood can be provided. In this way, by adjusting the way the answer is expressed based on the user's emotions, a more appropriate answer can be provided.

[0097] When analyzing a user's request, the analysis unit can prioritize analysis of highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, information related to that area is prioritized for analysis. The analysis unit can also select the optimal analysis method based on the user's current location. Furthermore, it can also prioritize analysis of highly relevant information based on the user's geographical location information. This makes it possible to provide highly relevant information with priority by taking into account the user's geographical location information.

[0098] The selection unit can take into account the market value of the generation AI service when selecting an appropriate generation AI service based on the information analyzed by the analysis unit. For example, the selection unit selects the optimal service based on the market value of the generation AI service. The selection unit can also improve the accuracy of the selection by taking into account the market value of the generation AI service. Furthermore, the selection unit can also select the optimal service based on the market value of the generation AI service. This allows the optimal service to be selected by taking market value into account.

[0099] When providing a user with an answer generated by the generation AI service selected by the selection unit, the providing unit can estimate the user's emotions and adjust the length of the answer to be provided based on the emotions. For example, if the user is feeling stressed, a concise and short answer can be provided. Alternatively, if the user is relaxed, a long answer including detailed information can be provided. Furthermore, if the user is in a hurry, a short answer that can be quickly understood can be provided. In this way, by adjusting the length of the answer based on the user's emotions, more appropriate answers can be provided.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The analysis unit analyzes the user's request. User requests include questions, requests, and feedback. The analysis unit uses natural language processing technology, data mining technology, and machine learning algorithms to understand the user's intent and analyze the request. Step 2: The selection unit selects an appropriate generative AI service based on the information analyzed by the analysis unit. The appropriate generative AI service may include text generation, image generation, speech generation, etc. Step 3: The providing unit provides the answer generated by the generation AI service selected by the selecting unit to the user in a text format, audio format, visual format, or the like.

[0102] 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.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] 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.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] [Explanation of symbols]

[0174] 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. an analysis unit that analyzes a user request; A selection unit that selects an appropriate generated AI service based on the information analyzed by the analysis unit; A providing unit that provides an answer generated by the generation AI service selected by the selection unit; Equipped with A system characterized by:

2. The analysis unit Understanding user intent using natural language processing technology The system of claim 1 .

3. The selection unit Selecting an appropriate generation AI service based on the information analyzed by the analysis unit The system of claim 1 .

4. The providing unit The answer generated by the generation AI service selected by the selection unit is provided to the user. The system of claim 1 .

5. The analysis unit Estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. The system of claim 1 .

6. The analysis unit Analyze the user's past question history and select the appropriate analysis method The system of claim 1 .

7. The analysis unit When parsing questions or requests, filtering based on the user's current projects or areas of interest The system of claim 1 .

8. The analysis unit When analyzing questions or requests, select the appropriate analysis method depending on the user's input method. The system of claim 1 .

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