system

Abstract

The system according to this embodiment aims to seamlessly switch between dialogue histories between different generative AIs. [Solution] The system according to the embodiment comprises an export unit, an import unit, and a switching unit. The export unit exports the dialogue history. The import unit imports the dialogue history exported by the export unit. The switching unit seamlessly switches between different generative AIs based on the dialogue history imported by the import unit.

Description

Technical Field

[0004] ,

[0006] , , , , ,

[0005] , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is impossible to seamlessly switch the conversation history between different generative AIs, and it is difficult for a user to compare and utilize the answers of different AIs while maintaining the context of the conversation.

[0005] The system according to the embodiment aims to seamlessly switch the conversation history between different generative AIs.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an export unit, an import unit, and a switching unit. The export unit exports the dialogue history. The import unit imports the dialogue history exported by the export unit. The switching unit seamlessly switches between different generative AIs based on the dialogue history imported by the import unit. [Effects of the Invention]

[0007] The system according to this embodiment can seamlessly switch between dialogue histories between different generative AIs. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The seamless switching system between generative AIs according to an embodiment of the present invention is a system that can export and import dialogue history between different generative AIs and switch seamlessly between them. The seamless switching system between generative AIs allows users to compare and utilize responses from different generative AIs while maintaining the context of the dialogue, thus enabling them to obtain the optimal response. This system provides a dialogue history export function, a dialogue history import function, and a seamless switching function between different generative AIs. First, the user exports the dialogue history of the generative AI currently being used. For example, when a user asks a question to a generative AI, exporting the dialogue history prepares it for import into another generative AI. This export function allows the user to switch to a different generative AI while maintaining the context of the dialogue. Next, the exported dialogue history is imported into another generative AI. For example, when a user switches from generative AI A to generative AI B, importing the exported dialogue history into generative AI B allows generative AI B to understand the context of the dialogue and provide a response. This import function saves the user the trouble of re-entering the same question. Furthermore, it provides a seamless switching function between different generative AIs. For example, when a user switches from Generator AI A to Generator AI B, the dialogue history is automatically exported and imported, allowing the user to compare and use the responses from different Generator AIs while preserving the context of the dialogue. This seamless switching function enables the user to obtain the optimal response. This system allows the user to obtain a contextually appropriate response by continuing the dialogue history, even if the response from one Generator AI is not as expected, without wasting time and effort switching to another Generator AI. It also makes it easier to compare between Generator AIs, leading to the acquisition of the optimal response. This improves the user experience and increases the efficiency of Generator AI utilization. In short, the seamless switching system between Generator AIs allows users to compare and use the responses from different Generator AIs while preserving the context of the dialogue, thus enabling them to obtain the optimal response.

[0029] The seamless switching system between generating AIs according to this embodiment comprises an export unit, an import unit, and a switching unit. The export unit exports the dialogue history. For example, when a user asks a question to a generating AI, the export unit exports the dialogue history, preparing it for import into another generating AI. The export unit can also export the dialogue history in text or audio format. For example, the export unit can save the dialogue history as a text file. The export unit can also save the dialogue history as an audio file. Furthermore, when exporting the dialogue history, the export unit can select the export format based on the length and content of the dialogue. For example, the export unit can summarize and export a long dialogue history. The import unit imports the dialogue history exported by the export unit. For example, when a user switches from generating AI A to generating AI B, the import unit imports the exported dialogue history into generating AI B, allowing generating AI B to understand the context of the dialogue and provide an answer. The import unit can also import the dialogue history in text or audio format. For example, the import unit can read a dialogue history saved as a text file. The import unit can also read dialogue history saved as audio files. Furthermore, when importing dialogue history, the import unit can select the import format based on the length and content of the dialogue. For example, the import unit can summarize and import long dialogue histories. The switching unit seamlessly switches between different generative AIs based on the dialogue history imported by the import unit. For example, when a user switches from generative AI A to generative AI B, the switching unit automatically exports and imports the dialogue history, allowing the user to compare and use the responses of different generative AIs while preserving the context of the dialogue. The switching unit can also automate the process of exporting and importing dialogue history.For example, when a user switches from Generating AI A to Generating AI B, the switching unit automatically exports and imports the dialogue history, allowing the user to switch seamlessly without any effort. As a result, the seamless switching system between Generating AIs according to this embodiment can export and import dialogue history and switch seamlessly between different Generating AIs.

[0030] The export unit exports the dialogue history. For example, when a user asks a question to a generative AI, the export unit exports the dialogue history, preparing it for import into another generative AI. Specifically, the export unit can export the dialogue history in text or audio format. For example, the export unit can save the dialogue history as a text file. When saving as a text file, it can not only save the content of the dialogue as a string, but also include the dialogue's timestamp and speaker information. This makes it possible to accurately reproduce the flow and context of the dialogue when importing it later. The export unit can also save the dialogue history as an audio file. When saving as an audio file, it can not only save the audio data of the dialogue as is, but also save the data that has been converted into text using speech recognition technology. This makes it possible to reproduce the dialogue history more accurately by using both the audio data and the text data. Furthermore, when exporting the dialogue history, the export unit can select the export format based on the length and content of the dialogue. For example, the export unit can summarize and export long dialogue histories. When summarizing, natural language processing techniques are used to extract important information and keywords, and to concisely summarize the main points of the conversation. This allows for efficient use of the summarized format even if the exported conversation history is too long and difficult to handle. The export unit combines these functions to support users in seamlessly transferring conversation history between generating AIs.

[0031] The import unit imports the dialogue history exported by the export unit. For example, when a user switches from Generating AI A to Generating AI B, the import unit imports the exported dialogue history into Generating AI B, allowing Generating AI B to understand the context of the dialogue and provide answers accordingly. Specifically, the import unit can import dialogue history in text or audio format. For example, the import unit can read dialogue history saved as a text file. When reading a text file, it analyzes not only the content of the dialogue but also timestamps and speaker information to accurately reproduce the flow of the dialogue. The import unit can also read dialogue history saved as an audio file. When reading an audio file, it uses speech recognition technology to convert the audio data into text and analyzes that text data to understand the context of the dialogue. Furthermore, when importing dialogue history, the import unit can select the import format based on the length and content of the dialogue. For example, the import unit can summarize and import long dialogue histories. By importing summarized dialogue histories, Generating AI B can grasp important information and keywords and continue the dialogue efficiently. The import section combines these functions to support users in seamlessly transferring conversation history between generated AIs.

[0032] The switching unit seamlessly switches between different generative AIs based on the dialogue history imported by the import unit. For example, when a user switches from generative AI A to generative AI B, the switching unit automatically exports and imports the dialogue history, allowing the user to compare and utilize the responses of different generative AIs while preserving the context of the dialogue. Specifically, the switching unit can automate the process of exporting and importing dialogue history. For example, when a user switches from generative AI A to generative AI B, the switching unit automatically exports and imports the dialogue history, allowing the user to switch seamlessly without any effort. The switching unit integrates the functions of the export and import units, enabling a smooth transition of dialogue history when the user continues to interact with generative AIs. Furthermore, the switching unit can optimize the timing of dialogue history export and import. For example, by exporting the dialogue history just before the user ends the conversation with generative AI A and importing it just before starting the conversation with generative AI B, the interruption to the conversation can be minimized. Additionally, the switching unit can perform the dialogue history export and import process in the background. This allows the user to switch between generative AIs smoothly without experiencing any interruption to the conversation. The switching section combines these functions to support users in seamlessly continuing conversations between generated AIs.

[0033] The export unit has the functionality to export dialogue history. For example, when a user asks a question to a generative AI, the export unit exports the dialogue history, preparing it for import into another generative AI. The export unit can also export dialogue history in text or audio format. For example, the export unit can save the dialogue history as a text file. It can also save the dialogue history as an audio file. Furthermore, when exporting dialogue history, the export unit can select the export format based on the length and content of the dialogue. For example, the export unit can summarize and export long dialogue histories. In this way, the export unit can provide the functionality to export dialogue history.

[0034] The import unit has the functionality to import dialogue history. For example, when a user switches from Generating AI A to Generating AI B, the import unit can import the exported dialogue history into Generating AI B, allowing Generating AI B to understand the context of the dialogue and provide a response. The import unit can also import dialogue history in text or audio format. For example, the import unit can read dialogue history saved as a text file. It can also read dialogue history saved as an audio file. Furthermore, when importing dialogue history, the import unit can select the import format based on the length and content of the dialogue. For example, the import unit can summarize and import long dialogue histories. In this way, the import unit can provide the functionality to import dialogue history.

[0035] The switching unit has the functionality to automate the export and import process. For example, when a user switches from Generating AI A to Generating AI B, the switching unit automatically exports and imports the dialogue history, allowing the user to compare and use the responses of different Generating AIs while preserving the context of the dialogue. Furthermore, the switching unit can automate the process of exporting and importing the dialogue history. For example, when a user switches from Generating AI A to Generating AI B, the switching unit automatically exports and imports the dialogue history, allowing the user to switch seamlessly without any effort. In this way, the switching unit can automate the export and import process.

[0036] The comparison unit has functions to facilitate comparison between generating AIs. For example, when a user compares the answers of generating AI A and generating AI B, the comparison unit can display the answers of both side by side based on the dialogue history. The comparison unit can also visually show the differences in answers between generating AIs. For example, the comparison unit can highlight the differences in answers between generating AI A and generating AI B. Furthermore, the comparison unit can evaluate the answers between generating AIs. For example, the comparison unit can allow the user to evaluate the answers of generating AI A and generating AI B and select the optimal answer based on the evaluation results. In this way, the comparison unit makes it easy to compare between generating AIs.

[0037] The export unit adjusts the level of detail in the export based on the importance of the dialogue history. For example, it exports important dialogue history in detail and simplifies other histories. It can also export less important dialogue history in a summarized format. Furthermore, it can export highly important dialogue history including all details. In this way, the export unit can adjust the level of detail in the export based on the importance of the dialogue history.

[0038] The export function applies different export formats depending on the category of the dialogue history during export. For example, the export function exports business-related dialogue history in PDF format. It can also export private dialogue history in text format. Furthermore, it can export academic dialogue history in CSV format. This allows the export function to apply different export formats depending on the category of the dialogue history.

[0039] The export unit determines the export priority based on the submission date of the dialogue history during the export process. For example, the export unit prioritizes exporting the most recent dialogue history. It can also postpone exporting older dialogue history. Furthermore, it can prioritize exporting dialogue history that has been submitted recently. In this way, the export unit can determine the export priority based on the submission date of the dialogue history.

[0040] The export unit adjusts the export order based on the relevance of the dialogue history during the export process. For example, the export unit prioritizes exporting highly relevant dialogue history. It can also postpone exporting less relevant dialogue history. Furthermore, the export unit can group relevant dialogue history together for export. This allows the export unit to adjust the export order based on the relevance of the dialogue history.

[0041] The import unit adjusts the level of detail during import based on the importance of the dialogue history. For example, it imports important dialogue history in detail and simplifies other histories. It can also import less important dialogue history in a summarized format. Furthermore, it can import highly important dialogue history including all details. In this way, the import unit can adjust the level of detail of the import based on the importance of the dialogue history.

[0042] The import function applies different import formats depending on the category of the dialogue history during import. For example, the import function imports business-related dialogue history in PDF format. It can also import private dialogue history in text format. Furthermore, it can import academic dialogue history in CSV format. This allows the import function to apply different import formats depending on the category of the dialogue history.

[0043] The import unit determines the import priority based on the submission date of the dialogue history during the import process. For example, the import unit prioritizes importing the most recent dialogue history. It can also postpone importing older dialogue history. Furthermore, it can prioritize importing dialogue history that has been submitted recently. In this way, the import unit can determine the import priority based on the submission date of the dialogue history.

[0044] The import unit adjusts the import order based on the relevance of the dialogue history during import. For example, the import unit prioritizes importing highly relevant dialogue history. It can also postpone importing less relevant dialogue history. Furthermore, the import unit can group relevant dialogue history together for import. This allows the import unit to adjust the import order based on the relevance of the dialogue history.

[0045] The switching unit adjusts the level of detail in the switching process based on the importance of the dialogue history. For example, it switches important dialogue histories in detail and other histories in a simplified manner. It can also summarize and switch less important dialogue histories. Furthermore, it can switch highly important dialogue histories with all the details included. In this way, the switching unit can adjust the level of detail in the switching process based on the importance of the dialogue history.

[0046] The switching unit applies different switching algorithms depending on the category of the dialogue history during the switching process. For example, the switching unit can quickly switch business-related dialogue history. It can also switch private dialogue history carefully. Furthermore, it can switch academic dialogue history in detail. This allows the switching unit to apply different switching algorithms depending on the category of the dialogue history.

[0047] The switching unit determines the switching priority based on the submission date of the dialogue history during the switching process. For example, the switching unit prioritizes switching to the most recent dialogue history. It can also postpone switching to older dialogue history. Furthermore, it can prioritize switching to dialogue history that has been submitted recently. In this way, the switching unit can determine the switching priority based on the submission date of the dialogue history.

[0048] The switching unit adjusts the switching order based on the relevance of the dialogue history during the switching process. For example, the switching unit prioritizes switching highly relevant dialogue history. It can also postpone switching less relevant dialogue history. Furthermore, the switching unit can group relevant dialogue history together and switch accordingly. This allows the switching unit to adjust the switching order based on the relevance of the dialogue history.

[0049] The comparison unit adjusts the level of detail in the comparison based on the importance of the dialogue history. For example, it compares important dialogue histories in detail and simplifies others. It can also summarize and compare less important dialogue histories. Furthermore, it can compare highly important dialogue histories including all the details. In this way, the comparison unit can adjust the level of detail in the comparison based on the importance of the dialogue history.

[0050] The comparison unit applies different comparison algorithms depending on the category of the dialogue history during the comparison process. For example, the comparison unit can quickly compare business-related dialogue histories, carefully compare private dialogue histories, and compare academic dialogue histories in detail. This allows the comparison unit to apply different comparison algorithms depending on the category of the dialogue history.

[0051] The comparison unit determines the priority of the comparison based on the submission date of the dialogue history. For example, the comparison unit prioritizes comparing the most recent dialogue history. It can also postpone comparing older dialogue history entries. Furthermore, it can prioritize comparing dialogue history entries that are more recent. In this way, the comparison unit can determine the priority of the comparison based on the submission date of the dialogue history.

[0052] The comparison unit adjusts the order of comparisons based on the relevance of the dialogue histories. For example, the comparison unit prioritizes comparing highly relevant dialogue histories. It can also postpone comparing less relevant dialogue histories. Furthermore, the comparison unit can group relevant dialogue histories for comparison. This allows the comparison unit to adjust the order of comparisons based on the relevance of the dialogue histories.

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

[0054] The seamless switching system between generating AIs allows users to select the export format based on the dialogue category when exporting dialogue history. For example, business-related dialogue history can be exported in PDF format, private dialogue history in text format, and academic dialogue history in CSV format. This allows users to select the export format according to the category of the dialogue history.

[0055] The seamless switching system between generating AIs allows users to select the import format based on the category of the dialogue history when importing it. For example, business-related dialogue history can be imported in PDF format, private dialogue history in text format, and academic dialogue history in CSV format. This allows users to select the import format according to the category of the dialogue history.

[0056] The seamless switching system between generated AIs can adjust the level of detail in the exported dialogue history based on the importance of the dialogue. For example, important dialogue histories can be exported in detail, while others can be simplified. Less important dialogue histories can be exported in summary form. Furthermore, highly important dialogue histories can be exported with all details included. This allows the level of detail in the export to be adjusted based on the importance of the dialogue history.

[0057] The seamless switching system between generated AIs can adjust the level of detail during dialogue history import based on the importance of each dialogue. For example, important dialogue histories can be imported in detail, while others can be simplified. Less important dialogue histories can be imported in summary form. Furthermore, highly important dialogue histories can be imported with all details included. This allows the level of detail of the import to be adjusted based on the importance of each dialogue history.

[0058] The seamless switching system between generated AIs can adjust the export order of dialogue history based on the relevance of the dialogues during export. For example, highly relevant dialogue history can be prioritized for export, while less relevant dialogue history can be postponed. Furthermore, relevant dialogue history can be grouped and exported together, allowing for adjustment of the export order based on the relevance of the dialogue history.

[0059] The seamless switching system between generated AIs can adjust the import order of dialogue history based on the relevance of the dialogues during import. For example, highly relevant dialogue history can be imported preferentially, while less relevant dialogue history can be postponed. Furthermore, relevant dialogue history can be grouped and imported together, allowing for adjustment of the import order based on the relevance of the dialogue history.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The export unit exports the dialogue history. For example, when a user asks a question to the AI ​​generator, the dialogue history can be exported in text or audio format. The export unit can also select the export format based on the length and content of the dialogue, and it is possible to summarize and export long dialogue histories. Step 2: The import unit imports the dialogue history exported by the export unit. For example, when a user switches from Generator AI A to Generator AI B, importing the exported dialogue history into Generator AI B allows Generator AI B to understand the context of the dialogue and provide a response. The import unit can import the dialogue history in text or audio format, and can also select the import format based on the length and content of the dialogue. Step 3: The switching unit seamlessly switches between different generative AIs based on the dialogue history imported by the import unit. For example, when a user switches from generative AI A to generative AI B, the dialogue history is automatically exported and imported, allowing the user to compare and use the responses of different generative AIs while preserving the context of the dialogue. The switching unit can automate the process of exporting and importing dialogue history.

[0062] (Example of form 2) The seamless switching system between generative AIs according to an embodiment of the present invention is a system that can export and import dialogue history between different generative AIs and switch seamlessly between them. The seamless switching system between generative AIs allows users to compare and utilize responses from different generative AIs while maintaining the context of the dialogue, thus enabling them to obtain the optimal response. This system provides a dialogue history export function, a dialogue history import function, and a seamless switching function between different generative AIs. First, the user exports the dialogue history of the generative AI currently being used. For example, when a user asks a question to a generative AI, exporting the dialogue history prepares it for import into another generative AI. This export function allows the user to switch to a different generative AI while maintaining the context of the dialogue. Next, the exported dialogue history is imported into another generative AI. For example, when a user switches from generative AI A to generative AI B, importing the exported dialogue history into generative AI B allows generative AI B to understand the context of the dialogue and provide a response. This import function saves the user the trouble of re-entering the same question. Furthermore, it provides a seamless switching function between different generative AIs. For example, when a user switches from Generator AI A to Generator AI B, the dialogue history is automatically exported and imported, allowing the user to compare and use the responses from different Generator AIs while preserving the context of the dialogue. This seamless switching function enables the user to obtain the optimal response. This system allows the user to obtain a contextually appropriate response by continuing the dialogue history, even if the response from one Generator AI is not as expected, without wasting time and effort switching to another Generator AI. It also makes it easier to compare between Generator AIs, leading to the acquisition of the optimal response. This improves the user experience and increases the efficiency of Generator AI utilization. In short, the seamless switching system between Generator AIs allows users to compare and use the responses from different Generator AIs while preserving the context of the dialogue, thus enabling them to obtain the optimal response.

[0063] The seamless switching system between generating AIs according to this embodiment comprises an export unit, an import unit, and a switching unit. The export unit exports the dialogue history. For example, when a user asks a question to a generating AI, the export unit exports the dialogue history, preparing it for import into another generating AI. The export unit can also export the dialogue history in text or audio format. For example, the export unit can save the dialogue history as a text file. The export unit can also save the dialogue history as an audio file. Furthermore, when exporting the dialogue history, the export unit can select the export format based on the length and content of the dialogue. For example, the export unit can summarize and export a long dialogue history. The import unit imports the dialogue history exported by the export unit. For example, when a user switches from generating AI A to generating AI B, the import unit imports the exported dialogue history into generating AI B, allowing generating AI B to understand the context of the dialogue and provide an answer. The import unit can also import the dialogue history in text or audio format. For example, the import unit can read a dialogue history saved as a text file. The import unit can also read dialogue history saved as audio files. Furthermore, when importing dialogue history, the import unit can select the import format based on the length and content of the dialogue. For example, the import unit can summarize and import long dialogue histories. The switching unit seamlessly switches between different generative AIs based on the dialogue history imported by the import unit. For example, when a user switches from generative AI A to generative AI B, the switching unit automatically exports and imports the dialogue history, allowing the user to compare and use the responses of different generative AIs while preserving the context of the dialogue. The switching unit can also automate the process of exporting and importing dialogue history.For example, when a user switches from Generating AI A to Generating AI B, the switching unit automatically exports and imports the dialogue history, allowing the user to switch seamlessly without any effort. As a result, the seamless switching system between Generating AIs according to this embodiment can export and import dialogue history and switch seamlessly between different Generating AIs.

[0064] The export unit exports the dialogue history. For example, when a user asks a question to a generative AI, the export unit exports the dialogue history, preparing it for import into another generative AI. Specifically, the export unit can export the dialogue history in text or audio format. For example, the export unit can save the dialogue history as a text file. When saving as a text file, it can not only save the content of the dialogue as a string, but also include the dialogue's timestamp and speaker information. This makes it possible to accurately reproduce the flow and context of the dialogue when importing it later. The export unit can also save the dialogue history as an audio file. When saving as an audio file, it can not only save the audio data of the dialogue as is, but also save the data that has been converted into text using speech recognition technology. This makes it possible to reproduce the dialogue history more accurately by using both the audio data and the text data. Furthermore, when exporting the dialogue history, the export unit can select the export format based on the length and content of the dialogue. For example, the export unit can summarize and export long dialogue histories. When summarizing, natural language processing techniques are used to extract important information and keywords, and to concisely summarize the main points of the conversation. This allows for efficient use of the summarized format even if the exported conversation history is too long and difficult to handle. The export unit combines these functions to support users in seamlessly transferring conversation history between generating AIs.

[0065] The import unit imports the dialogue history exported by the export unit. For example, when a user switches from Generating AI A to Generating AI B, the import unit imports the exported dialogue history into Generating AI B, allowing Generating AI B to understand the context of the dialogue and provide answers accordingly. Specifically, the import unit can import dialogue history in text or audio format. For example, the import unit can read dialogue history saved as a text file. When reading a text file, it analyzes not only the content of the dialogue but also timestamps and speaker information to accurately reproduce the flow of the dialogue. The import unit can also read dialogue history saved as an audio file. When reading an audio file, it uses speech recognition technology to convert the audio data into text and analyzes that text data to understand the context of the dialogue. Furthermore, when importing dialogue history, the import unit can select the import format based on the length and content of the dialogue. For example, the import unit can summarize and import long dialogue histories. By importing summarized dialogue histories, Generating AI B can grasp important information and keywords and continue the dialogue efficiently. The import section combines these functions to support users in seamlessly transferring conversation history between generated AIs.

[0066] The switching unit seamlessly switches between different generative AIs based on the dialogue history imported by the import unit. For example, when a user switches from generative AI A to generative AI B, the switching unit automatically exports and imports the dialogue history, allowing the user to compare and utilize the responses of different generative AIs while preserving the context of the dialogue. Specifically, the switching unit can automate the process of exporting and importing dialogue history. For example, when a user switches from generative AI A to generative AI B, the switching unit automatically exports and imports the dialogue history, allowing the user to switch seamlessly without any effort. The switching unit integrates the functions of the export and import units, enabling a smooth transition of dialogue history when the user continues to interact with generative AIs. Furthermore, the switching unit can optimize the timing of dialogue history export and import. For example, by exporting the dialogue history just before the user ends the conversation with generative AI A and importing it just before starting the conversation with generative AI B, the interruption to the conversation can be minimized. Additionally, the switching unit can perform the dialogue history export and import process in the background. This allows the user to switch between generative AIs smoothly without experiencing any interruption to the conversation. The switching section combines these functions to support users in seamlessly continuing conversations between generated AIs.

[0067] The export unit has the functionality to export dialogue history. For example, when a user asks a question to a generative AI, the export unit exports the dialogue history, preparing it for import into another generative AI. The export unit can also export dialogue history in text or audio format. For example, the export unit can save the dialogue history as a text file. It can also save the dialogue history as an audio file. Furthermore, when exporting dialogue history, the export unit can select the export format based on the length and content of the dialogue. For example, the export unit can summarize and export long dialogue histories. In this way, the export unit can provide the functionality to export dialogue history.

[0068] The import unit has the functionality to import dialogue history. For example, when a user switches from Generating AI A to Generating AI B, the import unit can import the exported dialogue history into Generating AI B, allowing Generating AI B to understand the context of the dialogue and provide a response. The import unit can also import dialogue history in text or audio format. For example, the import unit can read dialogue history saved as a text file. It can also read dialogue history saved as an audio file. Furthermore, when importing dialogue history, the import unit can select the import format based on the length and content of the dialogue. For example, the import unit can summarize and import long dialogue histories. In this way, the import unit can provide the functionality to import dialogue history.

[0069] The switching unit has the functionality to automate the export and import process. For example, when a user switches from Generating AI A to Generating AI B, the switching unit automatically exports and imports the dialogue history, allowing the user to compare and use the responses of different Generating AIs while preserving the context of the dialogue. Furthermore, the switching unit can automate the process of exporting and importing the dialogue history. For example, when a user switches from Generating AI A to Generating AI B, the switching unit automatically exports and imports the dialogue history, allowing the user to switch seamlessly without any effort. In this way, the switching unit can automate the export and import process.

[0070] The comparison unit has functions to facilitate comparison between generating AIs. For example, when a user compares the answers of generating AI A and generating AI B, the comparison unit can display the answers of both side by side based on the dialogue history. The comparison unit can also visually show the differences in answers between generating AIs. For example, the comparison unit can highlight the differences in answers between generating AI A and generating AI B. Furthermore, the comparison unit can evaluate the answers between generating AIs. For example, the comparison unit can allow the user to evaluate the answers of generating AI A and generating AI B and select the optimal answer based on the evaluation results. In this way, the comparison unit makes it easy to compare between generating AIs.

[0071] The export unit estimates the user's emotions and determines the priority of the dialogue history to export based on the estimated emotions. For example, if the user is stressed, the export unit will prioritize exporting important dialogue history. If the user is relaxed, the export unit can export all dialogue history equally. Furthermore, if the user is in a hurry, the export unit can prioritize exporting the most recent dialogue history. In this way, the export unit can determine the priority of the dialogue history to export based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0072] The export unit adjusts the level of detail in the export based on the importance of the dialogue history. For example, it exports important dialogue history in detail and simplifies other histories. It can also export less important dialogue history in a summarized format. Furthermore, it can export highly important dialogue history including all details. In this way, the export unit can adjust the level of detail in the export based on the importance of the dialogue history.

[0073] The export function applies different export formats depending on the category of the dialogue history during export. For example, the export function exports business-related dialogue history in PDF format. It can also export private dialogue history in text format. Furthermore, it can export academic dialogue history in CSV format. This allows the export function to apply different export formats depending on the category of the dialogue history.

[0074] The export unit estimates the user's emotions and adjusts the timing of the export based on the estimated emotions. For example, if the user is stressed, the export unit will perform the export immediately. If the user is relaxed, the export unit can perform the export at a time specified by the user. Furthermore, if the user is in a hurry, the export unit can perform the export quickly. In this way, the export unit can adjust the timing of the export based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The export unit determines the export priority based on the submission date of the dialogue history during the export process. For example, the export unit prioritizes exporting the most recent dialogue history. It can also postpone exporting older dialogue history. Furthermore, it can prioritize exporting dialogue history that has been submitted recently. In this way, the export unit can determine the export priority based on the submission date of the dialogue history.

[0076] The export unit adjusts the export order based on the relevance of the dialogue history during the export process. For example, the export unit prioritizes exporting highly relevant dialogue history. It can also postpone exporting less relevant dialogue history. Furthermore, the export unit can group relevant dialogue history together for export. This allows the export unit to adjust the export order based on the relevance of the dialogue history.

[0077] The import unit estimates the user's emotions and determines the priority of the dialogue history to import based on the estimated emotions. For example, if the user is stressed, the import unit will prioritize importing important dialogue history. If the user is relaxed, the import unit can import all dialogue history equally. Furthermore, if the user is in a hurry, the import unit can prioritize importing the most recent dialogue history. In this way, the import unit can determine the priority of the dialogue history to import based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The import unit adjusts the level of detail during import based on the importance of the dialogue history. For example, it imports important dialogue history in detail and simplifies other histories. It can also import less important dialogue history in a summarized format. Furthermore, it can import highly important dialogue history including all details. In this way, the import unit can adjust the level of detail of the import based on the importance of the dialogue history.

[0079] The import function applies different import formats depending on the category of the dialogue history during import. For example, the import function imports business-related dialogue history in PDF format. It can also import private dialogue history in text format. Furthermore, it can import academic dialogue history in CSV format. This allows the import function to apply different import formats depending on the category of the dialogue history.

[0080] The import unit estimates the user's emotions and adjusts the timing of the import based on the estimated emotions. For example, if the user is stressed, the import unit will perform the import immediately. If the user is relaxed, the import unit can perform the import at a time specified by the user. Furthermore, if the user is in a hurry, the import unit can perform the import quickly. In this way, the import unit can adjust the timing of the import based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The import unit determines the import priority based on the submission date of the dialogue history during the import process. For example, the import unit prioritizes importing the most recent dialogue history. It can also postpone importing older dialogue history. Furthermore, it can prioritize importing dialogue history that has been submitted recently. In this way, the import unit can determine the import priority based on the submission date of the dialogue history.

[0082] The import unit adjusts the import order based on the relevance of the dialogue history during import. For example, the import unit prioritizes importing highly relevant dialogue history. It can also postpone importing less relevant dialogue history. Furthermore, the import unit can group relevant dialogue history together for import. This allows the import unit to adjust the import order based on the relevance of the dialogue history.

[0083] The switching unit estimates the user's emotions and adjusts the timing of the switch based on the estimated emotions. For example, if the user is feeling stressed, the switching unit will switch immediately. If the user is relaxed, the switching unit can switch at a time specified by the user. Furthermore, if the user is in a hurry, the switching unit can switch quickly. In this way, the switching unit can adjust the timing of the switch based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The switching unit adjusts the level of detail in the switching process based on the importance of the dialogue history. For example, it switches important dialogue histories in detail and other histories in a simplified manner. It can also summarize and switch less important dialogue histories. Furthermore, it can switch highly important dialogue histories with all the details included. In this way, the switching unit can adjust the level of detail in the switching process based on the importance of the dialogue history.

[0085] The switching unit applies different switching algorithms depending on the category of the dialogue history during the switching process. For example, the switching unit can quickly switch business-related dialogue history. It can also switch private dialogue history carefully. Furthermore, it can switch academic dialogue history in detail. This allows the switching unit to apply different switching algorithms depending on the category of the dialogue history.

[0086] The switching unit estimates the user's emotions and determines the switching priority based on the estimated emotions. For example, if the user is stressed, the switching unit will prioritize switching to important conversation history. If the user is relaxed, the switching unit can switch to all conversation history equally. Furthermore, if the user is in a hurry, the switching unit can prioritize switching to the most recent conversation history. In this way, the switching unit can determine the switching priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The switching unit determines the switching priority based on the submission date of the dialogue history during the switching process. For example, the switching unit prioritizes switching to the most recent dialogue history. It can also postpone switching to older dialogue history. Furthermore, it can prioritize switching to dialogue history that has been submitted recently. In this way, the switching unit can determine the switching priority based on the submission date of the dialogue history.

[0088] The switching unit adjusts the switching order based on the relevance of the dialogue history during the switching process. For example, the switching unit prioritizes switching highly relevant dialogue history. It can also postpone switching less relevant dialogue history. Furthermore, the switching unit can group relevant dialogue history together and switch accordingly. This allows the switching unit to adjust the switching order based on the relevance of the dialogue history.

[0089] The comparison unit estimates the user's emotions and adjusts the display method of the comparison based on the estimated emotions. For example, if the user is nervous, the comparison unit provides a simple and highly visible display method. If the user is relaxed, the comparison unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the comparison unit can provide a concise display method. In this way, the comparison unit can adjust the display method of the comparison based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The comparison unit adjusts the level of detail in the comparison based on the importance of the dialogue history. For example, it compares important dialogue histories in detail and simplifies others. It can also summarize and compare less important dialogue histories. Furthermore, it can compare highly important dialogue histories including all the details. In this way, the comparison unit can adjust the level of detail in the comparison based on the importance of the dialogue history.

[0091] The comparison unit applies different comparison algorithms depending on the category of the dialogue history during the comparison process. For example, the comparison unit can quickly compare business-related dialogue histories, carefully compare private dialogue histories, and compare academic dialogue histories in detail. This allows the comparison unit to apply different comparison algorithms depending on the category of the dialogue history.

[0092] The comparison unit estimates the user's emotions and determines the priority of the comparison based on the estimated emotions. For example, if the user is stressed, the comparison unit will prioritize comparing important conversation history. If the user is relaxed, the comparison unit can compare all conversation history equally. Furthermore, if the user is in a hurry, the comparison unit can prioritize comparing the most recent conversation history. In this way, the comparison unit can determine the priority of the comparison based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The comparison unit determines the priority of the comparison based on the submission date of the dialogue history. For example, the comparison unit prioritizes comparing the most recent dialogue history. It can also postpone comparing older dialogue history entries. Furthermore, it can prioritize comparing dialogue history entries that are more recent. In this way, the comparison unit can determine the priority of the comparison based on the submission date of the dialogue history.

[0094] The comparison unit adjusts the order of comparisons based on the relevance of the dialogue histories. For example, the comparison unit prioritizes comparing highly relevant dialogue histories. It can also postpone comparing less relevant dialogue histories. Furthermore, the comparison unit can group relevant dialogue histories for comparison. This allows the comparison unit to adjust the order of comparisons based on the relevance of the dialogue histories.

[0095] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0096] The seamless switching system between generative AIs can estimate the user's emotions and automatically select a generative AI based on those emotions. For example, if the user is stressed, it can select a generative AI that provides a more relaxing conversation. If the user is excited, it can select a generative AI that provides more detailed information. Furthermore, if the user is tired, it can select a generative AI that provides concise and to-the-point answers. This allows the system to automatically select the optimal generative AI according to the user's emotions.

[0097] The seamless switching system between generated AIs can filter the exported conversation history based on the user's emotions. For example, if the user is stressed, only important conversation history can be exported. If the user is relaxed, all conversation history can be exported. Furthermore, if the user is in a hurry, the most recent conversation history can be prioritized for export. This allows for data filtering tailored to the user's emotions.

[0098] The seamless switching system between generated AIs can prioritize the data to be imported based on the user's emotions when importing dialogue history. For example, if the user is stressed, important dialogue history can be imported preferentially. If the user is relaxed, all dialogue history can be imported equally. Furthermore, if the user is in a hurry, the most recent dialogue history can be imported preferentially. This allows for data prioritization tailored to the user's emotions.

[0099] The seamless switching system between generating AIs allows users to select the export format based on the dialogue category when exporting dialogue history. For example, business-related dialogue history can be exported in PDF format, private dialogue history in text format, and academic dialogue history in CSV format. This allows users to select the export format according to the category of the dialogue history.

[0100] The seamless switching system between generating AIs allows users to select the import format based on the category of the dialogue history when importing it. For example, business-related dialogue history can be imported in PDF format, private dialogue history in text format, and academic dialogue history in CSV format. This allows users to select the import format according to the category of the dialogue history.

[0101] The seamless switching system between generated AIs can adjust the level of detail in the exported dialogue history based on the importance of the dialogue. For example, important dialogue histories can be exported in detail, while others can be simplified. Less important dialogue histories can be exported in summary form. Furthermore, highly important dialogue histories can be exported with all details included. This allows the level of detail in the export to be adjusted based on the importance of the dialogue history.

[0102] The seamless switching system between generated AIs can adjust the level of detail during dialogue history import based on the importance of each dialogue. For example, important dialogue histories can be imported in detail, while others can be simplified. Less important dialogue histories can be imported in summary form. Furthermore, highly important dialogue histories can be imported with all details included. This allows the level of detail of the import to be adjusted based on the importance of each dialogue history.

[0103] The seamless switching system between generated AIs can adjust the export order of dialogue history based on the relevance of the dialogues during export. For example, highly relevant dialogue history can be prioritized for export, while less relevant dialogue history can be postponed. Furthermore, relevant dialogue history can be grouped and exported together, allowing for adjustment of the export order based on the relevance of the dialogue history.

[0104] The seamless switching system between generated AIs can adjust the import order of dialogue history based on the relevance of the dialogues during import. For example, highly relevant dialogue history can be imported preferentially, while less relevant dialogue history can be postponed. Furthermore, relevant dialogue history can be grouped and imported together, allowing for adjustment of the import order based on the relevance of the dialogue history.

[0105] The seamless switching system between generated AIs can estimate the user's emotions and adjust how the conversation history is displayed based on those emotions. For example, if the user is nervous, it can provide a simple and easy-to-read display. If the user is relaxed, it can provide a display that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display that gets straight to the point. This allows the system to provide the optimal display method according to the user's emotions.

[0106] The following briefly describes the processing flow for example form 2.

[0107] Step 1: The export unit exports the dialogue history. For example, when a user asks a question to the AI ​​generator, the dialogue history can be exported in text or audio format. The export unit can also select the export format based on the length and content of the dialogue, and it is possible to summarize and export long dialogue histories. Step 2: The import unit imports the dialogue history exported by the export unit. For example, when a user switches from Generator AI A to Generator AI B, importing the exported dialogue history into Generator AI B allows Generator AI B to understand the context of the dialogue and provide a response. The import unit can import the dialogue history in text or audio format, and can also select the import format based on the length and content of the dialogue. Step 3: The switching unit seamlessly switches between different generative AIs based on the dialogue history imported by the import unit. For example, when a user switches from generative AI A to generative AI B, the dialogue history is automatically exported and imported, allowing the user to compare and use the responses of different generative AIs while preserving the context of the dialogue. The switching unit can automate the process of exporting and importing dialogue history.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0110] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0111] Each of the multiple elements described above, including the export unit, import unit, switching unit, and comparison unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the export unit can export the dialogue history by the control unit 46A of the smart device 14. The import unit can import the exported dialogue history by the specific processing unit 290 of the data processing device 12. The switching unit can seamlessly switch between different generating AIs by the control unit 46A of the smart device 14. The comparison unit can compare responses between generating AIs by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0112] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0115] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0116] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0118] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0119] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0120] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0121] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0122] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0123] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0126] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the export unit, import unit, switching unit, and comparison unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the export unit can export the dialogue history by the control unit 46A of the smart glasses 214. The import unit can import the exported dialogue history by the specific processing unit 290 of the data processing unit 12. The switching unit can seamlessly switch between different generating AIs by the control unit 46A of the smart glasses 214. The comparison unit can compare responses between generating AIs by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0128] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0129] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0138] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0140] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0142] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the export unit, import unit, switching unit, and comparison unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the export unit can export the dialogue history by the control unit 46A of the headset terminal 314. The import unit can import the exported dialogue history by, for example, the specific processing unit 290 of the data processing unit 12. The switching unit can seamlessly switch between different generating AIs by, for example, the control unit 46A of the headset terminal 314. The comparison unit can compare responses between generating AIs by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0144] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0145] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0152] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the export unit, import unit, switching unit, and comparison unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the export unit can export the dialogue history by the control unit 46A of the robot 414. The import unit can import the exported dialogue history by, for example, the specific processing unit 290 of the data processing unit 12. The switching unit can seamlessly switch between different generating AIs by, for example, the control unit 46A of the robot 414. The comparison unit can compare responses between generating AIs by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0161] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0162] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0163] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0164] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0165] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0166] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0167] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0168] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0169] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0170] 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.

[0171] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0172] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0173] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0174] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0175] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0176] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0177] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0178] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0179] (Note 1) The export section exports the dialogue history, An import unit that imports the dialogue history exported by the export unit, The system includes a switching unit that seamlessly switches between different generative AIs based on the dialogue history imported by the import unit. A system characterized by the following features. (Note 2) The aforementioned export unit is Features a function to export conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned import unit is It has a function to import dialogue history. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned switching unit is It features the ability to automate the export and import process. The system described in Appendix 1, characterized by the features described herein. (Note 5) It includes a comparison unit to facilitate comparison between generated AIs. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned export unit is It estimates the user's emotions and determines the priority of the conversation history to export based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned export unit is During export, adjust the level of detail in the export based on the importance of the conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned export unit is When exporting, apply different export formats depending on the category of the dialogue history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned export unit is It estimates the user's sentiment and adjusts the timing of the export based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned export unit is During export, the export priority is determined based on when the dialogue history was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned export unit is During export, adjust the export order based on the relevance of the conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned import unit is It estimates the user's emotions and determines the priority of the dialogue history to import based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned import unit is During import, adjust the level of detail based on the importance of the conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned import unit is During import, different import formats are applied depending on the category of the dialogue history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned import unit is It estimates the user's sentiment and adjusts the timing of the import based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned import unit is During import, the import priority is determined based on when the dialogue history was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned import unit is During import, the import order is adjusted based on the relevance of the dialogue history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned switching unit is It estimates the user's emotions and adjusts the timing of the switch based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned switching unit is When switching, adjust the level of detail of the switch based on the importance of the conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned switching unit is When switching, apply a different switching algorithm depending on the category of the dialogue history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned switching unit is It estimates the user's emotions and determines the priority of switching based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned switching unit is When switching systems, the priority of the switch is determined based on when the dialogue history was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned switching unit is When switching, adjust the switching order based on the relevance of the conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The comparison unit is, It estimates the user's sentiment and adjusts how comparisons are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The comparison unit is, When making comparisons, adjust the level of detail based on the importance of the conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The comparison unit is, When comparing, different comparison algorithms are applied depending on the category of the dialogue history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The comparison unit is, It estimates the user's emotions and determines the priority of comparisons based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The comparison unit is, When making comparisons, the priority of the comparison is determined based on when the dialogue history was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 29) The comparison unit is, When comparing, adjust the order of comparisons based on the relevance of the dialogue history. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The export section exports the dialogue history, An import unit that imports the dialogue history exported by the export unit, The system includes a switching unit that seamlessly switches between different generating AIs based on the dialogue history imported by the import unit. A system characterized by the following features.

2. The aforementioned export unit is Features a function to export conversation history. The system according to feature 1.

3. The aforementioned import unit is It has a function to import dialogue history. The system according to feature 1.

4. The aforementioned switching unit is It features the ability to automate the export and import process. The system according to feature 1.

5. It includes a comparison unit to facilitate comparison between generated AIs. The system according to feature 1.

6. The aforementioned export unit is It estimates the user's emotions and determines the priority of the conversation history to export based on the estimated user emotions. The system according to feature 1.

7. The aforementioned export unit is During export, adjust the level of detail in the export based on the importance of the conversation history. The system according to feature 1.

8. The aforementioned export unit is When exporting, apply different export formats depending on the category of the dialogue history. The system according to feature 1.

9. The aforementioned export unit is It estimates the user's sentiment and adjusts the timing of the export based on the estimated user sentiment. The system according to feature 1.

10. The aforementioned export unit is During export, the export priority is determined based on when the dialogue history was submitted. The system according to feature 1.

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